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2019-data-breach-investigations-report.pdf
2019 Data Breach Investigations Report
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business ready
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A couple of tidbits Before we formally introduce you to the 2019 Data Breach Investigations Report (DBIR), let us get some clarifcations out of the way frst to reduce potential ambiguity around terms, labels, and fgures that you will fnd throughout this study.
VERIS resources
The terms “threat actions,” “threat actors,” “varieties,” and “vectors” will be referenced a lot. These are part of the Vocabulary for Event Recording and Incident Sharing (VERIS), a framework designed to allow for a consistent, unequivocal collection of security incident details. Here are some select defnitions followed by links with more information on the framework and on the enumerations.
Threat actor: Who is behind the event? This could be the external “bad guy” that launches a phishing campaign, or an employee who leaves sensitive documents in their seat back pocket.
Threat action: What tactics (actions) were used to afect an asset? VERIS uses seven primary categories of threat actions: Malware, Hacking, Social, Misuse, Physical, Error, and Environmental. Examples at a high level are hacking a server, installing malware, and infuencing human behavior.
Variety: More specifc enumerations of higher level categories - e.g., classifying the external “bad guy” as an organized criminal group, or recording a hacking action as SQL injection or brute force.
Learn more here: • github.com/vz-risk/dbir/tree/gh-pages/2019 – DBIR fgures and
fgure data. • veriscommunity.net features information on the framework with
examples and enumeration listings. • github.com/vz-risk/veris features the full VERIS schema. • github.com/vz-risk/vcdb provides access to our database on
publicly disclosed breaches, the VERIS Community Database. • http://veriscommunity.net/veris_webapp_min.html
allows you to record your own incidents and breaches. Don’t fret, it saves any data locally and you only share what you want.
Incident vs. breaches
We talk a lot about incidents and breaches and we use the following defnitions:
Incident: A security event that compromises the integrity, confdentiality or availability of an information asset.
Breach: An incident that results in the confrmed disclosure—not just potential exposure—of data to an unauthorized party.
Industry labels
We align with the North American Industry Classifcation System (NAICS) standard to categorize the victim organizations in our corpus. The standard uses 2 to 6 digit codes to classify businesses and organizations. Our analysis is typically done at the 2-digit level and we will specify NAICS codes along with an industry label. For example, a chart with a label of Financial (52) is not indicative of 52 as a value. 52 is the NAICS code for the Finance and Insurance sector. The overall label of “Financial” is used for brevity within the fgures. Detailed information on the codes and classifcation system is available here:
https://www.census.gov/cgi-bin/sssd/naics/naicsrch?chart=2017
New chart, who dis?
You may notice that the bar chart shown may not be as, well, bar- ish as what you may be used to. Last year we talked a bit in the Methodology section about confdence. When we say a number is X, it’s really X +/- a small amount.
Server (Just large organization breaches, n=335)
Server (All breaches, n=1,881)
0% 20% 40% 60% 80% 100%
Breaches
Figure 1. Top asset variety in breaches
This year we’re putting it in the bar charts. The black dot is the value, but the slope gives you an idea of where the real value could be between. In this sample fgure we’ve added a few red bars to highlight it, but in 19 bars out of 20 (95%),1 the real number will be between the two red lines on the bar chart. Notice that as the sample size (n) goes down, the bars get farther apart. If the lower bound of the range on the top bar overlaps with the higher bound of the bar beneath it, they are treated as statistically similar and thus statements that x is more than y will not be proclaimed.
Questions? Comments? Brilliant ideas? We want to hear them. Drop us a line at [email protected], fnd us on LinkedIn, tweet @VZEnterprise with the #dbir. Got a data question? Tweet @VZDBIR!
1https://en.wikipedia.org/wiki/Confidence_interval
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Table of contents
Introduction
Summary of fndings
Results and analysis
Unbroken chains
Incident classifcation patterns and subsets
Data breaches: extended version
Victim demographics and industry analysis
Accommodation and Food Services
Educational Services
Financial and Insurance
Healthcare
Information
Manufacturing
Professional, Technical and Scientifc Services
Public Administration
Retail 58
Wrap up 61
Year in review
Appendix A: Transnational hacker debriefs
Appendix B: Methodology
Appendix C: Watching the watchers
Appendix D: Contributing organizations
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Introduction “The wound is the place where the light enters you.” — Rumi
Welcome! Pull up a chair with the 2019 Verizon Data Breach Investigations Report (DBIR). The statements you will read in the pages that follow are data-driven, either by the incident corpus that is the foundation of this publication, or by non-incident data sets contributed by several security vendors.
This report is built upon analysis of 41,686 security incidents, of which 2,013 were confrmed data breaches. We will take a look at how results are changing (or not) over the years as well as digging into the overall threat landscape and the actors, actions, and assets that are present in breaches. Windows into the most common pairs of threat actions and afected assets also are provided. This afords the reader with yet another means to analyze breaches and to fnd commonalities above and beyond the incident classifcation patterns that you may already be acquainted with.
Fear not, however. The nine incident classifcation patterns are still around, and we continue to focus on how they correlate to industry. In addition to the nine primary patterns, we have created a subset of data to pull out fnancially-motivated social engineering (FMSE) attacks that do not have a goal of malware installation. Instead, they are more focused on credential theft and duping people into transferring money into adversary- controlled accounts. In addition to comparing industry threat profles to each other, individual industry sections are once again front and center.
Joining forces with the ever-growing incident/breach corpus, several areas of research using non-incident data sets such as malware blocks, results of phishing training, and vulnerability scanning are also utilized. Leveraging, and sometimes combining, disparate data sources (like honeypots and internet scan research) allows for additional data-driven context.
It is our charge to present information on the common tactics used by attackers against organizations in your industry. The purpose of this study is not to rub salt in the wounds of information security, but to contribute to the “light” that raises awareness and provides the ability to learn from the past. Use it as another arrow in your quiver to win hearts, minds, and security budget. We often hear that this is “required reading” and strive to deliver actionable information in a manner that does not cause drowsiness, fatigue, or any other adverse side efects.
We continue to be encouraged and energized by the coordinated data sharing by our 73 data sources, 66 of which are organizations external to Verizon. This community of data contributors represents an international group of public and private entities willing to support this annual publication. We again thank them for their support, time, and, of course, DATA.
We all have wounds, none of us knows everything, let’s learn from each other.
Excelsior!2
2If you didn’t expect a Stan Lee reference in this report, then you are certainly a first-time reader. Welcome to the party pal!
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Summary of fndings
16% were breaches of Public sector entities
15% were breaches involving Healthcare organizations
10% were breaches of the Financial industry
43% of breaches involved small business victims
0% 20% 40% 60% 80% 100%
Breaches Figure 2. Who are the victims?
52% of breaches featured Hacking
33% included Social attacks
28% involved Malware
Errors were causal events in 21% of breaches
15% were Misuse by authorized users
Physical actions were present in 4% of breaches
0% 20% 40% 60% 80% 100%
Breaches Figure 3. What tactics are utilized?
69% perpetrated by outsiders
34% involved Internal actors
2% involved Partners
5% featured Multiple parties
Organized criminal groups were behind 39% of breaches
Actors identifed as nation-state or state- a�liated were involved in 23% of breaches
0% 20% 40% 60% 80% 100%
Breaches Figure 4. Who's behind the breaches?
71% of breaches were fnancially motivated
25% of breaches were motivated by the gain of strategic advantage (espionage)
32% of breaches involved phishing
29% of breaches involved use of stolen credentials
56% of breaches took months or longer to discover
0% 20% 40% 60% 80% 100%
Breaches Figure 5. What are other commonalities?
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Results and analysis The results found in this and subsequent sections within the report are based on a data set collected from a variety of sources such as publicly-disclosed security incidents, cases provided by the Verizon Threat Research Advisory Center (VTRAC) investigators, and by our external collaborators. The year-to-year data set(s) will have new sources of incident and breach data as we strive to locate and engage with organizations that are willing to share information to improve the diversity and coverage of real-world events. This is a convenience sample, and changes in contributors, both additions and those who were not able to participate this year, will infuence the data set. Moreover, potential changes in their areas of focus can stir the pot o’ breaches when we trend over time. All of this means we are not always researching and analyzing the same fsh in the same barrel. Still other potential factors that may afect these results are changes in how we subset data and large-scale events that can sometimes infuence metrics for a given year. These are all taken into consideration, and acknowledged where necessary, within the text to provide appropriate context to the reader.
With those cards on the table, a year-to-year view of the actors (and their motives),3 followed by changes in threat actions and afected assets over time is once again provided. A deeper dive into the overall results for this year’s data set with an old-school focus on threat action categories follows. Within the threat action results, relevant non-incident data is included to add more awareness regarding the tactics that are in the adversaries’ arsenal.
Defning the threats
Threat actor is the terminology used to describe who was pulling the strings of the breach (or if an error, tripping on them). Actors are broken out into three high-level categories of External, Internal, and Partner. External actors have long been the primary culprits behind confrmed data breaches and this
year the trend continues. There are some subsets of data that are removed from the general corpus, notably over 50,000 botnet related breaches. These would have been attributed to external groups and, had they been included, would have further increased the gap between the External and Internal threat.
80% External
60%
40% Internal
20%
Partner
0% 2011 2013 2015 2017
Figure 6. Threat actors in breaches over time
B re
ac he
s B
re ac
he s
Financial 75%
50%
Espionage
25%
Other 0%
2011 2013 2015 2017
Figure 7. Threat actor motives in breaches over time
3And we show the whole deck in Appendix B: Methodology.
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80%
60%
40%
20%
State-aˆliated
Activist
System Admin
Cashier
Organized crime
2011 2013 2015 2017
B re
ac he
s
0%
Figure 8. Select threat actors in breaches over time
Financial gain is still the most common motive behind data breaches where a motive is known or applicable (errors are not categorized with any motive). This continued positioning of personal or fnancial gain at the top is not unexpected. In addition to the botnet breaches that were fltered out, there are other scalable breach types that allow for opportunistic criminals to attack and compromise numerous victims.4 Breaches with a strategic advantage as the end goal are well-represented, with one-quarter of the breaches associated with espionage. The ebb and fow of the fnancial and espionage motives are indicative of changes in the data contributions and the multi-victim sprees.
This year there was a continued reduction in card-present breaches involving point of sale
environments and card skimming operations. Similar percentage changes in organized criminal groups and state-afliated operations are shown in Figure 8 above. Another notable fnding (since we are already walking down memory lane) is the bump in Activists, who were somewhat of a one-hit wonder in the 2012 DBIR with regard to confrmed data breaches. We also don’t see much of Cashier (which also encompasses food servers and bank tellers) anymore. System administrators are creeping up and while the rogue admin planting logic bombs and other mayhem makes for a good story, the presence of insiders is most often in the form of errors. These are either by misconfguring servers to allow for unwanted access or publishing data to a server that should not have been accessible by all site viewers. Please, close those buckets!
4In Appendix C: “Watching the Watchers”, we refer to these as zero-marginal cost attacks.
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0
2018 2013 DIFF 2018 2013 DIFF
-3Hacking 53% 56%
-1Malware 29 30
Social +18 17 35
+5Error 17 21
-2Misuse 14 16
-6Physical 4 10
Environmental 0 0
Breaches Figure 9. Threat actions in data breaches over time n=2,501 (2013), n=1,638 (2018)
Figures 9 and 10 show changes in threat actions and afected assets from 2013 to 2018.5,6 No, we don’t have some odd afnity for seven-year time frames (as far as you know). Prior years were heavily infuenced by payment card breaches featuring automated attacks on POS devices with default credentials, so 2013 was a better representative starting point. The rise in social engineering is evident in both charts, with the action category Social and the related human asset both increasing.
Threat action varieties
When we delve a bit deeper and examine threat actions at the variety level, the proverbial question of “What are the bad guys doing?” starts to become clearer. Figure 11 shows Denial of Service attacks are again at the top
-2Server 63% 65%
User Dev 28 30
+2
Person 19 39
+20
Media 9 17
-8
Kiosk/Term 1 7
-5
Network 0 1
Breaches
+1
Figure 10. Asset categories in data breaches over time n=2,294 (2013), n=1,513 (2018)
of action varieties associated with security incidents, but it is still very rare for DoS to feature in a confrmed data breach. Similarly, Loss, which is short for Lost or misplaced assets, incidents are not labeled as a data breach if the asset lost is a laptop or phone, as there is no feasible way to determine if data was accessed. We allow ourselves to infer data disclosure if the asset involved was printed documents.
Switching over to breaches in Figure 12, phishing and the hacking action variety of use of stolen credentials are prominent fxtures. The next group of three involves the installation and subsequent use of back- door or Command and Control (C2) malware. These tactics have historically been common facets of data breaches and based on our data, there is still much success to be had there.
5Credit where it’s due. These dumbbell charts are based on the design at http://www.pewglobal.org/2016/02/22/social-networking-very-popular-among-adult-internet-users-in-emerging-and-developing-nations/ and code at https://rud.is/b/2016/04/17/ggplot2-exercising-with-ggalt-dumbbells/
6Note these are incident years, not DBIR years. All of the 2018 will be represented in this year’s data, but a 2012 breach not discovered until 2013 would be part of the 2014 DBIR.
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DoS Phishing
Loss Use of stolen creds
C2
Misdelivery
Phishing
Use of stolen creds
Ransomware
Privilege abuse
Backdoor
Use of backdoor or C2
Spyware/Keylogger
Pretexting
Data mishandling
Adminware
Backdoor
C2
Use of backdoor or C2
Privilege abuse
Spyware/Keylogger
Misdelivery
Capture app data
Data mishandling
Adminware
Publishing error
Pretexting
Exploit vuln
Adware Misconfguration
0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100%
Incidents Breaches Figure 11. Top threat action varieties in incidents, (n=17,310) Figure 12. Top threat action varieties in breaches (n=1,774)
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Hacking
A quick glance at the fgures below uncovers two prominent hacking variety and vector combinations. The more obvious scenario is using a backdoor or C2 via the backdoor or C2 channel, and the less obvious, but more interesting, use of stolen credentials. Utilizing valid credentials to pop web applications is not exactly avant garde.
The reason it becomes noteworthy is that 60% of the time, the compromised web application vector was the front-end to cloud based email servers.
Use of stolen creds
Use of backdoor or C2
Exploit vuln
Brute force
Bu�er overfow
Abuse of functionality
RFI
SQLi
Other
0% 20% 40% 60% 80% 100%
Breaches Figure 13. Top hacking action varieties in breaches (n=755)
Web application
Backdoor or C2
Desktop sharing
Desktop sharing software
Other
VPN
Partner
Command shell
3rd party desktop
Physical access
0% 20% 40% 60% 80% 100%
Breaches Figure 14. Top hacking action vectors in breaches (n=862)
Even though stolen credentials are not directly associated with patch currency, it is still a necessary and noble undertaking. At most, six percent of breaches in our data set this year involved exploiting vulnerabilities. Remember that time your network was scanned for vulnerabilities and there were zero fndings? You slept soundly that night only to be jolted from your drowsy utopia by your alarm radio blaring “I Got You Babe.” Vulnerability scanning always yields fndings (even benign informational ones) and it is up to the administrators to determine which are accepted, and which are addressed.
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Figure 15 shows the patching behavior of hundreds of organizations from multiple vulnerability scanning contributors. Based on scan history, we determine that organizations will typically have a big push to remediate fndings after they are initially discovered and after that there is a steady increase in percentage of fndings fxed until it levels out. Not unlike the amount of romance and mutual regard that occurs while dating vs. once married. You get the idea.
The area under the curve (AUC) is how protected you are while you are actively patching. Quick remediation will result in a higher AUC. The percentage completed-on-time (COT) is the amount of vulnerabilities patched at a pre-determined cut-of time; we used 90 days. Your COT metric could be diferent, and it would make sense to have diferent COTs for Internet-facing devices or browser vulnerabilities, and certainly for vulnerabilities with active exploitation in the wild.
It is important to acknowledge that there will always be fndings. The key is to prioritize the important ones and have a plan for the remaining actionable vulnerabilities; and to be able to defend acceptance of unaddressed fndings.
100%
75% AUC: 32.4% COT: 43.8%
50%
25%
0% 7 30 60 90
Days taken to patch Figure 15. Time to patch
F in
di ng
s fx
ed
Malware
Malware can be leveraged in numerous ways to establish or advance attacks. Command and Control (C2) and backdoors are found in both security incidents and breaches. Ransomware is still a major issue for organizations and is not forced to rely on data theft in order to be lucrative.
C2
Ransomware
Backdoor
Spyware/keylogger
Adminware
Adware
Capture app data
Spam
Downloader
Capture stored data
0% 20% 40% 60% 80% 100%
Incidents Figure 16. Top malware action varieties in incidents (n=2,103)
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We were at a hipster cofee shop and it was packed with people talking about cryptomining malware as the next big thing. The numbers in this year’s data set do not support the hype, however, as this malware functionality does not even appear in the top 10 varieties. In previous versions of VERIS,
Backdoor
C2
Spyware/keylogger
Capture app data
Adminware
Downloader
Capture stored data
Password dumper
Ram scraper
Ransomware
0% 20% 40% 60% 80% 100%
Breaches Figure 17. Top malware action varieties in breaches (n=500)
cryptominers were lumped in with click-fraud, but they received their own stand-alone enumeration this year. Combining both the new and legacy enumerations for this year, the total was 39—more than zero, but still far fewer than the almost 500 ransomware cases this year.
Email attachment
Direct install
Email unknown
Web drive-by
Download by malware
Remote injection
Email link
Network propagation
Other
Web download
0% 20% 40% 60% 80% 100%
Incidents Figure 18. Top malware action vectors in incidents (n=795)
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Delivery Method File Type 100%
75%
94% 23% 0% 45% 26% 22% 50%
25%
email web other O�ce doc Windows app other 0%
Figure 19. Malware types and delivery methods
Figure 18 displays that when the method of malware installation was known, email was the most common point of entry. This fnding is supported in Figure 19, which presents data received from millions of malware detonations, and illustrates that the median company received over 90% of their detected malware by email. Direct install is indicative of a device that is already compromised and the malware is installed after access is established. It is possible for malware to be introduced via email, and once the foothold is gained, additional malware is downloaded, encoded to bypass detection and installed directly. Like most enumerations, these are not mutually exclusive.
Social
While hacking and malicious code may be the words that resonate most with people when the term “data breach” is used, there are other threat action catego- ries that have been around much longer and are still ubiquitous. Social engineering, along with Misuse, Error, and Physical, do not rely on the existence of “cyberstuf” and are defnitely worth discussing. We will talk about these “OGs” now, beginning with the manipulation of human behavior.
There is some cause for hope in regard to phishing, as click rates from the combined results of multiple security awareness vendors are going down. As you can see in Figure 21, click rates are at 3%.
Phishing
Pretexting
Bribery
Extortion
Forgery
Infuence
Other
Scam
0% 20% 40% 60% 80% 100%
Breaches Figure 20. Top social action varieties in breaches (n=670)
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With regard to the event chain for these attacks, if the device on which the communication was read and/or interacted with does not have malicious code installed as part of the phish, it may not be recorded as an afected asset. For example, if a user is tricked into visiting a phony site and he/she then enters credentials, the human asset is recorded as well as the asset that the credentials are used to access. To that end, those moments when the users thoughts are adrift provide an excellent opportunity for criminals to phish via SMS or emails to mobile devices. This is supported by the 18% of clicks from the sanctioned phishing data that were attributed to mobile. Below is a window into mobile devices and how the way humans use them can contribute to successful phishing attacks provided by researcher Arun Vishwanath, Chief Technologist, Avant Research Group, LLC.
Research points to users being signifcantly more susceptible to social attacks they receive on mobile devices. This is the case for email-based spear phishing, spoofng attacks that attempt to mimic legitimate webpages, as well as attacks via social media.7, 8, 9
The reasons for this stem from the design of mobile and how users interact with these devices. In hardware terms, mobile devices have relatively limited screen sizes that restrict what can be accessed and viewed clearly. Most smartphones also limit the ability to view multiple pages side-by-side, and navi- gating pages and apps necessitates toggling between them—all of which make it tedious for users to check the veracity of emails and requests while on mobile.
Mobile OS and apps also restrict the availability of information often necessary for verifying whether an email or webpage is fraudulent. For instance, many mobile browsers limit users’ ability to assess the quality of a website’s SSL certifcate. Likewise, many mobile email apps also limit what aspects of the email header are visible and whether the email-source information is even accessible.
C lic
ke d
25%
20%
15%
10%
5%
0% 2.99%
2012 2014 2016 2018
Figure 21. Click rates over time in sanctioned phishing exercises
Mobile software also enhances the prominence of GUI elements that foster action—accept, reply, send, like, and such—which make it easier for users to respond to a request. Thus, on the one hand, the hardware and software on mobile devices restrict the quality of information that is available, while on the other they make it easier for users to make snap decisions.
The fnal nail is driven in by how people use mobile devices. Users often interact with their mobile devices while walking, talking, driving, and doing all manner of other activities that interfere with their ability to pay careful attention to incoming information. While already cognitively constrained, on screen notifcations that allow users to respond to incoming requests, often without even having to navigate back to the application from which the request emanates, further enhance the likelihood of reactively responding to requests.
Thus, the confuence of design and how users interact with mobile devices make it easier for users to make snap, often uninformed decisions—which signifcantly increases their susceptibility to social attacks on mobile devices.
7Vishwanath, A. (2016). Mobile device affordance: Explicating how smartphones influence the outcome of phishing attacks. Computers in Human Behavior, 63, 198-207. 8Vishwanath, A. (2017). Getting phished on social media. Decision Support Systems, 103, 70-81. 9Vishwanath, A., Harrison, B., & Ng, Y. J. (2018). Suspicion, cognition, and automaticity model of phishing susceptibility. Communication Research, 45(8), 1146-1166.
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Misuse
Misuse is the malicious or inappropriate use of existing privileges. Often it cannot be further defned beyond that point in this document due to a lack of granularity provided; this fact is refected in the more generic label of Privilege abuse as the
Privilege abuse
Data mishandling
Unapproved workaround
Knowledge abuse
Email misuse
Possession abuse
Unapproved hardware
Unapproved software
Net misuse
Illicit content
0% 20% 40% 60% 80% 100%
Breaches Figure 22. Top misuse varieties in breaches (n=292)
top variety in Figure 22. The motives are predominantly fnancial in nature, but employees taking sensitive data on the way out to provide themselves with an illegal advantage in their next endeavor are also common.
Financial
Espionage
Fun
Grudge
Other
Convenience
Ideology
Fear
Secondary
0% 20% 40% 60% 80% 100%
Breaches Figure 23. Actor motives in misuse breaches (n=245)
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Error
As we see in Figure 24, the top two error varieties are consistent with prior publications, with Misconfguration increasing at the expense of Loss and Disposal Errors. Sending data to the incorrect recipients (either via email or by mailed documents) is still an issue. Similarly, exposing data on a public website (publishing error) or misconfguring an asset to allow for unwanted guests also remain prevalent.
2010 2018 DIFF
+5Misdelivery 32% 37%
Publishing +5 error
16 21
Misconfguration +21 0 21
Loss -24 7 31
Programming +5 error
0 5
Disposal -9 error
5 14
+2Omission 2 4
-4Ga e 0 4
Breaches Figure 24. Figure 24. Top error varieties in breaches over time
n=100 (2010), n=347 (2018)
Afected assets
Workstations, web applications, and surprisingly, mail servers are in the top group of assets afected in data breaches. There is a great deal to be learned about how threat actions associate with assets within the event chains of breaches. We get down to business in Table 1 to pull out some of the more interesting stories the 2019 DBIR data has to tell us.
Server - Mail
User Dev - Desktop
Server - Web application
Server - Database
Media - Documents
Person - End-user
User Dev - Laptop
Server - POS controller
User Dev - POS terminal
Person - Finance
0% 20% 40% 60% 80% 100%
Breaches Figure 25. Top asset varieties in breaches (n=1,699)
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340
270
251
229
Action Asset Count
Hacking - Use of stolen creds Server - Mail
Social - Phishing Server - Mail
Social - Phishing User Dev - Desktop
Malware - Backdoor User Dev - Desktop
Malware - C2 User Dev - Desktop
Hacking - Use of backdoor or C2 User Dev - Desktop
Malware - Spyware/Keylogger User Dev - Desktop
Malware - Adminware User Dev - Desktop
Misuse - Privilege abuse Server - Database
Malware - Capture app data Server - Web application
Table 1 Top action and asset variety combinations within breaches, (n= 2,013)
The table above does exclude assets where a particular variety was not known. In the majority of phishing breaches, we are not privy to the exact role of the infuenced user and thus, Person - Unknown would have been present. We can deduce that phishing of Those Who Cannot Be Named leads to malware installed on desktops or tricking users into providing their credentials.
Most often, those compromised credentials were to cloud-based mail servers. There was an uptick in actors seeking these credentials to compromise a user’s email account. It turns out there are several ways to leverage this newly found access. Actors can launch large phishing campaigns from
the account, or if the account owner has a certain degree of clout, send more targeted and elaborate emails to employees who are authorized to pay bogus invoices.
There were also numerous cases where an organization’s email accounts were compromised and the adversary inserted themselves into conversations that centered around payments. At this point, the actors are appropriately positioned to add forwarding rules in order to shut out the real account owner from the conversation. Then they simply inform the other recipients that they need to wire money to a diferent account on this occasion because…reasons.
210
208
103
91
90
83
18
Another trend in this year’s data set is a marked shift away from going after payment cards via ATM/gas pump skimming or Point of Sale systems and towards e-commerce applications. The 83 breaches with the association of web application and the action of type capture application data is one indicator of this change. Figure 26 below illustrates how breaches with compromised payment cards are becoming increasingly about web servers – additional details can be found in the Retail industry section.
Not Webapp Server
75%
50%
Webapp 25% Server
2015 2016 2017 2018
Figure 26. Webapp Server vs. Not Webapp Server assets in payment data breaches over time
Compromised data
Figure 27 details the varieties of data that were disclosed as a result of the data breaches that occurred this year. Personal information is once again prevalent. Credentials and Internal are statistically even, and are often both found in the same breach. The previously mentioned credential theft leading to the access of corporate email is a very common example.
Internal
Credentials
Personal
Medical
Payment
Secrets
Bank
System
Classifed
Other
0% 20% 40% 60% 80% 100%
Breaches Figure 27. Top data varieties compromised in breaches (n=1,285)
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Breach timeline
As we have mentioned in previous reports, when breaches are successful, the time to compromise is typically quite short. Obviously, we have no way of knowing how many resources were expended in activities such as intelligence gathering and other preparations.10 However, the time from the attacker’s frst action in an event chain to the initial compromise of an asset is typically measured in minutes. Conversely, the time to discovery is more likely to be months. Discovery time is very dependent on the type of attack in question. With payment card compromises, for instance, discovery is usually based upon the fraudulent use of the stolen data (typically weeks or months), while a stolen laptop will usually be
discovered much more quickly because it is relatively obvious when someone has broken the glass out of your car door and taken your computer.
Finally, it goes without saying that not being compromised in the frst place is the most desirable scenario in which to fnd oneself. Therefore, a focus on understanding what data types you possess that are likely to be targeted, along with the correct application of controls to make that data more difcult (even with an initial device compromise) to access and exfltrate is vital. Unfortunately, we do not have a lot of data around time to exfltration, but improvements within your own organization in relation to both that metric along with time to discovery can result in the prevention of a high-impact confrmed data breach.
Compromise, (n=140)
Exfltration, (n=87)
Discovery, (n=390)
Containment, (n=127)
B re
ac he
s
60%
40%
20%
0%
60%
40%
20%
0%
60%
40%
20%
0%
60%
40%
20%
0%
Seconds Minutes Hours Days Weeks Months Years
Figure 28. Breach timelines
10Though we are starting to look before and after the breach in the Data Breaches, Extended Version section
20
Unbroken chains
While it is our belief that this section can be of interest and beneft to our readers, there are a couple of caveats that should be made clear from the beginning. First of all, we have only recently updated the VERIS schema to allow for collection of event chain data. Secondly, not all incident and breach records ofer enough details to attempt to map out the path traveled by the threat actor.
We collect an action, actor, asset, and attribute at each step. However, each may be “Unknown” or omitted completely if it did not occur in that particular step of the attack. To create a single path from these factors, we begin by placing the actor at the frst step at the beginning of the path. It’s followed by the action and then attribute present in the step. For the remaining steps it proceeds from action to attribute to action of the next step, simply skipping over any omitted.
This calls for the old Billy Baroo.
Last year we pointed out how a golfer navigating a golf course is a lot like an adversary attacking your network.11
The course creator builds sand traps and water hazards along the way to make life difcult. Additional steps, such as the length of grass in the rough and even the pin placement on the green can raise the stroke average for a given hole. In our world, you’ve put defenses and mitigations in place to deter, detect, and defend. And just like on the golf course, the attackers reach into their bag, pull out their iron, in the form of a threat action, and do everything they can to land on the attribute they want in the soft grass of the fairway.
The frst thing to know is that unlike a golfer who graciously paces all the way back to the tees to take his or her frst shot, your attackers won’t be anywhere near as courteous. In Figure 29 we see that attack paths are much more likely to be short than long. And why not, if you’re not following the rules (and which attackers do?) why hit from the tees unless you
“My golf security is so delicate, so tenuously wired together with silent inward prayers, exhortations and unstable visualizations, that the sheer pressure of an additional pair of eyes crumbles the whole rickety structure into rubble.” —John Updike, with the sympathy of some CISOs.
absolutely have to? Just place your ball right there on the green and tap it in for a birdie or a double eagle, as the case may be. And while your normal genteel golfer will abide (to a greater or lesser degree) by the course rules on the of chance that there is a Marshall watching and start on hole 1, threat actors will invari- ably take the shotgun start approach. They will begin their round on the hole they are shooting for, whether it’s confdentiality, integrity, or availability.
300
200
In ci de
nt s
100
0 0 5 10 15
Number of steps Figure 29. Number of steps per incident (n=1,285) Short attack paths are much more common than long attack paths.
Figure 30 provides a look at the three holes on our InfoSec golf course. It displays the number of events and threat actions in the attack chains, by last attri- bute afected. There is a lot to take in, and we do want to point a few things out.
11We are not saying hackers have early 90’s John Daly mullets. We don’t have data to support that. We just imagine that they do, and that this is why they all wear hoodies in clip art.
21
Availability Confdentiality
Integrity
Availability Confdentiality
Integrity
Availability Confdentiality
Integrity
Steps 14 12 10 8 6 4 2 0
Action Error Malware Physical Unknown Hacking Misuse Social
Figure 30. Attack chain by fnal attribute compromised¹² (n=941)
Integrity C
o nfd
entiality A
vailab ility
First, starting with Confdentiality, take a look at just Malware as it compromises the Confdentiality and how many short paths result from Misuse and Error, Integrity of the target. and to a lesser extent from Physical actions. On the other hand, we can see Hacking actions bounding Obviously, there’s a lot going on in Figure 30. An back and forth between attributes for several steps. easier way of looking at it is what actions start In Integrity we see an especially long chain beginning (Figure 31), continue (Figure 32), and end (Figure 33) with Hacking and going to and fro between that and incidents.
12There’s a lot going on in this figure. Take your time and explore it. For example, notice the differences between short and long attacks.
22
We see that the while Hacking is a little farther ahead, the frst action in an incident could be almost anything. The most interesting part is that Malware is at the end of the chart, even behind Physical, which requires the attacker to be, well, physically present during the attack. Malware is usually not the driver you use to get of the tee; remember that most is delivered via social or hacking actions.
Hacking
Error
Social
Misuse
Physical
Malware
0% 20% 40% 60% 80% 100%
Incidents Figure 31. Actions in frst step of incidents (n=909). An additional 32 incidents, (3.40% of all paths), started with an unknown action.
Moving on to Figure 32, Malware makes its grand entrance. It may not be the opening shot, but it is the trusty 7-iron (or 3 wood, pick your analogy according to your skills), that is your go-to club for those middle action shots. Interestingly, there are almost no Misuse and Physical middle actions and no Error in our data set. That’s primarily because these are short attack paths and to be in the middle you have to have at least three events in the chain.
Malware
Hacking
Social
Misuse
Physical
0% 20% 40% 60% 80% 100%
Incidents Figure 32. Actions in middle steps of incidents (n=302)
And fnally, we get a chance to see where attacks end in Figure 33. The most signifcant part is how Social is now at the bottom. While social attacks are signifcant for starting and continuing attacks as seen in Figures 31, they’re rarely the three-foot putt followed by the tip of the visor to the sunburned gallery.
Hacking
Malware
Error
Misuse
Physical
Social
0% 20% 40% 60% 80% 100%
Incidents Figure 33. Actions in last step of incidents (n=942)
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A tt
ac k
su cc
es s
0 5 10 15
Number of steps
75%
50%
25%
0%
Figure 34. Attack success by chain length in simulated incidents (n=87)
Attack Paths and Mitigations
Our friends at the Center for Internet Security contributed some thoughts on mitigating attack paths:
Much of security has been founded on cat- alogues of controls, vague vendor promises, laborious legislation, and tomes of things to do to keep your organization safe. Within this sea of options, we also have to justify our budgets, staf, and meet the business needs of the organization. Leveraging an attack path model is not only an important step towards formalizing our understanding of attacks, but also a means to understanding our defense. Previously, when looking at attack summary data we were presented with a snapshot of an attacker’s process which requires us to infer
At this point, you may be wondering if your sand traps are sandy enough. Figure 34 comes from breach simulation data. It shows that in testing, defenders fail to stop short paths substantially more often than long paths. So, just in case you were looking on your systems and thinking “it’s the other guys that let the attackers start on the putting green,” short attacks work.
the preceding and proceeding events. Wheth- er we realize it or not, such interpretations impact how we plan our defenses. Defending against malware takes a diferent approach if the malware is dropped via social engineering, a drive-by download, or brought in by an insider via a USB device.
In addition, while being faced with what seems like an endless list of potential attacks, limiting ourselves to snapshots also hinders our ability to fnd commonalities between these attacks. Such commonalities may be key dependencies in an attacker’s process which represent opportunities for us to disrupt. The more we can understand the sequence of events happening in an attack, the more we as a community can make it harder for adversaries to reuse the same process.
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Incident classifcation patterns and subsets Beginning with the 2014 report, we have utilized nine basic patterns to categorize security incidents and data breaches that share several similar characteristics. This was done in an efort to communicate that the majority of incidents/breaches, even targeted, sophisticated attacks, generally share enough commonalities to categorize them, and study how often each pattern is found in a particular industry’s data set. When we frst identifed the
Privilege Misuse
Denial of Service
Crimeware
Lost and Stolen Assets
Web Applications
Miscellaneous Errors
Everything Else
Cyber-Espionage
Point of Sale
patterns six years ago we reported that 92 percent of the incidents in our corpus going back 10 years could be categorized into one of the nine patterns. Fast-forwarding to today with over 375,000 incidents and over 17,000 data breaches, the numbers reveal that 98.5% of security incidents and 88% of data breaches continue to fnd a home within one of the original nine patterns. So, it would appear that, as with humans, the “I can change” mantra is false here as well.
Web Applications
Miscellaneous Errors
Privilege Misuse
Cyber-Espionage
Everything Else
Crimeware
Lost and Stolen Assets
Point of Sale
Payment Card Skimmers
Payment Card Skimmers Denial of Service
0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100%
Incidents Breaches
Figure 35. Incidents per pattern (n=41,686) Figure 36. Breaches per pattern (n=2,013)
25
The patterns will be referenced more in the industry sections, but to get acquainted or rekindle a relationship, they are defned below:
Crimeware: All instances involving malware that did not ft into a more specifc pattern. The majority of incidents that comprise this pattern are opportunistic in nature and are fnancially motivated.
Notable fndings: Command and control (C2) is the most common functionality (47%) in incidents, followed by Ransomware (28%).
Cyber-Espionage: Incidents in this pattern include unauthorized network or system access linked to state-afliated actors and/or exhibiting the motive of espionage.
Notable fndings: Threat actors attributed to state- afliated groups or nation-states combine to make up 96% of breaches, with former employees, competitors, and organized criminal groups representing the rest. Phishing was present in 78% of Cyber-Espionage incidents and the installation and use of backdoors and/or C2 malware was found in over 87% of incidents. Breaches involving internal actors are categorized in the Insider and Privilege Misuse pattern.
Denial of Service: Any attack intended to compromise the availability of networks and systems. This includes both network and application attacks designed to overwhelm systems, resulting in performance degradation or interruption of service.
Notable fndings: This pattern is based on the specifc hacking action variety of DoS. The victims in our data set are large organizations over 99 percent of the time.
Insider and Privilege Misuse: All incidents tagged with the action category of Misuse—any unapproved or malicious use of organizational resources—fall within this pattern.
Notable fndings: This is mainly insider misuse, but former and collusive employees as well as partners are present in the data set.
Miscellaneous Errors: Incidents in which unintentional actions directly compromised a security attribute of an asset.
Notable fndings: Misdelivery of sensitive data, publishing data to unintended audiences, and misconfgured servers account for 85% of this pattern.
Payment Card Skimmers: All incidents in which a skimming device was physically implanted (tampering) on an asset that reads magnetic stripe data from a payment card.
Notable fndings: Physical tampering of ATMs and gas pumps has decreased from last year. This may be attributable to EMV and disruption of card-present fraud capabilities.
Point of Sale Intrusions: Remote attacks against the environments where card-present retail transactions are conducted. POS terminals and POS controllers are the targeted assets. Physical tampering of PIN entry device (PED) pads or swapping out devices is covered in the Payment Card Skimmers section.
Notable fndings: The Accommodation industry is still the most common victim within this pattern, although breaches were less common this year.
Physical Theft and Loss: Any incident where an information asset went missing, whether through misplacement or malice.
Notable fndings: The top two assets found in Physical Theft and Loss breaches are paper documents, and laptops. When recorded, the most common location of theft was at the victim work area, or from employee-owned vehicles.
Web Application Attacks: Any incident in which a web application was the vector of attack. This includes exploits of code-level vulnerabilities in the application as well as thwarting authentication mechanisms.
Notable fndings: Over one-half of breaches in this pattern are associated with unauthorized access of cloud-based email servers.
26
Everything Else: Any incident or breach that was not categorized into one of the nine aforementioned patterns.
Notable fndings: Of the 241 breaches that fell into the Everything Else pattern, 28% are part of the Financially-Motivated Social Engineering attacks subset discussed later in this section.
Patterns within patterns
There are two subsets of incidents that will be called out when looking at industry breakouts. The increase in mail server (and email account) compromise and the signifcant dollar losses from social attacks leading to fraudulent payments provided an opportunity to create a Financially-Motivated Social Engineering (FMSE) subset that Includes incidents and breaches that would fall into Web Application Attacks or Everything Else. These incidents are included in the main corpus, but we will look at them independently as well. The incidents that comprise the botnet subset, are not part of the main data set, due to the sheer volume. These incidents could fall into Crimeware if modeled from the perspective of the malware recipient, or Web applications if the botnet steals credentials from one victim and is used against another organizations’ application. Our data is from the latter, organizations whose systems are logged on via stolen user credentials.
Financially-Motivated Social Engineering Subset: Financially motivated incidents that resulted in either a data breach or fraudulent transaction that featured a Social action but did not involve malware installation or employee misuse. Financial pretexting and phishing attacks (e.g., Business Email Compromise, W-2 phishing) are included in this subset.
Notable fndings: 370 incidents, 248 of which are confrmed data breaches, populate this subset. The incidents are split almost evenly between parent patterns of Everything Else and Web applications. The breaches are closer to a 3:1 Web Application to Everything Else ratio.
Analysis shows 6x fewer Human Resources personnel being impacted in breaches this year. This fnding, as correlated with the W-2 scams, almost disappearing from our dataset. While this may be due to improved awareness within organizations, our data doesn’t ofer any defnitive answers as to what has caused the drop.
Botnet Subset: Comprised of over 50,000 instances of customers as victims of banking Trojans or other credential-stealing malware. These are generally low on details and analyzed separately to avoid eclipsing the rest of the main analysis data set.
Notable fndings: 84% of the victims were in Finance and Insurance (52), 10% in Information (51), and 5% in Professional, Scientifc, and Technical Services (54). 180 countries and territories are represented in these breaches. Botnets are truly a low-efort attack that knows no boundaries and brings attackers either direct revenue through fnancial account compromise or infrastructure to work from.
Secondary Subset: Comprised of 6,527 incidents of web applications used for secondary attacks such as DDoS sources or malware hosting. These are legitimate incidents, but low on details and analyzed separately from the main analysis data set.
Notable fndings: Many times, these are light on specifcs, but we do know that 39% of the time they involved a malware action, with 70% of those being DDoS, and 30% exploiting a vulnerability and downloading additional malware. Attackers need infrastructure too and just like with the botnet subset, when an attacker takes over your web application, your infrastructure just got converted to multi-tenant.
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Data breaches: extended version There’s defnitely a feeling in InfoSec that the attackers are outpacing us. They’ve got all the creds, the vulns, and the shells, not to mention the possibility of huge monetary incentives. We, on the other hand, have a four-year project just to replace the servers on end-of-life operating systems. However, when contemplating this unfair advantage it’s sometimes easy for us to overlook the bigger picture. While it is true that attacks typically happen quickly (hours or less) when they are well aimed, and it also is true that when our organizations are successfully breached it often takes us months or more to learn of it, there is still room for optimism. In the paths section we examined the route that attackers take to get from point A to point B. In this section we take a look at those events that take place prior to the attack, and those required after the attack has ended in order for the attacker to realize their proft.
“Give me a place to stand and a lever long enough and I will move the world.” —Archimedes
Like all good stories, attackers need somewhere to begin, and whether this starting point is with a list of vulnerable servers, phished emails, or stolen creden- tials, if the proverbial lever is long enough they will breach your perimeter. Therefore, it is wise to do all that you can to reduce the number of starting points that they are provided. After all, vulns can usually be patched and creds can be better protected with multi-factor authentication. Having said that, we do realize that even the best security departments can only do so much. Sixty-two percent of breaches not involving an Error, Misuse, or Physical action (in other words, wounds that weren’t self-inficted) involved the use of stolen creds, brute force, or phishing. And all that malware doesn’t write itself. Admittedly, there’s not a lot you can do about the development, preparation, targeting, distribution, and other shenanigans that take place on the part of the bad guy before the breach.13 However, what goes down after the breach is another story altogether.
13Save some large organizations that have gone after dark markets or bullet-proof hosting
Just ask the axis
Let’s look at what’s being stolen. In Figure 37 we illustrate the analysis of the amount lost to attackers in two types of breaches: business email compromises and computer data breaches. This loss impact data comes courtesy of the Federal Bureau of Investigation Internet Crime Complaint Center (FBI IC3) who have ofered some helpful hints in the breakout at the end of this section. When looking at the visualized distribution, the frst thing to notice is the spike at zero. Not all incidents and breaches result in a loss. The second piece of good news is that the median loss for a business email compromise is approximately the same as the average cost of a used car. The bad news is that the dollar axis isn’t linear. There are about as many breaches resulting in the loss of between zero and the median as there are between the median and $100 million. We are no longer talking about used-car money at this point, unless you happen to be Jay Leno.
As mentioned above, there’s a great deal that has to occur even after the breach takes place to make it worth the criminal’s while. For example, business email compromises normally involve the fraudulent transfer of funds into an attacker-owned bank account.
Computer Data Breach
Median = $7,611 (n = 1,711)
Business Email Compromise
Median = $24,439 (n = 18,606)
$0 $100 $100K $100M
Dollars Figure 37. Amount stolen by breach type
28
Figure 38. Term clusters in criminal forum and marketplace posts
29
On this front, we have more glad tidings to impart. When the IC3 Recovery Asset Team acts upon BECs, and works with the destination bank, half of all US-based business email compromises had 99% of the money recovered or frozen; and only 9% had nothing recovered. Let that sink in. BECs do not pay out as well as it initially appears, and just because the attacker won the frst round doesn’t mean you shouldn’t keep fghting.
On the other hand, BECs are still advantageous for the criminal element because they provide a quick way to cash out. Many other types of data breaches require a little more work on the adversaries’ part to convert stolen data into accessible wealth. A common solution is to sell what you stole, whether PII, email addresses, creds, credit card numbers, or access to resources you have compromised. Figure 38 provides information about the numerous things for sale in the darker corners of the Internet (which surprisingly enough, resemble a 1990s video game message board). In the center we see a large blue cluster. This is comprised primarily of credit card related posts—the buying and selling of credit cards, to make money, to take money, and to cash out gains. It also includes smaller nodes related to the attacks involved in actually stealing the cards. There’s an even smaller cluster in the upper right which is related to credential theft. These may grant access to more lucrative things such as bank accounts, but many times are for consumer services including video games, streaming video, etc., that attackers use directly.
The alternative to posting this data for sale on the dark web is using the data to steal identities and committing direct fraud themselves. Herein lies the appeal of stealing tax and health-related information. Filing fraudulent tax returns or insurance claims is a relatively straightforward way to put cash in one’s pocket. The problem is that tax returns and insur- ance claims don’t pay out in unmarked bills or wire transfers to South America. This requires another step in the post-breach to-do list: money laundering. Normally money laundering is an expensive and risky task. If, for example, the money has to go through three separate set of hands on its way to its fnal destination, each person needs to take their respective cut. If the third person in the succession says they did not receive it, but the frst person insists they sent it, who does the actor believe? “There is no honor among thieves,” etc.
This is in large part why attackers often favor cryptocurrency, as is it can be laundered and transferred for relatively low cost and presents negligible risk. However, a distinct drawback is that this type of currency is a bit limited with regard to what one can purchase with it. Thus, at some point it has to be exchanged. For these and other reasons, research into increasing both the risk and cost associated with cryptocurrency laundering and/or exchange for illicit purposes has a good deal of potential as a means of increasing breach overhead and thereby decreasing the relative proft associated with such crimes.
About the IC3
The Federal Bureau of Investigation Internet Crime Complaint Center (IC3) provides the public with a trustworthy and convenient report- ing mechanism to submit information concerning suspected internet-facilitated criminal activity.
The IC3 defnes the Business Email Compromise (BEC) as a sophisticated scam targeting both business and individuals per- forming wire transfer payments.
The Recovery Asset Team (RAT) is an IC3 initiative to assist in the identifcation and freezing of fraudulent funds related to BEC incidents.
Regardless of dollar loss, victims are encouraged and often directed by law enforce- ment to fle a complaint online at www.ic3.gov. The IC3 RAT may be able to assist in the recovery eforts.
30
20
Victim demographics and industry analysis Incidents: Total Small Large Unknown Breaches: Total Small Large Unknown
Accommodation (72) 87 38 9 40 61 34 7
Administrative (56) 90 13 23 54 17 6 6
Agriculture (11) 4 2 0 2 2 2 0
Construction (23) 31 11 13 7 11 7 3
Education (61) 382 24 11 347 99 14 8
Entertainment (71) 6,299 6 6 6,287 10 2 3
Finance (52) 927 50 64 813 207 26 19
Healthcare (62) 466 45 40 381 304 29 25
Information (51) 1,094 30 37 1,027 155 20 18
Management (55) 4 1 3 0 2 1 1
Manufacturing (31-33) 352 27 220 105 87 10 22
Mining (21) 28 3 6 19 15 2 5
Other Services (81) 78 14 5 59 54 6 5
Professional (54) 670 54 17 599 157 34 10
Public (92) 23,399 30 22,930 439 330 17 83
Real Estate (53) 22 9 5 8 14 6 3
Retail (44-45) 234 58 31 145 139 46 19
Trade (42) 34 5 16 13 16 4 8
Transportation (48-49) 112 6 23 83 36 3 9
Utilities (22) 23 3 7 13 8 2 0
Unknown 7,350 0 3,558 3,792 289 0 109
Total 41,686 429 27,024 14,233 2,013 271 363 1,379
Table 2 Number of security incidents by victim industry and organization size
The data set for this report totals over 100,000 incidents, 101,168 to be exact. After we removed the subsets that were detailed in the prior section, and applied minimum complexity flters, the data set used for core analysis is established. Table 2 is the repre- sentation of that data set broken out by industry and
organization size, when known. Our annual statement on what not to do with this breakout will now follow. Do not utilize this to judge one industry over another – so a security stafer from a construction organization waving this in the face of their peer from the fnancial sector and trash-talking is a big no-no.
5
0
1
77
5
162
250
117
0
55
8
43
113
230
5
74
4
24
6
180
31
Figure 39. Industry comparison
Incidents Breaches
A cc
om m
od at
io n
(7 2)
E du
ca tio
n (6
1)
F in
an ce
(5 2)
H ea
lth ca
re (6
2)
In fo
rm at
io n
(5 1)
M an
uf ac
tu rin
g (3
1- 3
3)
P ro
fe ss
io na
l ( 5
4)
P ub
lic (9
2)
R et
ai l (
4 4
-4 5)
A cc
om m
od at
io n
(7 2)
E du
ca tio
n (6
1)
F in
an ce
(5 2)
H ea
lth ca
re (6
2)
In fo
rm at
io n
(5 1)
M an
uf ac
tu rin
g (3
1- 3
3)
P ro
fe ss
io na
l ( 5
4)
P ub
lic (9
2)
R et
ai l (
4 4
-4 5)
A ss
et
P at
te rn
Crimeware
Web Applications
Privilege Misuse
Everything Else
Denial of Service
Cyber-Espionage
Miscellaneous Errors
Lost and Stolen Assets
Point of Sale
Payment Card Skimmers
Malware
Hacking
Misuse
Social
Error
Physical
User Dev
Server
Person
Network
Media
Kiosk/Term
A ct
io n
684
206
75
69
23
22
14
4
1
575
100
76
52
36
32
29
21
9
408
79
60
59
30
14
13
9
226
37
31
30
24
19
9
6
110
104
76
71
62
39
3
3
40 2
17
14
7
5
4
1
1
13,021
4,758
2,820
1,515
992
143
93
61
163
58
40
36
23
16
14
5
92
54
21
16
14
12
10
10
7
2
796
244
72
38
14
5
699
100
96
88
38
32
524
100
91
37
13
8
279
50
43
40
19
6
124
110
100
91
85
47
61
45
18
5
5
1 13,021
4,922
4,317
1,279
201
20
233
88
56
36
16
4
162
90
16
16
15
15
874
41
38
2
1
1
722
90
69
24
16
1
559
104
58
20
4
324
45
45
10
2
225
98
93
71
3
1
68
40
18
1
3,009
1,244
777
201
3
259
62
58
2
1
1
184
30
15
9
8
1
65
45
20
17
7
3
1
1
70
45
34
22
18
12
7
2
73
28
26
10
8
8
5
1
35
24
20
9
5
3
3
97
85
65
28
27
2
2
1
38
14
3
3
2
1
1
1 140
58
40
37
33
16
8
36
14
13
12
8
5
2
88
14
11
9
8
4
3
3
2
75
67
33
32
7
2
95
69
45
36
33
18
100
69
31
29
10
3
42
38
37
16
9
1
110
85
78
78
17
46 7
42
14
2
2
1
205
173
153
66
40
9
58
42
26
14
13
2
102
70
14
14
10
6
133
32
19
2
1
1
117
70
38
17
13
1
111
73
29
14
2
60
40
32
6
1
165
80
79
29
1
55
33
14
1
173
165
131
31
1
64
44
26
2
1
118
16
10
7
4
1
Figure 39. Industry Comparison (left: all security incidents, right: only breaches) 0% 25% 50% 75% 100%
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Our community of contributors, disclosure requirements, and the population sizes for the industries all play a major part in the numbers above. The actual threat landscapes for organizations are better depicted in Figure 39. This shows what types of attack patterns are more common to your industry, along with breakouts for threat action categories and afected assets. We will explore deeper into the breach jungle, machete in hand, in the individual industry sections.
Professional (54)
Healthcare (62)
Finance (52)
Manufacturing (31-33)
Education (61)
Public (92)
Information (51)
Accommodation (72)
Retail (44-45)
0% 20% 40% 60% 80% 100%
Incidents Figure 40. FMSE incidents by industry (n=370)
Before you fip/scroll over to your industry section, we have aligned several non-incident data sources to industry that are worth your while to peruse frst.
As we break down industries we see, for example, in Figure 40 how FMSE incidents disproportionately afect Professional Services, Healthcare and Finance, with more point of sale-centric industries appear towards the bottom of the list. However, it’s clear that FMSE incidents afect all industries and so all organizations need to be trained and prepared to prevent them.
Phishing
Figure 41 ranks the click rates per industry for sanctioned security awareness training exercises. This data was provided by several vendors in this space, and merged together for analysis. While we realize we were relatively strict earlier about curtailing trash talk on the above Table, feel free to use this for some good-natured banter on an as-needed basis. Just be sure to keep it at an appropriate level. “Not looking so hot anymore for someone who works outside, Construction” is approximately the correct amount of snark (trust us, we are experts). On a positive note, all industries are clocking in with percentages that are less than the overall percentage in this study 2 years ago. So, this calls for much rejoicing.
Education 4.93% (61)
Public 4.48% (92)
Professional 3.23% (54)
Manufacturing 3.12% (31-33)
Information 2.33% (51)
Healthcare 2.13% (62)
Finance 2.04% (52)
Retail 1.32% (44-45)
0% 2% 4% 6%
Figure 41. Click rate in phishing tests by industry
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Denial of Service
Over time DDoS attacks have been getting much more tightly clumped with regard to size (similar to Manufacturing in Figure 42). However, as other industries illustrate, that is not always the case. Some industries, Information for instance, experience attacks across a much wider range. Another important takeaway is that the median DDoS doesn’t change much from industry to industry. The diference between the biggest and smallest industry median is 800Mbps and 400Kpps.
BPS
What’s your vector, Victor?
Figure 43 takes a look at the median percentage of malware vectors and fle types per industry; in other words, it helps you know where to look for the malware that’s coming in to your organization and what it will most likely look like. First of all, the majority of initial malware is delivered by email. Secondary infections are downloaded by the initial malware, or directly installed and, as such, are more difcult for network tools to spot. Secondly, though it varies a bit by industry, Ofce documents and Windows applications are the most common vehicles for the malware along with “Other” (archives, PDFs, DLLs, links, and Flash/iOS/Apple/Linux/Android apps).
PPS
0.69Gbps
1.47Gbps
1.27Gbps
0.65Gbps
0.93Gbps
0.7Gbps
1.1Gbps
0.11Mpps
0.5Mpps
0.36Mpps
0.16Mpps
0.45Mpps
0.11Mpps
0.29Mpps
Education (61) n=219
Finance (52) n=565
Information (51) n=660
Manufacturing (31-33) n=161
Professional (54) n=401
Public (92) n=792
Retail (44-45) n=52
D en
si ty
100 1K 10K 100K 1M 10M 100M 1B 10B 100B 100 1K 10K 100K 1M 10M 100M
Count Figure 42. DDoS attack bandwidth and packet counts by industry
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Delivery Method File Type
Retail and Wholesale
Public (92)
Professional (54)
Manufacturing (31-33)
Information (51)
Healthcare (62)
Finance (52)
Education (61)
Accommodation (72)
95.2%
95.6%
96.9%
98.0%
97.7%
91.1%
96.8%
61.4%
94.8%
17.1% 0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
15.1%
14.4%
9.0%
10.5%
21.0%
10.3%
86.1%
16.7%
41.5%
64.4%
51.2%
37.6%
49.7%
67.1%
74.5%
26.6%
56.2%
28.2% 21.2%
16.1%
18.4%
25.7%
20.7%
17.4%
12.6%
34.2%
19.0%
24.9%
25.0%
33.3%
25.3%
10.0%
12.5%
27.2%
15.8%
100%
75%
50%
25%
0%
email web other O–ce doc Windows app other
Figure 43. Malware types and delivery methods by industry
35
Accommodation and Food Services
The breach totals in our data set have decreased from last year, primarily due to a lack of POS vendor incidents that have led to numerous organizations being compromised with stolen partner credentials.
Frequency 87 incidents, 61 with confrmed data disclosure
Top 3 patterns Point of Sale intrusions, Web applications and Crimeware patterns represent 93% of all data breaches within Accommodation
Threat actors External (95%), Internal (5%) (breaches)
Actor motives Financial (100%) (breaches)
Data compromised Payment (77%), Credentials (25%), Internal (19%) (breaches)
How can we be of service?
The Accommodation industry prides itself on hospitality, and over the years it has been far too hospitable to criminals. Financially motivated actors are bringing home the bacon by compromising the Point of Sale (POS) environments and collecting customers’ payment card data. Table 3 lists the 10 most common combinations of threat action varieties and assets. These are pairings that are found in the same breach, but not necessarily the same event or step in the breach.
As stated above, some of these combinations are indicative of a specifc action taken against a specifc asset (e.g., RAM Scraping malware infecting a
POS Terminal). Others show that some actions are conducted earlier or later in event chains that feature a particular asset – you don’t phish a laptop, but you may phish a human and install malware on his/her laptop in the next step. In brief, the game has not changed for this industry. POS Controllers are compromised and malware specifcally designed to capture payment card data in memory is installed and extended to connected POS Terminals. While these POS intrusions are often a small business issue, large hotel and restaurant chains can learn from this data and if they use a franchise business model, disseminate this knowledge to their franchisees.
The RAM scrapers may be the specialty of the house, but malware does not spontaneously appear on systems. When the infection vector is known, it is typically a direct installation after the actors use stolen, guessable, or default credentials to gain access into the POS environment.
A cause for optimism?
While attacks against POS environments make up the vast majority of incidents against Accommodation and Food Service organizations, the number has decreased from 307 in last year’s report to 40 in this report. Sounds pretty dope so far, but we do not use number of breaches as a solid indicator of “better” or “worse” as there are not only changes in our contributors, but also changes in the types of events our contributors may focus on year over year. Even with such a drastic change, it isn’t unprecedented. Figure 44 shows the volatility of breach counts of this ilk. POS breaches are often conducted by organized criminal groups looking to breach numerous targets and there have been sprees of hundreds of victims associated with the same hacking group. Back in 2011, default credentials were used with great success evidenced by over 400 breaches, and recent sprees have been associated with POS vendors sufering breaches leading to subsequent breaches of their customer base.
36
32
27
8
8
7
Action Asset Count
Malware - RAM scraper Server - POS controller
Malware - RAM scraper User Dev - POS terminal
Hacking - Use of stolen creds Server - Mail
Social - Phishing Server - Mail
Hacking - Use of stolen creds Server - POS controller
Hacking - Use of stolen creds User Dev - POS terminal
Malware - Backdoor Server - POS controller
Malware - Backdoor User Dev - POS terminal
Hacking - Brute force Server - POS controller
Hacking - Brute force User Dev - POS terminal
Table 3 Top threat action and asset pairings within Accommodation breaches (n= 61)
B re
ac he
s
400
300
200
100
0 2010 2012 2014 2016 2018
Figure 44. POS intrusions in Accommodation breaches over time
The absence of a large spree in this year’s data set is refected in the drop, but (and it seems like there is always a “but”) after our window for data closed and during this writing there has already been a publicly disclosed POS vendor breach afecting multiple food service victims.14 So, let this be the frst ever sneak peek into the 2020 DBIR – POS attacks are not quite an endangered species.
14https://ncbpdataevent.com/
And speaking of delivering bad news
Accommodation data breach victims are informed of their plight the majority of the time via Common Point of Purchase alerts as shown in Figure 45. In fact, 100 percent of POS intrusions in this industry were discovered via external methods. This is a clear indicator that while there is work to be done on preventative controls around POS compromise, there is equal room for improvement in detecting compromise. Being a realist and understanding that many of these victims are “mom and pop” operations asking for sophisticated fle integrity software or DLP is not a feasible plan of action for many of these organizations. Working with POS vendors to ensure that someone knows when the environment is accessed via existing remote access methods is a start. A pragmatic process to inform the business owners that legitimate work is being done by the partner would certainly be another simple step up from the current state of afairs.
7
6
6
5
3
37
Fraud detection
Customer
Law enforcement
Log review
Break in discovered
IT review
0% 20% 40% 60% 80% 100%
Breaches Figure 45. Discovery methods in Accommodation breaches (n=42)
Things to consider:
No vacancy The numbers from annual breach totals are infuenced by smaller food service businesses caught up in what we have described as POS smash-and-grabs. Whether leveraging default credentials or stolen credentials, organized criminal groups often go after numerous little fsh – but not always. Several interna- tional hotel chains and restaurants have also been hit. While the initial intrusion method may not have been as easy as scanning the Internet and issuing a de- fault password, there are some lessons to be learned. Static authentication is circumvented using valid credentials and what follows is installation of RAM scraping malware and adminware such as psexec or PowerShell to facilitate the spread of malware across multiple terminals in multiple locations.
Cover your assets The data shows year-over-year that there is a malware problem afecting POS controllers and terminals. Implement anti-malware defenses across these environments and validate (and re–vali- date) the breadth of implementation and currency of controls. Focus on detective controls as well, the external correlation of fraudulent usage of payment cards should not be the sole means of fnding out that malware has been introduced into your POS environment. Restrict remote access to POS servers and balance the business needs of intercon- nectivity between POS systems among your locations with defending against the potential spread of malware from the initial location compromised.
Sleep with one eye open Since you can’t build a perfectly secure system, security operations helps monitor for those weird logins in the middle of the night. If you can justify it in your budget, a security operations team is a must. Even if you can’t aford an in-house team, contracting it as a service or requiring it to be a part of your POS or IT contracts will cover you and allow you to beneft from economies of scale.
Chips and Dip When a chip-enabled card is dipped in a properly confgured EMV-enabled POS terminal, the static, reusable magnetic strip information (PAN) is not exposed or stored. This is a good thing and along with contactless payment methods, disrupts the old way of stealing things for the bad guys. The attacks against EMV technology are more theoretical and/or not conducive to real-world use. We know that cyber-criminals are a crafty bunch and nothing is bulletproof, but continue to embrace and implement new technologies that raise the bar to protect against payment card fraud.
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Educational Services
Education continues to be plagued by errors, social engineering and inadequately secured email credentials. With regard to incidents, DoS attacks account for over half of all incidents in Education.
Frequency 382 incidents, 99 with confrmed data disclosure
Top 3 patterns Miscellaneous Errors, Web Application Attacks, and Everything Else represent 80% of breaches
Threat actors External (57%), Internal (45%), Multiple parties (2%) (breaches)
Actor motives Financial (80%), Espionage (11%), Fun (4%), Grudge (2%), Ideology (2%) (breaches)
Data compromised Personal (55%), Credentials (53%), and Internal (35%) (breaches)
It’s in the syllabus
Anticipating the top pattern for Education each year is a bit like playing the “which shell is it under?” game. You know it’s (most likely) under one of three shells, but when you fnally point to one, the data proves you wrong with a deft statistical sleight of hand. There were three patterns in a statistical dead heat and like the Netherlands’ women speed skaters in the 3000m, it was a dominant podium sweep. Miscellaneous Errors (35%) had a strong showing, because (spoiler alert) people still have their moments. Most of these errors are of the typical misdelivery and publishing error types that we have all come to know and love.
Miscellaneous Errors
Web Applications
Everything Else
Privilege Misuse
Cyber-Espionage
Lost and Stolen Assets
Crimeware
0% 20% 40% 60% 80% 100%
Breaches Figure 46. Patterns within Education breaches (n=99)
Web Application Attacks accounted for roughly one quarter of breaches in the Education vertical. This is mostly due to the frequent compromise of cloud-based mail services via phishing links to phony login pages. So, if you use such a service 24/7/...365 you might want to consider tightening up your password security and implement a second authentication factor and then turning of IMAP.
2
39
Use of stolen creds
Use of backdoor or C2
Brute force
Exploit vuln
MitM
0% 20% 40% 60% 80%
Breaches Figure 47. Hacking varieties in Education breaches (n=37)
100%
Web application
Backdoor or C2
Desktop sharing
Other
Partner
0% 20% 40% 60% 80%
Breaches Figure 48. Hacking vectors in Education breaches (n=33)
100%
Everything Else, as previously stated, is more or less the pattern equivalent of a “lost and found” bin. It contains numerous incident types we frequently encounter but that do not provide enough granularity for us to place in one of the other patterns. For exam- ple, there are compromised mail servers, but it was undetermined if stolen web credentials were the point of entry. About half or more of these breaches could be attributed to social engineering attacks via phishing.
When known, the motivation is primarily fnancial, and is carried out mostly by organized criminal groups. There was a smattering of state-afliated or cyber- espionage cases in this year’s data set, a reduction from the 2017 report as shown in Figure 49. This fnding should not convince our readers that attacks seeking research fndings and other espionage-related goals have gone the way of Home Economics in this vertical, but is instead more related to the number and type of incidents provided by our partners.
2016 2018 DIFF
Financial +33 45% 79%
Espionage -31 12 43
Fun -4 5 9
Grudge 0 2
Ideology +2 0 2
Breaches
Figure 49. External motives in Education breaches over time n=44 (2016), n=42 (2018) (Secondary motives excluded)
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Things to consider:
Clean out your lockers Many of the breaches that are repre- sented in this industry are a result of poor security hygiene and a lack of attention to detail. Clean up human error to the best extent possible – then establish a baseline level of security around internet-facing assets like web servers. And in 2019, 2FA on those servers is baseline security.
Varsity or JV? Universities that partner with private Silicon Valley companies, run policy institutes or research centers are probably more likely to be a target of cyber-espionage than secondary school districts. Understand what data you have and the type of adversary who historically seeks it. Your institution of learning may not be researching bleeding-edge tech, but you have PII on students and faculty at the very least.
Security conformity There are threats that (no matter how individualized one may feel) everyone still has to contend with. Phishing and general email security, Ransomware, and DoS are all potential issues that should be threat modeled and addressed. These topics may not seem new, but we still have not learned our lesson.
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Financial and Insurance
Denial of Service and use of stolen credentials on banking applications remain common. Compromised email accounts become evident once those attacked are fltered. ATM Skimming continues to decline.
Frequency 927 incidents, 207 with confrmed data disclosure
Top 3 patterns Web Applications, Privilege Misuse, and Miscellaneous Errors represent 72% of breaches
Threat actors External (72%), Internal (36%), Multiple parties (10%), Partner (2%) (breaches)
Actor motives Financial (88%), Espionage (10%) (breaches)
Data compromised Personal (43%), Credentials (38%), Internal (38%) (breaches)
Filters are not just for social media photos
We use flters in data analysis to focus on particular industries or threat actors and to pull out interesting topics to discuss. We also exclude certain subsets of data in order to reduce skew and avoid overlooking other trends and fndings. This is not to say that we ignore or deny their existence, but rather we analyze them
independently in other sections of this study. In this industry, we acknowledge, but flter, customer credential theft via banking Trojan botnets. Their numbers in this year’s data set show that they are not inconsequential matters, over 40,000 breaches associated with botnets were separately analyzed for the fnancial sector. We discuss both of these scenarios in more depth in the Results and Analysis section, but there is not much to say that has not already been said on the subjects. Below is what’s left and we will start with the common pairings of action and asset varieties.
Keep in mind that breaches are often more than one event, and sometimes more than one of the combinations above are found in the same breach.
I’d rather be phishing
When we look at the two pairings that share mail servers as an afected asset in Table 4, we can see a story developing. Adversaries are utilizing social engineering tactics on users and tricking them into providing their web-based email credentials. That is followed by the use of those stolen creds to access the mail account. There are also breaches where the method of mail server compromise was not known, but the account was known to have been used to send phishing emails to colleagues. So, while the specifc action of phishing is directed at a human (as, by defnition, social attacks are), it often precedes or follows a mail server compromise. And there is no law that states that phishing cannot both precede and follow the access into the mail account (there are laws against phishing, however). Phishing is also a great way to deliver malicious payloads.
42
End of an era? 2017 2018 DIFF
Physical attacks against ATMs have seen a decline Personal
from their heyday of the early 2010s. We are hopeful 36% 43%
that the progress made in the implementation of EMV chips in debit cards, infuenced by the liability shift to ATM owners, is one reason for this decline. ATM jackpotting is certainly an interesting way to make a buck, but is not a widespread phenomenon. Figure 50 highlights the drop in Payment card data compromise Payment
from last year’s report. 14 33
While payment card breaches are declining, personal data is showing the largest gain from the 2018 report. Focusing on fnancial breaches where personal data was compromised, social attacks (Everything Else), misdelivery of data and misconfgurations Bank (Miscellaneous Errors), Web Applications and 14 21
Privilege Misuse are behind over 85 percent.
+7
-20
+7
+27Credentials 12 38
Breaches Figure 50. Select data varieties in Financial breaches over time n=144 (2017), n=125 (2018)
Action Asset Count
Hacking - Use of stolen creds Server - Mail
Social - Phishing Server - Mail
Hacking - Use of backdoor or C2 User Dev - Desktop
Malware - C2 User Dev - Desktop
Physical - Skimmer Kiosk/Term - ATM
Misuse - Privilege abuse Server - Database
Hacking - Use of stolen creds Server - Web application
Social - Phishing User Dev - Desktop
Error - Misdelivery User Dev - Desktop
Malware - Backdoor User Dev - Desktop
Table 4 Top combinations of threat actions and assets, (n= 207)
43
41
17
16
16
14
10
10
9
9
43
Things to consider:
Do your part 2FA everything. Use strong authentication on your customer- facing applications, any remote access, and any cloud-based email. Contrarians will be quick to point out examples of second authentication factors being compromised, but that does not excuse a lack of implementation.
Squish the phish There is little that fnancial organizations can do to ensure that their customers are running up-to- date malware defenses or make them “phish-proof,” but spreading a little security awareness their way can’t hurt. And speaking of security awareness, leverage it to keep employees on their toes when interacting with emails.
Inside job There were 45 confrmed breaches associated with misuse of privileges. The details were light on most of these but tried and true controls are still relevant. Monitor and log access to sensitive fnancial data (which we think you are already), and make it quite clear to staf that it is being done and just how good you are at recognizing fraudulent transactions. In other words, “Misuse doesn’t pay.”
44
Healthcare
Healthcare stands out due to the majority of breaches being associated with internal actors. Denial of Service attacks are infrequent, but availability issues arise in the form of ransomware.
Frequency 466 incidents, 304 with confrmed data disclosure
Top 3 patterns Miscellaneous Errors, Privilege Misuse and Web Applications represent 81% of incidents within Healthcare
Threat actors Internal (59%), External (42%), Partner (4%), and Multiple parties (3%) (breaches)
Actor motives Financial (83%), Fun (6%), Convenience (3%), Grudge (3%), and Espionage (2%) (breaches)
Data compromised Medical (72%), Personal (34%), Credentials (25%) (breaches)
The doctor can’t see you now (that you work for them)
Most people do not enjoy going to the hospital, but once it becomes unavoidable we all need to believe fervently that the good women and men who are providing us care are just this side of perfect. Spoiler alert: they are not. Healthcare is not only fast paced and stressful, it is also a heavily-regulated industry. Those who work in this vertical need to do things right, do things fast, and remain in compliance with legislation such as HIPAA and HITECH (in the US). That in itself is a pretty tall order, but when one combines that with the fact that the most common threat actors in this industry are internal to the organization, it can paint a rather challenging picture.
With internal actors, the main problem is that they have already been granted access to your systems in order to do their jobs. One of the top pairings in Table 5 between actions and assets for Healthcare was privilege abuse (by internal actors) against databases. Efectively monitoring and fagging unusual and/or inappropriate access to data that is not necessary for valid business use or required for patient care is a matter of real concern for this vertical. Across all industries, internal actor breaches have been more difcult to detect, more often taking years to detect than do those breaches involving external actors.
Mailing it in
The Healthcare industry has a multifaceted problem with mail, in both electronic and printed form. The industry is not immune to the same illnesses we see in other verticals such as the very common scenario of phishing emails sent to dupe users into clicking and entering their email credentials on a phony site. The freshly stolen login information is then used to access the user’s cloud-based mail account, and any patient data that is chilling in the Inbox, or Sent Items, or other folder for that matter is considered compromised – and its disclosure time.
Misdelivery, sending data to the wrong recipient, is another common threat action variety that plagues the Healthcare industry. It is the most common error type that leads to data breaches as shown in Figure 51. As seen in Table 5 on the next page, documents are a commonly compromised asset. This could be due to errors in mailing paperwork to the patient’s home address or by issuance of discharge papers or other medical records to the wrong recipient.
Ransomware “breaches”
Most ransomware incidents are not defned as breaches in this study due to their lack of the required confrmation of data loss. Unfortunately for them, Healthcare organizations are required to disclose ransomware attacks as though they
45
were confrmed breaches due to U.S. regulatory requirements. This compulsory action will infuence the number of ransomware incidents associated with the Things to consider: Healthcare sector. Acknowledging the bias, this is the second straight year that ransomware incidents were Easy access over 70 percent of all malware outbreaks in this vertical. Know where your major data stores are,
limit necessary access, and track all access attempts. Start with monitoring the users who have a lot of access that
Misdelivery might not be necessary to perform their jobs, and make a goal of fnding any unnecessary lookups.
Publishing error Snitches don’t get stitches Work on improving phishing reporting to more quickly respond to early clickers
Disposal error and prevent late clickers. Think about reward-based motivation if you can—you catch more fies with honey. And you can catch phish with fies. Coincidence? Loss
Perfectly imperfect Know which processes deliver, publish
Misconfguration or dispose of personal or medical information and ensure they include checks so that one mistake doesn’t
0% 20% 40% 60% 80% 100% equate to one breach. Breaches Figure 51. Top error varieties in Healthcare breaches (n=109)
Action Asset Count
Hacking - Use of stolen creds Server - Mail 51
Misuse - Privilege abuse Server - Database 51
Social - Phishing Server - Mail 48
Error - Misdelivery Media - Documents 30
Physical - Theft Media - Documents 14
Error - Publishing error Server - Web application 13
Error - Disposal error Media - Documents 12
Error - Loss Media - Documents 12
Error - Misdelivery User Dev - Desktop 12
Hacking - Use of stolen creds Person - End-user 7
Table 5 Top pairs of threat action varieties and asset varieties, (n= 304)
46
Information
Web applications are targeted with availability attacks as well as leveraged for access to cloud-based organizational email accounts.
Frequency 1,094 Incidents, 155 with confrmed data disclosure
Top 3 patterns Miscellaneous Errors, Web Applications, and Cyber- Espionage represent 83% of breaches within Information
Threat actors External (56%), Internal (44%), Partner (2%) (breaches)
Actor motives Financial (67%), Espionage (29%) (breaches)
Data compromised Personal (47%), Credentials (34%), Secrets (22%) (breaches)
The Information Society
The Information industry is a veritable pantechnicon (look it up) that is chock-full of organizations that have to do with the creation, transmission and storing of information. One might think that with so wide an array of victims, the attacks would be all over the place, but, in fact, it is our duty to inform you that much of what we saw in this category for the 2019 report mirrors last year’s results. As was the case in 2018, most of the incidents in this industry consists of DoS attacks (63%). In fact, it is perhaps ftting that this industry covers both TV and motion pictures, since it is in many ways a rerun of last year’s programming when viewed from an incident point of view.
With regard to confrmed data disclosure, two of the top three patterns remain the same as last year (albeit in a diferent order) and we have one newcomer. In order of frequency, the patterns are Miscellaneous Errors (42%), Web App attacks (29%) and Cyber-Espionage (13%). Let’s take a quick look at the most common errors below.
Misconfguration
Publishing error
Programming error
Misdelivery
Omission
Malfunction
0% 20% 40% 60% 80% 100%
Breaches Figure 52. Error varieties in Information breaches (n=66)
47
24
22
19
16
Action Asset Count
Error - Misconfguration Server - Database
Social - Phishing Person - Unknown
Hacking - Unknown Server - Web application
Malware - C2 User Dev - Desktop
Social - Phishing User Dev - Desktop
Malware - Backdoor Person - Unknown
Malware - Backdoor User Dev - Desktop
Malware - C2 Person - Unknown
Error - Publishing error Server - Web application
Hacking - Use of stolen creds Person - Unknown
Table 6 Top pairs of threat action varieties and asset varieties, (n= 155)
Faulty towers
No one is perfect, but when you are a system administra- tor you are often provided with a better stage on which to showcase that imperfection. Figure 52 illustrates how errors are put in the spotlight. Our data indicates that misconfguration (45%) and publishing errors (24%) are common miscues that allowed data disclosure to occur. When looking at the relationship between actions and assets in Table 6, 36% (24 of 67) of error-related breach- es involved misconfgurations on databases, often cloud storage – not good. Obviously, those buckets of data are meant to store lots of information and if your bucket has a (fgurative) hole in it, then it may run completely dry before you make it back home from the well and notice. Often these servers are brought online in haste and confgured to be open to the public, while storing non-public data. Publishing errors on web applications ofer a similar exposure of data to a much wider than intended audience. Just for cmd shift and giggles, we will mention that programming errors were committed on web servers and a couple of databases.
It’s not only Charlotte’s Web (apps) you can read about
Even if your IT department doesn’t make big mistakes like the poor unfortunate souls above, there is no need to worry. You still have more excellent chances to get your data stolen. Criminals do love a tempting freshly baked (or half-baked) web application to attack. The illicit use (and reuse) of stolen creds is a common hacking action against web applications regardless of industry. The malware action variety of capture app data is more commonly associated with e-retailers, the application data being captured is the user inputting payment information. While not as common, any internet portals or membership sites that sell content as opposed to a physical product would fall into the Information sector. And payment cards used to purchase content are just as good to steal as ones used to buy shoes online.
16
15
15
15
14
14
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I spy with my little eye, something phished
The third pattern in Information breaches we highlight is Cyber-Espionage. An eye opening 36 percent of external attackers were of the state- afliated variety, statistically even with organized crime. As we have pointed out many times in the past, most Cyber-Espionage attacks begin with a successful phishing campaign and that goes some
way to explain why 84 percent of social attacks in this industry featured phishing emails.
Sir Francis Bacon once famously stated “knowledge is power.” Perhaps a better defnition for 2019 would be “to gain and to control information is power.” Therefore, we should probably not be shocked that the organizations that own and distribute that information are the target of such attacks.
Things to consider:
Asset assistance Whether intentional web attacks or erroneous actions, both databases and web application servers are oft-compromised assets, especially for this industry. Many will complain about”‘checklist security” but a standard protocol regarding bringing up cloud servers and publishing sensitive data on websites – if implemented and followed – would go a long way to mitigate human error/carelessness.
Scrubbing packets While breaches were at the forefront of this section, DDoS protection is an essential control for Information entities given the percentage
of Denial of Service incidents. Guard against non-malicious interruptions with continuous monitoring and capacity planning for trafc spikes.
It bears repeating Knowledge is power, and the increase in state-afliated attacks is a data point we will keep an eye on. It could very well be a spike and not indicative of a trend, but Information organizations have desirable data and these motivations would not be likely to disappear in a year. Understand that these attacks are often “phishy” in nature and start with a compromised workstation and escalate from there.
49
Manufacturing
Manufacturing has been experiencing an increase in fnancially motivated breaches in the past couple of years, but espionage is still a strong motivator. Most breaches involve phishing and the use of stolen credentials.
Frequency 352 incidents, 87 with confrmed data disclosure
Top 3 patterns Web Applications, Privilege Misuse, and Cyber-Espionage represent 71% of breaches
Threat actors External (75%), Internal (30%), Multiple parties (6%), Partner (1%) (breaches)
Actor motives Financial (68%), Espionage (27%), Grudge (3%), Fun (2%) (breaches)
Data compromised Credentials (49%), Internal (41%), Secrets (36%) (breaches)
Uncle Owen, this R2 unit has a fnancial motivator
For the second year in a row, fnancially motivated attacks outnumber cyber-espionage as the main reason for breaches in Manufacturing, and this year by a more signifcant percentage (40% diference). If this were in most any other vertical, it would not be worth mentioning as money is the reason for the vast majority of attacks. However, Manufacturing has experienced a higher level of espionage-related breaches than other verticals in the past few years. So, shall we conclude that James Bond and
Ethan Hunt15 have fnally routed their respective nemeses for good? Are we free to buy the world a Coke and teach it to sing in perfect harmony? Probably not. A more likely explanation is that some of our partners who typically provide data around cyber-espionage were either unable to participate this year or simply happened to work other types of investigations. This may have contributed to a bias on those results, meaning the real percentage of cyber-espionage cases was higher in the wild. If the relative percentage of one type of case goes down, the result is an apparent upswing in the other.
Web Applications
Privilege Misuse
Cyber-Espionage
Miscellaneous Errors
Everything Else
Crimeware
Lost and Stolen Assets
0% 20% 40% 60% 80% 100%
Breaches Figure 53. Patterns in Manufacturing breaches (n=87)
15 Old-school readers, feel free to substitute Rollin Hand as the pop culture reference here if preferred.
50
Speaking to the web application attacks, this industry shares the same burden of dealing with stolen web- mail credentials as other industries. Most breaches with a web application as a vector also featured a mail
Use of stolen creds
Exploit vuln
Use of backdoor or C2
Abuse of functionality
Brute force
Other
SQLi
Bu�er overfow
Path traversal
URL redirector abuse
0% 20% 40% 60% 80% 100%
Breaches Figure 54. Hacking varieties in Manufacturing breaches (n=43)
server as an afected asset. From an overall breach perspective, the use of stolen credentials and web applications were the most common hacking action and vector – see Figures 54 and 55.
Web application
Backdoor or C2
VPN
Desktop sharing
Desktop sharing software
0% 20% 40% 60% 80% 100%
Breaches Figure 55. Hacking vectors in Manufacturing breaches (n=49)
51
Secrets and truths
The Cyber-Espionage pattern, while not as prominent as in past reports, is still an attack type that we recommend the Manufacturing industry defend against. The typical utilization of phishing attacks to convince users to install remote access tools that establish footholds and begin the journey towards stealing important competitive information from victims remains the same.
In keeping with the aforementioned rise in fnancially motivated attacks, the primary perpetrator when known is organized crime. With regard to data variety, there is a group of four data types that feature prominently in this industry. Credentials (49%) and Internal data (41%), stem from the webmail attacks – if a more specifc data type is not known, Internal is used for compromised organizational emails. Secrets (36%) drop from previous heights commensurate to the reduction in espionage as a motive. The fourth amigo is Personal information (25%), a data type that includes employee’s W-2 information and other nuggets that can be used for identity theft.
Things to consider:
Multiple factors work better than one It is a good idea to deploy multiple factor authentication throughout all systems that support it, and discourage password reuse. These actions will defnitely help mitigate the impact of stolen credentials across the organization.
Recycling also applies for security Regardless of motivation, a large number of breaches in this sector started with phishing or pretexting attacks. Providing employees with frequent security training opportunities can help reduce the likelihood they will be reeled in by one of those attacks.
Workers must use safety equipment at all times Unless inconvenient to do so – due to the prevalence of malware usage in the espionage breaches, it is advisable to deploy and keep up-to-date solutions that can help detect and stop those threats.
52
54
Professional, Technical and Scientifc Services
Phishing and credential theft associated with cloud- based mail accounts have risen as the prominent attack types.
Frequency 670 incidents, 157 with confrmed data disclosure
Top 3 patterns Web Applications, Everything Else, and Miscellaneous Errors represent 81% of breaches within Professional Services
Threat actors External (77%), Internal (21%), Partner (5%), Multiple parties (3%) (breaches)
Actor motives Financial (88%), Espionage (14%), Convenience (2%) (breaches)
Data compromised Credentials (50%), Internal (50%), Personal (46%) (breaches)
Wide range of services, narrower range of threats
Professional Services is a broad category even by NAICS standards, and the members of its ranks include law ofces, advertising agencies, and engineering and design frms, to name only a few. Starting with a focus on the data lost in the 157 Professional Services breaches, Figure 56 gives us an idea of the types of data most commonly involved in these cases.
2014 2018 DIFF
Personal +27 19% 46%
Credentials +28 23 50
Internal or 0
Secrets 54
Breaches Figure 56. Top error varieties in Professional Services breaches over time, n=105 (2014), n=137 (2018)
We see an overall increase in Personal data and Credentials breached. A lot of this comes from breaches now compromising multiple data types at the same time. Often, credentials are the key that opens the door for other actions. Figure 57 shows that most of the time, it’s on the way to compromise Internal and/or Personal data. This is indicative of gaining access to a user’s inbox via webmail login using stolen credentials.
53
Internal
Personal
No other variety
0% 20% 40% 60% 80% 100%
Breaches Figure 57. Other data varieties in Professional Services credential breaches (n=69)
Sometimes you just have to ask
Credentials compromising email...sounds a lot like Business Email Compromise doesn’t it? Figure 58 provides ample evidence that BECs are an issue for Professional Services. Financial staf were the most likely to be compromised in incidents involving fraudulent transactions, but it should be noted that executives were compromised in 20 percent of the incidentsand are 6x more likely to be the asset compromised in Professional Services breaches than the median industry. You have to hand it to the attackers. At some point one must have thought “why don’t we skip all the hard hacking and just, you know, ask for the money?”
Pretexting
Finance sta� compromised
Use of stolen creds
Executive sta� compromised
0% 20% 40% 60% 80% 100%
Incidents Figure 58. Select enumerations in fraudulent transaction incidents (n=41)
Paths of the unrighteous
To wrap up, Figure 59 illustrates the single step Misuse and Error breaches, but also shows us the Social and Hacking breaches that take slightly longer to develop. All of it provides excellent immediate teaching mo- ments for any organization.
Availability
Confdentiality
Integrity
4 3 2 1 0
Steps
Action Error Malware Social Hacking Misuse Unknown
Figure 59. Confdentiality attack chains for Professional Services incidents (n=90) Misuse and error are short paths while social and hacking take longer.
54
Things to consider:
One is the loneliest number We don’t like saying it any more than you like hearing it, but static credentials are the keys. Password managers and two-factor authentica- tion are the spool pins in the lock. Don’t forget to audit where all your doors are. It doesn’t help to put XO-9s on most of your entrances if you’ve got one in the back rocking a screen door.
Social butterfies You know a great way to capture credentials?
A social attack. At least we know where it’s coming from. Monitor email for links and executables (including macro-enabled Ofce docs). Give your team a way to report potential phishing or pretexting.
To err is human Set your staf up for success. Monitor what processes access personal data and add in redundant controls so that a single mistake doesn’t result in a breach.
55
Public Administration
Cyber-Espionage is rampant in the Public sector, with State-afliated actors accounting for 79 percent of all breaches involving external actors. Privilege Misuse and Error by insiders account for 30 percent of breaches.
Frequency 23,399 incidents, 330 with confrmed data disclosure
Top 3 patterns Cyber-Espionage, Miscellaneous Errors and Privilege Misuse represent 72% of breaches
Threat actors External (75%), Internal (30%), Partner (1%), Multiple parties (6%) (breaches)
Actor motives Espionage (66%), Financial (29%), Other (2%) (breaches)
Data compromised Internal (68%), Personal (22%), Credentials (12%) (breaches)
Given the sheer number of incidents in this sector, you would think that the government incident responders must either be cape and tights wearing super heroes, or so stressed they’re barely hanging on by their fngernails. And while that may yet be the case, keep in mind that we do have very good visibility into this industry, in part due to regulatory requirements that members (at least in the United States) must report their incidents to one of our data sharing partners (the US-CERT). Arguably more interesting is the fact that with similar breach numbers from last year’s report, the makeup of the breaches has seen some change.
Master of whisperers
While the Cyber-Espionage pattern was also the most prominent in this industry in last year’s report, the number of breaches in the Cyber-Espionage pattern is 168% of last year’s amount. Figure 60 shows how the percentages shifted from last year.
The most common pairings of threat actions and assets in Table 7 tells a story that is as easy to follow as “See Spot Send Malicious Attachments and Gain a Foothold.” We have a gang of fve threat actions found in breaches that had a human asset16 and a workstation as afected assets. We are seeing the familiar phish > backdoor/C2 > use of the newly acquired channel into the network. Admittedly we do not have as much data as to what is happening beyond the deception and initial device compromise. The inclusion of keylogging malware is a good indicator that additional credential theft and reuse is a likely next step.
2017 2018 DIFF
Cyber-Espionage +17 25% 42%
Everything Else -6 11 17
Privilege Misuse -5 12 17
Web Applications -6 10 16
Miscellaneous Errors +1 16 18
Breaches
Figure 60. Patterns in Public breaches over time n=305 (2017), n=330 (2018)
16Person – Unknown was not filtered out due to the amount of phishing without a known organizational role associated with the target.
56
155
139
130
129
Action Asset Count
Social - Phishing Person - Unknown
Social - Phishing User Dev - Desktop
Malware - Backdoor Person - Unknown
Malware - Backdoor User Dev - Desktop
Hacking - Use of backdoor or C2 Person - Unknown
Hacking - Use of backdoor or C2 User Dev - Desktop
Malware - C2 User Dev - Desktop
Malware - C2 Person - Unknown
Malware - Spyware/Keylogger User Dev - Desktop
Malware - Spyware/Keylogger Person - Unknown
Table 7 Common threat action and asset combinations within Public breaches, (n=330)
I click, therefore I am
Since we have established a bit of a problem with malicious emails, we wanted to dig more into the security awareness training data provided to us this year. Figure 61 shows how quickly employees in this sector are clicking or reporting on phishing emails. Early on in the training similar percentages of users are clicking and reporting, but reporting drops of after the frst hour, where clicking is more active. Not optimal, but since this was sanctioned and not actually malicious, nothing was done after the initial reporting other than an “atta boy.” Having documented, understood, and tested incident response plans to the real thing will allow the containment process to begin during that frst hour to limit the efectiveness and impact through quick identifcation. This should also limit the opportunity for the users who are not KonMari-ing their inboxes to interact with the malicious message days later.
S im
ul at
ed P
hi sh
es 10%
Clicked 8%
5%
2%
0%
Figure 61. Click and reporting rate in public simulated phishes over time
Reported
Minute Hour Day Week
119
119
100
99
82
81
57
The wheels of government discover slowly
When there is enough detail to derive breach timeline metrics, the data shows that breaches in the Public sector are taking months and years to be discovered. Public breaches are over 2.5 times more likely to be undiscovered for years. Espionage-related breaches typically do take longer to discover due to the lack of external fraud detection, but we did not have timeline data for those breaches. Privilege Misuse is the most common pattern within breaches that went undiscovered for months or more.
Years
Months
Days
Weeks
0% 20% 40% 60% 80% 100%
Breaches Figure 62. Time-to-discovery in Public breaches (n=32)
Things to consider:
Understand the human factor Not just from a phishing target standpoint. Errors in the forms of misdelivery and erroneous publishing of data rear their risky heads again. Insider misuse is also still a concern, so ensure eforts are taken to routinely assess user privileges. Limit the amount of damage an employee acting inappropriately or maliciously can do with existing privileges.
Lookin’ out my backdoor While not as obvious as cartwheeling giants, validate there are controls in place to look for suspicious egress trafc that could be indicative of backdoor or C2 malware installation.
The malware conundrum Large government entities with a massive community of end-points face a challenge in ensuring the breadth of up-to-date malware defenses are implemented. Smaller organizations may lack the budget for additional malware defenses other than desktop AV. Make friends with the desktop security folks and fnd out what their specifc challenges are.
58
0
Retail
Card present breaches involving POS compromises or gas-pump skimmers continue to decline. Attacks against e-commerce payment applications are satisfying the fnancial motives of the threat actors targeting this industry.
Point of Sale
2018
6%
2014
63%
DIFF
Frequency 234 incidents, 139 with confrmed data disclosure
Everything Else
6 13
-57
-7
CrimewareTop 3 patterns Web Applications, Privilege Misuse, and Miscellaneous 2 10
Errors represent 81% of Payment Card
breaches Skimmers 3 6
-7
-3
Threat actors External (81%), Internal (19%) (breaches) Web
Applications 5 63
Actor motives Financial (97%), Fun (2%), PrivilegeEspionage (2%) (breaches)
Misuse 3 10
Data compromised Payment (64%), Credentials (20%), Miscellaneous
ErrorsPersonal (16%) (breaches) 1 8
+58
+7
+7
Not such a POS anymore
Let’s jump in our DBIR time machine and travel all the way back to four years ago. It was the second year that we featured the incident classifcation patterns and the top pattern for Retail was POS Intrusion, along with remote compromise of point of sale environments, with all of the malware and payment card exfltration that comes with it. Coming back to the present year’s data set in Figure 63, the times they are a-changing.
Lost and +2 Stolen Assets
0 2
Denial of 0 Service
0
Cyber- +1 Espionage
0 1
Breaches Figure 63. Patterns in Retail breaches over time n=145 (2014), n=139 (2018)
59
Essentially, Web application attacks have punched the time clock and relieved POS Intrusion of their duties. This is not just a retail-specifc phenomenon – Figure 64 comes courtesy of our friends at the National Cyber-Forensics and Training Alliance (NCFTA) and their tracking of card-present versus card-not-present fraud independent of victim industry.
75%
50%
25%
Card-present
Card-not-present
2016 2017 2018 2019
Figure 64. Comparison of card-present vs. card-not-present fraud
Action Asset
The above shift certainly supports the reduction in POS breaches, and to a lesser extent, Payment Card Skimming. Pay at the pump terminals at gas stations would fall into the retail industry as well. We are cautiously optimistic that EMV has diminished the value proposition of card-present fraud for the cyber-criminals in our midst. Alas, it will still not make criminal elements eschew money and move to self-sustaining communes to lead simpler lives.
One door closes, kick in another one
Attacks against e-commerce web applications continue their renaissance. This is shown in Figure 64 on the left as well as Figure 26 back in the Results and Analysis section. To fnd out more about what tactics are used in attacks against payment applications we will go back to pairings of threat actions and afected assets.
The general modus operandi can be gleaned from Table 8 below. Attacker compromises a web application and installs code into the payment application that will capture customer payment card details as they complete their purchases. Some breaches had details that specifed a form-grabber which would be categorized under Spyware/Keylogger as it is another method of user input capture. Other times limited information was provided other than a statement
Count
Malware - Capture app data Server - Web application
Malware - Spyware/Keylogger Server - Web application
Hacking - Exploit vuln Server - Web application
Hacking - RFI Server - Web application
Malware - Ram scraper Server - POS controller
Malware - Ram scraper User Dev - POS terminal
Hacking - Use of stolen creds Server - Database
Hacking - Use of stolen creds Server - Mail
Hacking - Use of stolen creds Server - Web application
Misuse - Privilege abuse Server - Database
Table 8 Top action and asset variety combinations within Retail breaches, (n=139)
49
39
15
11
8
7
6
6
6
5
60
similar to “malicious code that harvested payment card data.” The more general functionality of capture app data was used in those instances. In reality there is likely little to no diference between the two pairings. We are also a little short on information on how the web application was compromised. If a specifc method like RFI is noted, we collect it. Often it may be a general notation that a web vuln was exploited, hence the Exploit Vuln variety (new to the latest version of VERIS!). Looking at what we do know and channeling our inner William of Ockham, this general chain of events is likely: scan
for specifc web application vulnerabilities > exploit and gain access > drop malware > harvest payment card data > proft. We have seen webshell backdoors involved in between the initial hack and introduction of malware in prior breaches. While that action was not recorded in signifcant numbers in this data set, it is an additional breadcrumb to look for in detection eforts. In brief, vulnerable internet-facing e-commerce applications provide an avenue for efcient, auto- mated, and scalable attacks. And there are criminal groups that specialize in these types of attacks that feast on low-hanging fruit.
Things to consider:
Integrity is integral The web application compromises are no longer attacks against data at rest. Code is being injected to capture customer data as they enter it into web forms. Widespread implementation of fle integrity software may not be a feasible undertaking. Adding this to your malware defenses on payment sites should be considered. This is, of course, in addition to patching OS, and payment application code.
Brick and Mort(ar)y Continue to embrace technologies that make it harder for criminals to turn your POS terminals into machines of unspeakable doom. EMV, mobile wallets – any method that utilizes a one-time trans- action code as opposed to PAN is a good thing.
Not just PCI Payment cards are not the only data variety that would be useful to the criminally-minded community. Rewards programs that can be leveraged for the “points” or for the personal information of your customer base are also potential targets.
61
Wrap up So, this concludes our 12th installment of this annual report. If the DBIR were a bottle of decent Scotch whiskey it would cost you around 100 bucks, instead of being free like this document. Likewise, the decisions you might make after fnishing them would probably difer wildly as well.17 Nevertheless, we hope you gain a certain degree of enjoyment and enlightenment from both.
On behalf of the team that labored to produce this document, we sincerely thank you, our readers, for your continued support and encouragement of this efort. We believe it to be of value to Information Security professionals and to industry at large, and we are grateful for the opportunity to bring it before you once again. As always, a tremendous thank you to our contributors who give of their time, efort, insight, and most importantly, their data. The task of creating this document is in no way trivial and we simply could not do it without their generosity of resources. We look forward to bringing you our 14th report (we are taking the high-rise hotel concept of enumeration here) next year, and in the meantime, may your security budgets be large and your attack surface small. Until then, feel free to refect on the more noteworthy publicly disclosed security events in 2018 from the VTRAC before jumping into the Appendices.
17We do not assert that your decisions would differ wildly as we do not have sufficient data to support that statement. It is, admittedly, a surmise on our part but internal research remains ongoing.
62
Year in review
January
On the second day of the year, the Verizon Threat Research Advisory Center (VTRAC) began to learn that researchers had discovered “Meltdown” and “Spectre,” new information disclosure vulnerabilities in most modern microprocessors. The vulnerabilities lie in foundational CPU architec- tures. Patching continued through 2018. We collected no reports of successful Meltdown or Spectre attacks in 2018. The frst week of the month included the frst report of malware attacks targeting the 2018 Winter Olympics in Pyeongchang, Republic of Korea. Investi- gative journalists reported India’s national ID database, “Aadhaar,” sufered a data breach afecting more than 1.2 billion Indian citizens. We began collecting reports of targeted attacks on Latin American banks. Attackers used disk wiping malware, probably to eliminate evidence of their actions and minimize the scale of the banks’ losses. On January 26th, we collected the frst report of GandCrab ransomware.
February
The frst “zero-day” in Adobe Flash kicked of February after APT37 embedded an exploit in Excel spread- sheets. The Punjab National Bank reported fraudulent transfers of ₹11,600 crore (USD 1.77 billion dollars). The Russian Central Bank reported “unsanctioned operations” caused the loss of ₽339 million (€4.8 million). “Olympic Destroyer” malware disrupted the opening cer- emony of the Pyeongchang Olympics but did not result in their cancellation. GitHub was hit with a new type of refection denial of service attack leveraging mis- confgured memcached servers. GitHub and other organizations endured 1.35-terabit-per-second junk trafc storms.
March
Intelligence for attacks on the Pyeongchang Olympics continued after the February 25th closing ceremonies. Operations Gold Dragon, HaoBao and Honeybee began as early as July 2017. In March, we collected intelligence on a full spectrum of APT-grade threat actors including APT28, menuPass (APT10), Patchwork, MuddyWater, OilRig, Lazarus and Cobalt. US-CERT published 15 fles with intelligence on Russian actors attacking critical infrastructure in the USA. Malaysia’s Central Bank foiled an attack that involved falsifed SWIFT wire-transfer requests. The Drupal project patched a remote code execution vulnerability reminiscent of the 2014 vulnerability that led to “Drupalgeddon.”
April
Attacks on “smart install” software in Cisco IOS switches by Russian threat actors were probably the most noteworthy InfoSec risk development in April. The VTRAC collected updated intelligence on the “Energetic Bear” Russian actor. A supply-chain attack on Latitude Technologies forced four natural-gas pipeline operators to temporarily shut down computer communications with their customers. Latitude supplies Electronic Data Interchange (EDI) services to the Energy and Oil verticals. March’s Drupal vulnerability did indeed attract cybercriminals. A variant of the Mirai IoT botnet began scanning for vulnerable Drupal servers and the subsequent compromises to install cryptomining software became known as Drupalgeddon2. The cyber-heist of US$150,000 in Ethereum from MyEtherWallet paled in signifcance to the BGP hijacking of the Internet’s infrastructure to do it.
63
May
Intelligence about the “Double Kill” zero-day vulnerability in Internet Explorer was collected at the end of April. In May, the VTRAC collected intelligence of a malicious PDF docu- ment with two more zero-day vulnerabilities, one each in Adobe PDF Reader and in Windows. Microsoft and Adobe patched all three on May’s Patch Tuesday. A surge in GandCrab ransomware infections were the focus of several of the best intelligence collections in May. New intelligence collections documented the Cobalt threat actor’s phishing campaign was targeting the fnancial sector. Multiple sources reported VPNFilter malware had infected routers and network-attached storage (NAS) appliances. Control the router—control the trafc passing through it.
June
Multiple sources released updated intelligence on North Korean threat actors engaged in cyber-confict and cybercrime operations. Adobe patched a new zero-day vulnerability in Flash. Like February’s, Flash zero-day, it was being used in malicious Excel fles but the targets were in the Middle East. Two Canadian Imperial Bank of Commerce subsidiaries – BMO (Bank of Montreal) and Simplii Financial sufered a leak of about 90,000 customer records. They learned of the breach when threat actors demanded US$750,000 for the return of the records. The Lazarus threat actor stole roughly KR ₩35 billion (around $31 million) in cryptocurrency from the South Korea-based exchange Bithumb. DanaBot, a new banking Trojan was discovered targeting Commonwealth Bank in Australia.
July
The frst major Magecart attack in 2018 was Ticketmaster’s UK branch. Hackers compromised Inbenta, a third-party functionality supplier. From Inbenta they placed digital skimmers on several Ticketmaster websites. The Ticketmaster attack was part of a campaign targeting third-party providers to perform widespread compromises of card data. July’s Magecart collections included indicators of compromise of over 800 victim websites. A malicious Mobile Device Management platform was used in highly targeted attacks on 13 iPhones and some Android and Windows platforms. Russia’s PIR Bank lost ₽58 million ($920,000) after the MoneyTaker actor compromised an outdated, unsupported Cisco router at a branch ofce and used it to pivot into the bank’s network.
August
The second Boundary Gateway Protocol (BGP) hijacking to steal cryptocurrency in 2018 redirected legitimate trafc from an Amazon DNS server. The malicious DNS server redirected users of MyEtherWallet to a spoofed site that harvested their credentials. Users of the service lost Ethereum worth about $152,000. Cosmos Bank in Pune, India, was the victim of US$13.4 million of fraudulent SWIFT and ATM transfers. The US Dept. of Justice announced the arrests of three managers from the FIN7 (Anunak, Carbanak, Carbon Spider) threat actor. Intelligence indicated a new vulnerability in Apache Struts, CVE-2018- 11776, was following the course set by March 2017’s CVE-2017-9805, the Jakarta multi-parser Struts vulnerability. The 2017 vulnerability led to the Equifax data breach. A detailed code reuse examination of malware linked to North Korea linked most malware attacks to the Lazarus Group. APT37 was linked to a small portion but was assessed to be more skilled and reserved for attacks with national strategic objectives.
64
September
New intelligence revealed Japanese corporations were being targeted by the menuPass (APT10) threat actor. On September 6th, British Airways announced it had sufered a breach resulting in the theft of cus- tomer data. Within a week, we collected intelligence British Airways had become another victim of a Magecart attack. Intelligence indicated in the preceding 6 months, 7,339 E-commerce sites had hosted Magecart payment card skimming scripts includ- ing online retailer Newegg. Weaponized IQY (Excel Web Query) attachments were discovered attempting to evade detection to deliver payloads of FlawedAmmyy remote access Trojan (RAT). The FBI and DHS issued an alert about the Remote Desktop Protocol (RDP). The alert listed several threats that exploit RDP connections: Crysis (Dharma), Crypton and SamSam ransomware families. DanaBot expanded its target set to Italy, Germany and Austria.
October
The VTRAC assessed claims that Chinese actors had compromised the technology supply chain did not constitute intelligence. The related report lacked technical details or corroboration and was based on unqualifed, unidentifed sources. US-CERT issued an updated alert on attacks on MSS providers by the menuPass (APT10) threat actor. Multiple sources reported North Korean actors engaged cybercrime attacks intended to provide revenue to the sanction-constrained regime. GreyEnergy is the latest successor to the Sandworm/BlackEnergy/ Quedagh/Telebots threat actor. GreyEnergy was linked to attacks on the energy sector and other strategic targets in Ukraine and Poland for the past three years. DanaBot began targeting fnancial services establishments in the USA. The Magecart threat actors executed a scaled supply chain attack on Shopper Approved, a customer scoring plugin used by 7000+ e-commerce sites. Detailed reports in August and October indicated the Cobalt threat actor had reorganized into a group with journeymen and apprentice members and a second group of masters reserved for more sophisticated campaigns.
November
Intelligence based on examination of Magecart malware indicated there are at least six independent threat actors conducting Magecart attacks. The initial Magecart successes in late 2016 and high-profle attacks beginning with Ticketmaster UK/Inbenta in June led to a bandwagon efect. Other threat actors copied and improved upon the TTP of early Magecart threat actor(s). The SamSam ransomware attack came to a standstill after two Iranian hackers were indicted for US$6 million extortion. Cisco released an advisory due to “active exploitation” of a vulnerability in Cisco Adaptive Security Appliance Software (ASA) and Cisco Firepower Threat Defense Software that could allow an unauthenticated, remote attacker to cause a denial of service. US-CERT released Activity Alert AA18-284A, “Publicly Available Tools Seen in Cyber Incidents Worldwide,” on fve tools threat actors had been using for their “Living of the Land” tactics. Marriott announced a 2014-18 breach had exposed the records of up to 500 million customers in its Starwood hotels reservation system.
December
VTRAC collections in December began with “Operation Poison Needles.” An unidentifed actor exploited the third Adobe Flash zero-day vulnerability to attack Polyclinic of the Presidential Administration of Russia. “Operation Sharpshooter” was a global campaign targeting nuclear, defense, energy and fnancial companies. Oil and gas services contractor Saipem sufered an attack that employed a new variant of Shamoon disk-wiping malware. December’s Patch Tuesday fxed CVE-2018-8611, the latest Windows zero-day being exploited by the FruityArmor APT threat actor. Partly in reaction to the 77 percent plunge in Bitcoin, cyber- criminals did not abandon cryptomining altogether, instead, SamSam and GandCrab ransomware were being used to attack corporations, government agencies, universities and other large organizations. Criminals targeted larger purses: organizations likely to pay ransom in lieu of days of lost business and productivity recovering from backups, re-imaging or other BCP/DR measures. At the end of 2018 the VTRAC was running like a Formula 1 car fnishing a mid-race lap: at full speed, staying ahead of some, striving to catch others and constantly improving our engineering.
65
Appendix A: Transnational hacker debriefs
Insights into their target selection and tactics, techniques and procedures – Michael D’Ambrosio, Deputy Assistant Director, United States Secret Service
Over the past ffteen years, the United States Secret Service has successfully identifed, located, and arrested numerous high-value cybercriminals. These individuals were responsible for some of the most signifcant and widely publicized data breaches of public and private industry networks. Over this period, the Secret Service’s Cyber Division has cultivated mutually benefcial partnerships with law enforcement agencies around the globe, which has extended the reach of the Secret Service’s investigative eforts far beyond its traditional limits. This network of collaborative partners has enabled the Secret Service to successfully extradite criminal suspects located overseas and have them face prosecution in the United States. The Secret Service continues to forge new international partnerships in furtherance of its mission to pursue and apprehend cybercriminals regardless of their geography.
As part of its mandate to combat fnancially motivated cybercrime, the Secret Service combines its investigative eforts with educational outreach programs. These are aimed at strengthening the ability of private and public sector entities to protect themselves against a range of cybercrimes. The Secret Service conducts in-depth analyses of the activities, tools, and methodologies used by the cybercriminals during the commission of their crimes to better assess the evolving threats that cybercrimi- nals pose to fnancial institutions and other potential targets. The Secret Service then shares the results of these reviews with its network of public and private partners through its outreach programs.
The Secret Service’s Cyber Division has learned that the most prescient information about cybercrime trends often comes from the cybercriminals themselves. The Secret Service conducts extensive debriefngs of arrested cybercriminals and uses their frst-hand knowledge to understand more fully the spectrum of variables they used to identify and select a particular target for intrusion and exploitation. The Secret Service has recently completed such debriefngs with a handful of highly skilled cybercriminals who were responsible for some of the most signifcant network intrusions in history, and has found that the ways in which these individuals select their targets and perpetrate their crimes share certain common features.
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Cybercriminals prey upon human error, IT security complacency, and technical defciencies present in computer networks all over the world. Individually, each of these tactics, techniques and procedures (TTPs) discussed below are not always initially successful and may seem easily mitigated; it is when multiple TTPs are utilized in concert that cybercriminals are able to gain and maintain access to a computer network, no matter their motives. Once they are inside a network their process is almost always the same: establish continued access, escalate or obtain administrator privileges, move slowly and quietly to map the entire network, look for open ports, locate the “crown jewels,” and exfltrate the data undetected for as long as possible.
The selection of a target is a continual process. Cybercriminals do their research. Almost always during these interviews, the hackers referred to gathering valuable intelligence from the same cybersecurity blogs, online IT security publications, and vulnerability reports that network administrators should be monitoring. They know that once a vulnerability is revealed, they still have a limited amount of time to try to exploit that vulnerability at a potential victim organization. Every time a vulnerability is disclosed or a system update or patch is released, a hacker sees an opportunity. They research the disclosure or update notes to learn if they can exploit the vulner- ability and where, searching for their best opportunity to monetize the vulnerability. Hackers also communicate vulnerability information and exploit techniques on hacking forums. Once a target is selected, the hacker conducts thorough research into the victim organization and their network(s), often using free and commercially available Internet scanning tools that reveal extremely useful information about the victim company’s network.
Webserver and/or webpage hacking has been a highly successful primary attack vector, as there are various potential avenues for exploitation. These include the main website of an institution or a less protected linked website, which in turn can provide access to the main network. The added use of Structured Query Language (SQL) database injections of malicious code has been a very efective attack vector because these types of intrusion techniques can be deployed at any access point of a website. There are additional webserver attack vectors such as overlooked or forgotten IP addresses, possibly from development or beta-testing and external webservers or data servers that share the same or common domain. Unmanaged servers that still utilize Unicode can be exploited via encoding the URL with certain characters to bypass application flters.
Other traditional and efective attack vectors should not be overlooked. These include spear phishing for login credentials or malware delivery and “Man in the Middle” attacks through poorly secured routers or web gateways. Botnets are a relatively inexpensive tool that have been used to degrade or brute force attack networks in connection with parallel tactics. A very skilled hacker admitted to the Secret Service that he ended up paying a collusive employee (insider threat) when all of his other hacking attempts to access a foreign bank’s network were unsuccessful.
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Once inside a network, cybercriminals continue to do their research and reconnaissance. Hackers often examine a webserver’s default error pages because those pages expose a lot of the target network’s system information. Cybercriminals take all of network information they can collect and utilize virtual machines (VMs) to build a mock system to emulate the network of the victim company. This is done both for testing their methods of exploitation and for better understanding the types of network defenses present within the system.
The exploits used by cybercriminals inside a target network depend on the installed network defenses. Undoubtedly, the hacker will try to install a web shell to ensure access into the system. Another sustainment method is the use of cross-site scripting (XSS) for session hijacking (cookie stealing) of a valid user through malicious code injections into a user’s JavaScript, ActiveX, Flash, or other code bank. The use of malware delivered to the valid user via spear phishing is a key component of this process.
In addition, hackers utilize directory transversal attacks (directory climbing, back- tracking, etc.) on web servers to attempt to reach otherwise restricted directories, such as Secure Socket Layer (SSL) private keys and password fles. Hackers can even execute commands on the server by accessing such directories. After administrator privileges are obtained, it is common for the prized data to be exfltrated by tunneling via a remote access protocol. Cybercriminals will also scan for open ports and attempt to install software of their choosing on non-standard ports for a variety of malicious uses. If the targeted network has the potential to provide valuable data continuously, diligent hackers will continuously clean up their “tracks” within the exploited network to obfuscate their presence indefnitely. Another prominent hacker described having persistent access into a company’s networks for 10 years using multiple “backdoors” (web shells) and continually cleaning up his “work” to go undetected. In reality, many of the hackers we debriefed often stated that they could see traces of other hackers in the targeted network which sometimes made it harder to hide their hacking exploits.
These are just some of the tactics, techniques and procedures the Secret Service has observed used by criminal groups to exploit victim networks. The threat is real and the adversary is constantly evolving, driven by diverse and varying motivations. Their success is more often dependent on how well network administrators can adapt their defenses to potential vulnerabilities as they are revealed.
The Secret Service will continue to pursue, arrest, and prosecute cybercriminals no matter where they are and we will continue to provide valuable attack methodology analysis from our investigations to better improve the cybersecurity eforts of our partners in law enforcement, academia, and the public and private sectors alike.
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Appendix B: Methodology One of the things readers value most about this report is the level of rigor and integrity we employ when collecting, analyzing, and presenting data. Knowing our readership cares about such things and consumes this information with a keen eye helps keep us honest. Detailing our methods is an important part of that honesty.
Our overall methodology remains intact and largely unchanged from previous years. All incidents included in this report were individually reviewed and converted (if necessary) into the VERIS framework to create a common, anonymous aggregate data set. If you are unfamiliar with the VERIS framework, it is short for Vocabulary for Event Recording and Incident Sharing, it is free to use, and links to VERIS resources are at the beginning of this report.
The collection method and conversion techniques difered between contributors. In general, three basic methods (expounded below) were used to accomplish this:
1. Direct recording of paid external forensic investigations and related intelligence operations conducted by Verizon using the VERIS Webapp.
2. Direct recording by partners using VERIS.
3. Converting partners existing schema into VERIS.
All contributors received instruction to omit any information that might identify organizations or individuals involved.
Reviewed spreadsheets and VERIS Webapp JavaScript Object Notation (JSON) are ingested by an automated workfow that converts the incidents and breaches within into the VERIS JSON format as necessary, adds missing enumerations, and then validates the record against business logic and the VERIS schema. The automated workfow subsets the data and analyzes the results. Based on the results of this exploratory analysis, the validation logs from the workfow, and discussions with the partners providing the data, the data is cleaned and re-analyzed. This process runs nightly for roughly three months as data is collected and analyzed.
Incident eligibility
For a potential entry to be eligible for the incident/breach corpus, a couple of requirements must be met. The entry must be a confrmed security incident, defned as a loss of confdentiality, integrity, or availability. In addition to meeting the baseline defnition of “security incident” the entry is assessed for quality. We create a subset of incidents (more on subsets later) that pass our quality flter. The details of what is a “quality” incident are:
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• The incident must have at least seven enumerations (e.g., threat actor variety, threat action category, variety of integrity loss, et al.) across 34 felds OR be a DDoS attack. Exceptions are given to confrmed data breaches with less than seven enumerations.
• The incident must have at least one known VERIS threat action category (hacking, malware, etc.)
In addition to having the level of detail necessary to pass the quality flter, the incident must be within the timeframe of analysis, (November 1, 2017 to October 31, 2018 for this report). The 2018 caseload is the primary analytical focus of the report, but the entire range of data is referenced throughout, notably in trending graphs. We also exclude incidents and breaches afecting individuals that cannot be tied to an organizational attribute loss. If your friend’s personal laptop was hit with CryptoLocker it would not be included in this report.
Lastly, for something to be eligible for inclusion into the DBIR, we have to know about it, which brings us to sample bias.
Acknowledgement of sample bias
We would like to reiterate that we make no claim that the fndings of this report are representative of all data breaches in all organizations at all times. Even though the combined records from all our contributors more closely refect reality than any of them in isolation, it is still a sample. And although we believe many of the fndings presented in this report to be appropriate for generalization (and our confdence in this grows as we gather more data and compare it to that of others), bias undoubtedly exists. Unfortunately, we cannot measure exactly how much bias exists (i.e., in order to give a precise margin of error). We have no way of knowing what proportion of all data breaches are represented because we have no way of knowing the total number of data breaches across all organizations in 2018. Many breaches go unreported (though our sample does contain many of those). Many more are as yet unknown by the victim (and thereby unknown to us).
While we believe many of the fndings presented in this report to be appropriate, generalization, bias, and methodological faws undoubtedly exist. However, with 73 contributing organizations this year, we’re aggregating across the diferent collection methods, priorities, and goals of our partners. We hope this aggregation will help minimize the infuence of any individual shortcomings in each of the samples, and the whole of this research will be greater than the sum of its parts.
Statistical analysis
We strive for statistical correctness in the DBIR. In this year’s data sample, the confdence interval is at least +/- 2% for breaches and +/- 0.5%18 for incidents. Smaller samples of the data (such as breaches within the Espionage pattern) will be even wider as the size is smaller. We have tried to treat every statement within the DBIR as a hypothesis19 based on exploratory analysis and ensure that each statement is accurate at a given confdence level (normally 95%). We’ve tried to express this confdence in the conditional probability bar charts explained in the “tidbits” that precede the Table of Contents.
18Bayes method, 95% confidence level. 19If you wonder why we treat them as hypotheses rather than findings, to confirm or deny our hypothesis would requires a second, unique data set we had not inspected ahead of time.
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Our data is non-exclusively multinomial meaning a single feature, such as “Action,” can have multiple values (i.e., “social,” “malware,” and “hacking”). This means that percentages do not necessarily add up to 100 percent. For example, if there are 5 botnet breaches, the sample size is 5. However, since each botnet used phishing, installed keyloggers, and used stolen credentials, there would be 5 social actions, 5 hacking actions, and 5 malware actions, adding up to 300 percent. This is normal, expected, and handled correctly in our analysis and tooling.
Another important point is that, when looking at the fndings, “unknown” is equivalent to “unmeasured.” Which is to say that if a record (or collection of records) contain ele- ments that have been marked as “unknown” (whether it is something as basic as the number of records involved in the incident, or as complex as what specifc capabilities a piece of malware contained) it means that we cannot make statements about that particular element as it stands in the record—we cannot measure where we have too little information. Because they are “unmeasured,” they are not counted in sample sizes. The enumeration “Other” is, however, counted as it means the value was known but not part of VERIS. Finally, “Not Applicable” (normally “NA”) may be counted or not counted depending on the hypothesis.
Data Subsets
We already mentioned the subset of incidents that passed our quality requirements, but as part of our analysis there are other instances where we defne subsets of data. These subsets consist of legitimate incidents that would eclipse smaller trends if left in. These are removed and analyzed separately (as called out in the relevant sections). This year we have two subsets of legitimate incidents that are not analyzed as part of the overall corpus:
1. We separately analyzed a subset of web servers that were identifed as secondary targets (such as taking over a website to spread malware).
2. We separately analyze botnet-related incidents.
Both subsets were separately analyzed last year as well.
Finally, we create some subsets to help further our analysis. In particular, a single subset is used for all analysis within the DBIR unless otherwise stated. It includes only quality incidents as described above and the aforementioned two subsets.
Non-incident data
Since 2015, the DBIR includes data that requires the analysis that did not ft into our usual categories of “incident” or “breach.” Examples of non-incident data include malware, patching, phishing, DDoS, and other types of data. The sample sizes for non-incident data tend to be much larger than the incident data, but from fewer sources. We make every efort to normalize the data, (for example reporting on the median organization rather than the average of all data). We also attempt to combine multiple contributors with similar data to conduct the analysis wherever possible. Once analysis is complete, we try to discuss our findings with the relevant contributor or contributors so as to validate it against their knowledge of the data.
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Appendix C: Watching the watchers Last year in the “Feeling vulnerable?” appendix, we discussed the services or weaknesses attackers look for in spray and pray internet scans, and how those aren’t necessarily the same things they look for in targeted attacks. In this section, we again examine what services are open to the internet and the adversary activity against them. At the risk of stating the obvious, what the attacker looks for tells you a great deal about what is of value to them.
Any port in a storm
Ports that ofer at least some value to, and at the same time require the least amount of investment from the attacker garner a lot of attention. An economist might call the amount invested by the actor per attack the marginal cost. The very best attacks from the criminal’s point of view would cost almost nothing per target. We will refer to these as zero-marginal-cost attacks.
P or
t r an
k in
h on
ey po
t e ve
nt s
1
10 5060
20
30
40
50
50 40 30 20 10 1
21
2223
25
53 80
123
137
161
389
443
445
500
1900
3389
8000
8080
11211
Port rank in DDoS attacks Figure 65. Comparison of ports in DDoS and honeypot attacks
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P or
t
Figure 65 illustrates the ports that are in the top 50 for both honeypot activity and DDoS attacks (with “1” in the upper right being the most common and the rest decreasing from that point). We can consider how often attackers look for a given port as an indicator for how valuable they are to the attacker. Ports below the red line, such as cLDAP (389), DNS (53), and NTP (123) are more valuable due to their DDoS ampli- fcation potential. The ports above the red line are more valuable for their non-DDoS malevolence including SSH (22), telnet (23), HTTP (8080), NetBIOS (445), and others.
Portémon Go
Probably the most efective way to judge perceived value for the attacker for a given port in zero-marginal-cost attacks is to examine their ranking in honeypot scans vs their general population ranking on the Internet. There are a myriad of organizations that scan the internet regularly, and there are a few of those who are gracious enough to contribute to the DBIR. As a result, we can share this data in Figure 66.
telnet (23)
Dell Open Management (1311)
NetBIOS (445)
Various trojans (6969)
Netis Routers (53413)
MS SQL Server (1433)
memcached (11211)
Building Managment
(47808)
cLDAP (389)
Various (8888)
0x 5x 10x 15x
Ratio of honeypot events rank to internet scans rank
Figure 66. Ports scanned for more often than they exist
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Figure 66 lists the top 10 ports by ratio of honeypot activity to internet prevelance.20
Some of these, for example, Telnet, NetBIOS, and SQL Server – legacy services with known weaknesses that are old enough to vote – may not be as common as dirt, but they still exist and when an attacker fnds them you can almost hear the intro to Pink Floyd’s “Money” foating in the ether. If your organization has any of these services exposed to the internet, it’s probably a good idea to go and take care of that now. We’ll wait here. Take your time. This report changes once a year, but those ports are being hammered daily.
Dime a dozen
The above section begs the question, “If those ports are what attackers frequently search for but rarely fnd, which open ports are plentiful but rarely sought?” We are glad you asked. For the most part they are unassigned or ephemeral ports. Of more interest are the ports that appear in vulnerability scans, but do not show up in honey- pots. Figure 67 gives us some insight into that area. The main takeaway is that there are a lot of ports far down on the list from a honeypot perspective (the big cluster in the lower left of the fgure) that get reported often in vulnerability scans. Those are the vulnerabilities that may be useful for attackers but either only for niche attacks or internal pivoting, or are of absolutely no interest whatsoever to the attacker.
20For example, if a port was the top ranked port in honeypot scans and the 15th most common on the internet, its ratio would be 15x.
74
443
3389
445
1433
80
22
0
123
2381
8443 161
500
636
5989
8080
8081
23
25
3269
8089
8880
1311
21
9443
5061
81
53
6701
8082
389
143
3071
587
10000
9043
623
993
5556
995
5000
7002
110
8444
9090
8445
5480
8000
1521
5353
7004
10
100
1,000
10,000
H on
ey po
t e ve
nt s
po rt
ra nk
(l og
s ca
le )
100 10
Vulnerability scans port rank (log scale) Figure 67. Comparison of ports in vulnerability scans and honeypot events
Take action
There may only be seven seas, but there are 65,535 ports. While not all are found in the fgures above, a great many are. So now what? We suggest you take a look to ascertain if you are vulnerable to any zero-marginal-cost attacks (easily identifed by their honeypot to internet scan ratio). If so, you are operating below a critical security threshold and you need to take action to get above it. Are you running a honeypot yourself? If not, why is that port open? Finally, take a cue from the Unbroken Chains section and be smart about what else you mitigate. Understand the paths attackers are most likely to take in order to exploit those services.
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Appendix D: Contributing organizations
CYBER+INFRASTRUCTURE
76
Security Awareness Training
77
C
A
Akamai Technologies Apura Cyber Intelligence AttackIQ Avant Research Group, LLC
B
BeyondTrust BinaryEdge BitSight Bit-x-bit
Center for Internet Security CERT Insider Threat Center CERT European Union Checkpoint Software
Technologies Ltd Chubb Cisco Security Services Computer Incident Response
Center Luxembourg (CIRCL) CrowdStrike Cybercrime Central Unit of
the Guardia Civil (Spain) CyberSecurity Malaysia,
an agency under the Ministry of Science, Technology and Innovation (MOSTI)
Cylance
D
Dell DFDR Forensics Digital Edge Digital Shadows Dragos, Inc
E
Edgescan Emergence Insurance
F
Federal Bureau of Investigations Internet Crime Complaint Center (FBI IC3)
Fortinet
G
Gillware Digital Forensics Government of Telangana,
ITE&C Dept., Secretariat GRA Quantum GreyNoise Intelligence
I
Interset Irish Reporting and
Information Security Services (IRISS-CERT)
J
JPCERT/CC
K
Kaspersky Lab KnowBe4
L
Lares Consulting LIFARS
M
Malicious Streams McAfee Mishcon de Reya Moss Adams (formerly
ASTECH consulting) MWR InfoSecurity
N
National Cyber-Forensics and Training Alliance (NCFTA)
NetDiligence NETSCOUT
P
Paladion Palo Alto Networks Proofpoint
Q
Qualys
R
Rapid7 Recorded Future
S
S21sec Shodan Social-Engineer, Inc. SwissCom
T
Tripwire
U
US Secret Service US Computer Emergency
Readiness Team (US-CERT)
V
VERIS Community Database Verizon Cyber Risk Programs Verizon Digital Media Services Verizon DOS Defense Verizon Managed Security
Services Verizon Network Operations
and Engineering Verizon Professional Services Verizon Threat Research
Advisory Center Vestige Ltd
W
Wandera West Monroe Partners Winston & Strawn LLP
Z
Zscaler
2019 Data Breach Investigations Report
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- Delivery MethodFile TypeAccommodation (72)Education (61)Finance (52)Healthcare (62)Information (51)Manufacturing (31-33)Professional (54)Public (92)Retail and Wholesale0%25%50%75%100%Figure 43. Malware types and delivery methods by industryemailwebotherO–ce docWindows appother95.2%95.6%96.9%98.0%97.7%91.1%96.8%61.4%94.8%17.1%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%15.1%14.4%9.0%10.5%21.0%10.3%86.1%16.7%41.5%64.4%51.2%37.6%49.7%67.1%74.5%26.6%56.2%28.2%21.2%16.1%18.4%25.7%20.7%17.4%12.6%34.2%19.0%24.9%25.0%33.3%
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