Assignment

JB12345

CH08-CompSec4e.pptx

Home >Information Systems homework help >Assignment

Computer Security:

Principles and Practice

Fourth Edition

By: William Stallings and Lawrie Brown

Lecture slides prepared for “Computer Security: Principles and Practice”, 4/e, by William Stallings and Lawrie Brown, Chapter 8 “Intrusion Detection”.

Chapter 8

Intrusion Detection

A significant security problem for networked systems is hostile, or at least

unwanted, trespass by users or software. User trespass can take the form of unauthorized

logon or other access to a machine or, in the case of an authorized user,

acquisition of privileges or performance of actions beyond those that have been

authorized. Software trespass includes a range of malware variants as we discuss

in Chapter 6.

This chapter covers the subject of intrusions. First, we examine the nature

of intruders and how they attack, then look at strategies for detecting intrusions.

Classes of Intruders – Cyber Criminals

Individuals or members of an organized crime group with a goal of financial reward

Their activities may include:

Identity theft

Theft of financial credentials

Corporate espionage

Data theft

Data ransoming

Typically they are young, often Eastern European, Russian, or southeast Asian hackers, who do business on the Web

They meet in underground forums to trade tips and data and coordinate attacks

One of the key threats to security is the use of some form of hacking by an intruder,

often referred to as a hacker or cracker. Verizon [VERI16] indicates that 92% of

the breaches they investigated were by outsiders, with 14% by insiders, and with

some breaches involving both outsiders and insiders. They also noted that insiders

were responsible for a small number of very large dataset compromises. Both

Symantec [SYMA16] and Verizon [VERI16] also comment that not only is there a

general increase in malicious hacking activity, but also an increase in attacks specifically

targeted at individuals in organizations and the IT systems they use. This trend

emphasizes the need to use defense-in-depth strategies, since such targeted attacks

may be designed to bypass perimeter defenses such as firewalls and network-based

Intrusion detection systems (IDSs).

As with any defense strategy, an understanding of possible motivations of the

attackers can assist in designing a suitable defensive strategy. Again, both Symantec

[SYMA16] and Verizon [VERI16] comment on the following broad classes of intruders:

• Cyber criminals: Are either individuals or members of an organized crime

group with a goal of financial reward. To achieve this, their activities may

include identity theft, theft of financial credentials, corporate espionage, data

theft, or data ransoming. Typically, they are young, often Eastern European,

Russian, or southeast Asian hackers, who do business on the Web [ANTE06].

They meet in underground forums with names like DarkMarket.org and

theftservices.com to trade tips and data and coordinate attacks. For some

years reports such as [SYMA16] have quoted very large and increasing costs

resulting from cyber-crime activities, and hence the need to take steps to mitigate

this threat.

Classes of Intruders – Activists

Are either individuals, usually working as insiders, or members of a larger group of outsider attackers, who are motivated by social or political causes

Also know as hacktivists

Skill level is often quite low

Aim of their attacks is often to promote and publicize their cause typically through:

Website defacement

Denial of service attacks

Theft and distribution of data that results in negative publicity or compromise of their targets

• Activists: Are either individuals, usually working as insiders, or members of

a larger group of outsider attackers, who are motivated by social or political

causes. They are also known as hacktivists, and their skill level is often quite

low. The aim of their attacks is often to promote and publicize their cause,

typically through website defacement, denial of service attacks, or the theft

and distribution of data that results in negative publicity or compromise of

their targets. Well-known recent examples include the activities of the groups

Anonymous and LulzSec, and the actions of Chelsea (born Bradley)

Manning and Edward Snowden.

Classes of Intruders – State-Sponsored Organizations

• State-sponsored organizations: Are groups of hackers sponsored by governments

to conduct espionage or sabotage activities. They are also known as

Advanced Persistent Threats (APTs), due to the covert nature and persistence

over extended periods involved with many attacks in this class. Recent

reports such as [MAND13], and information revealed by Edward Snowden,

indicate the widespread nature and scope of these activities by a wide range of

countries from China to the USA, UK, and their intelligence allies.

Groups of hackers sponsored by governments to conduct espionage or sabotage activities

Also known as Advanced Persistent Threats (APTs) due to the covert nature and persistence over extended periods involved with any attacks in this class

Widespread nature and scope of these activities by a wide range of countries from China to the USA, UK, and their intelligence allies

Classes of Intruders – Others

Hackers with motivations other than those previously listed

Include classic hackers or crackers who are motivated by technical challenge or by peer-group esteem and reputation

Many of those responsible for discovering new categories of buffer overflow vulnerabilities could be regarded as members of this class

Given the wide availability of attack toolkits, there is a pool of “hobby hackers” using them to explore system and network security

• Others: Are hackers with motivations other than those listed above, including

classic hackers or crackers who are motivated by technical challenge or

by peer-group esteem and reputation. Many of those responsible for discovering

new categories of buffer overflow vulnerabilities [MEER10] could be

regarded as members of this class. Also, given the wide availability of attack

toolkits, there is a pool of “hobby hackers” using them to explore system and

network security, who could potentially become recruits for the above classes.

Intruder Skill Levels – Apprentice

Hackers with minimal technical skill who primarily use existing attack toolkits

They likely comprise the largest number of attackers, including many criminal and activist attackers

Given their use of existing known tools, these attackers are the easiest to defend against

Also known as “script-kiddies” due to their use of existing scripts (tools)

Across these classes of intruders, there is also a range of skill levels seen.

These can be broadly classified as:

• Apprentice: Hackers with minimal technical skill who primarily use existing

attack toolkits. They likely comprise the largest number of attackers, including

many criminal and activist attackers. Given their use of existing known

tools, these attackers are the easiest to defend against. They are also known as

“script-kiddies” due to their use of existing scripts (tools).

Intruder Skill Levels – Journeyman

Hackers with sufficient technical skills to modify and extend attack toolkits to use newly discovered, or purchased, vulnerabilities

They may be able to locate new vulnerabilities to exploit that are similar to some already known

Hackers with such skills are likely found in all intruder classes

Adapt tools for use by others

• Journeyman: Hackers with sufficient technical skills to modify and extend

attack toolkits to use newly discovered, or purchased, vulnerabilities; or to

focus on different target groups. They may also be able to locate new vulnerabilities

to exploit that are similar to some already known. A number of

hackers with such skills are likely found in all intruder classes listed above,

adapting tools for use by others. The changes in attack tools make identifying

and defending against such attacks harder.

Intruder Skill Levels – Master

Hackers with high-level technical skills capable of discovering brand new categories of vulnerabilities

Write new powerful attack toolkits

Some of the better known classical hackers are of this level

Some are employed by state-sponsored organizations

Defending against these attacks is of the highest difficulty

Hackers with high-level technical skills capable of discovering brand

new categories of vulnerabilities, or writing new powerful attack toolkits.

Some of the better-known classical hackers are of this level, as clearly are

some of those employed by some state-sponsored organizations, as the designation

APT suggests. This makes defending against these attacks of the highest

difficulty.

Examples of Intrusion

Remote root compromise

Web server defacement

Guessing/cracking passwords

Copying databases containing credit card numbers

Viewing sensitive data without authorization

Running a packet sniffer

Distributing pirated software

Using an unsecured modem to access internal network

Impersonating an executive to get information

Using an unattended workstation

Intruder attacks range from the benign to the serious. At the benign end of

the scale, there are people who simply wish to explore internets and see what is out

there. At the serious end are individuals or groups that attempt to read privileged

data, perform unauthorized modifications to data, or disrupt systems.

NIST SP 800-61 (Computer Security Incident Handling Guide , August 2012)

lists the following examples of intrusion:

• Performing a remote root compromise of an e-mail server

• Defacing a Web server

• Guessing and cracking passwords

• Copying a database containing credit card numbers

• Viewing sensitive data, including payroll records and medical information,

without authorization

• Running a packet sniffer on a workstation to capture usernames and passwords

• Using a permission error on an anonymous FTP server to distribute pirated

software and music files

• Dialing into an unsecured modem and gaining internal network access

• Posing as an executive, calling the help desk, resetting the executive’s e-mail

password, and learning the new password

• Using an unattended, logged-in workstation without permission

Intrusion detection systems (IDSs) and intrusion prevention systems (IPSs),

of the type described in this chapter and Chapter 9, respectively, are designed to aid

countering these types of threats. They can be reasonably effective against known,

less sophisticated attacks, such as those by activist groups or large-scale email

scams. They are likely less effective against the more sophisticated, targeted attacks

by some criminal or state-sponsored intruders, since these attackers are more likely

to use new, zero-day exploits, and to better obscure their activities on the targeted

system. Hence they need to be part of a defense-in-depth strategy that may also

include encryption of sensitive information, detailed audit trails, strong authentication

and authorization controls, and active management of operating system and

application security.

Intruder Behavior

The techniques and behavior patterns of intruders are constantly shifting, to exploit

newly discovered weaknesses and to evade detection and countermeasures. However,

intruders typically use steps from a common attack methodology. [VERI16] in their

“Wrap up” section illustrate a typical sequence of actions, starting with a phishing attack

that results in the installation of malware that steals login credentials that eventually

result in the compromise of a Point-of-Sale terminal. They note that while this is one specific

incident scenario, the components are commonly seen in many attacks.

[MCCL12] discuss in detail activities associated with the following steps:

• Target Acquisition and Information Gathering: Where the attacker identifies

and characterizes the target systems using publicly available information, both

technical and non-technical, and the use network exploration tools to map target

resources.

Initial Access: The initial access to a target system, typically by exploiting a

remote network vulnerability as we discuss in Chapters 10 and 11, by guessing

weak authentication credentials used in a remote service as we discussed in

Chapter 3, or via the installation of malware on the system using some form of

social engineering or drive-by-download attack as we discuss in Chapter 6.

• Privilege Escalation: Actions taken on the system, typically via a local access

vulnerability as discussed in Chapters 10 and 11, to increase the privileges

available to the attacker to enable their desired goals on the target system.

• Information Gathering or System Exploit: Actions by the attacker to access

or modify information or resources on the system, or to navigate to another

target system.

• Maintaining Access: Actions such as the installation of backdoors or other

malicious software as we discuss in Chapter 6, or through the addition of covert

authentication credentials or other configuration changes to the system, to

enable continued access by the attacker after the initial attack.

• Covering Tracks: Where the attacker disables or edits audit logs such as we discuss

in Chapter 18, to remove evidence of attack activity, and uses rootkits and

other measures to hide covertly installed files or code as we discuss in Chapter 6.

Target acquisition and information gathering

Initial access

Privilege escalation

Information gathering or system exploit

Maintaining access

Covering tracks

Table 8.1

Examples of

Intruder Behavior

(Table can be found on pages 255-256 in the textbook.)

Table 8.1 lists examples of activities associated with the above steps.

Security Intrusion:

Unauthorized act of bypassing the security mechanisms of a system

Intrusion Detection:

A hardware or software function that gathers and analyzes information from various areas within a computer or a network to identify possible security intrusions

Definitions

security intrusion: Unauthorized act of bypassing the security mechanisms of a

system.

intrusion detection: A hardware or software function that gathers and analyzes

information from various areas within a computer or a network to identify possible

security intrusions.

Intrusion Detection System (IDS)

Host-based IDS (HIDS)

Monitors the characteristics of a single host for suspicious activity

Network-based IDS (NIDS)

Monitors network traffic and analyzes network, transport, and application protocols to identify suspicious activity

Distributed or hybrid IDS

Combines information from a number of sensors, often both host and network based, in a central analyzer that is able to better identify and respond to intrusion activity

An IDS comprises three logical components:

• Sensors : Sensors are responsible for collecting data. The input for a sensor

may be any part of a system that could contain evidence of an intrusion. Types

of input to a sensor includes network packets, log files, and system call traces.

Sensors collect and forward this information to the analyzer.

• Analyzers : Analyzers receive input from one or more sensors or from other

analyzers. The analyzer is responsible for determining if an intrusion has

occurred. The output of this component is an indication that an intrusion has

occurred. The output may include evidence supporting the conclusion that an

intrusion occurred. The analyzer may provide guidance about what actions

to take as a result of the intrusion. The sensor inputs may also be stored for

future analysis and review in a storage or database component.

• User interface: The user interface to an IDS enables a user to view output

from the system or control the behavior of the system. In some systems, the

user interface may equate to a manager, director, or console component.

An IDS may use a single sensor and analyzer, such as a classic HIDS on a host

or NIDS in a firewall device. More sophisticated IDSs can use multiple sensors,

across a range of host and network devices, sending information to a centralized

analyzer and user interface in a distributed architecture.

IDSs are often classified based on the source and type of data analyzed, as:

• Host-based IDS (HIDS): Monitors the characteristics of a single host and the

events occurring within that host, such as process identifiers and the system

calls they make, for evidence of suspicious activity.

• Network-based IDS (NIDS): Monitors network traffic for particular network

segments or devices and analyzes network, transport, and application protocols

to identify suspicious activity.

• Distributed or hybrid IDS: Combines information from a number of sensors,

often both host and network-based, in a central analyzer that is able to better

identify and respond to intrusion activity.

Comprises three logical components:

Sensors - collect data

Analyzers - determine if intrusion has occurred

User interface - view output or control system behavior

Authentication facilities, access control facilities, and firewalls all play a role in

countering intrusions. Another line of defense is intrusion detection, and this has

been the focus of much research in recent years. This interest is motivated by a

number of considerations, including the following:

1. If an intrusion is detected quickly enough, the intruder can be identified and

ejected from the system before any damage is done or any data are compromised.

Even if the detection is not sufficiently timely to preempt the intruder,

the sooner that the intrusion is detected, the less the amount of damage and

the more quickly that recovery can be achieved.

2. An effective IDS can serve as a deterrent, thus acting to prevent intrusions.

3. Intrusion detection enables the collection of information about intrusion techniques

that can be used to strengthen intrusion prevention measures.

Intrusion detection is based on the assumption that the behavior of the intruder

differs from that of a legitimate user in ways that can be quantified. Of course, we

cannot expect that there will be a crisp, exact distinction between an attack by an

intruder and the normal use of resources by an authorized user. Rather, we must

expect that there will be some overlap.

Figure 8.1 suggests, in abstract terms, the nature of the task confronting the

designer of an IDS. Although the typical behavior of an intruder differs from the

typical behavior of an authorized user, there is an overlap in these behaviors. Thus,

a loose interpretation of intruder behavior, which will catch more intruders, will

also lead to a number of false positives , or false alarms, where authorized users are

identified as intruders. On the other hand, an attempt to limit false positives by a

tight interpretation of intruder behavior will lead to an increase in false negatives ,

or intruders not identified as intruders. Thus, there is an element of compromise

and art in the practice of intrusion detection. Ideally you want an IDS to have a high

detection rate, that is, the ratio of detected to total attacks, while minimizing the

false alarm rate, the ratio of incorrectly classified to total normal usage [LAZA05].

In an important early study of intrusion [ANDE80], Anderson postulated

that one could, with reasonable confidence, distinguish between an outside attacker

and a legitimate user. Patterns of legitimate user behavior can be established by

observing past history, and significant deviation from such patterns can be detected.

Anderson suggests that the task of detecting an inside attacker (a legitimate user

acting in an unauthorized fashion) is more difficult, in that the distinction between

abnormal and normal behavior may be small. Anderson concluded that such violations

would be undetectable solely through the search for anomalous behavior.

However, insider behavior might nevertheless be detectable by intelligent definition

of the class of conditions that suggest unauthorized use. These observations,

which were made in 1980, remain true today.

IDS Requirements

To be of practical use, an IDS should detect a substantial percentage of intrusions

while keeping the false alarm rate at an acceptable level. If only a modest

percentage of actual intrusions are detected, the system provides a false sense of

security. On the other hand, if the system frequently triggers an alert when there

is no intrusion (a false alarm), then either system managers will begin to ignore the

alarms, or much time will be wasted analyzing the false alarms.

Unfortunately, because of the nature of the probabilities involved, it is very

difficult to meet the standard of high rate of detections with a low rate of false

alarms. In general, if the actual numbers of intrusions is low compared to the number

of legitimate uses of a system, then the false alarm rate will be high unless the

test is extremely discriminating. This is an example of a phenomenon known as

the base-rate fallacy . A study of existing IDSs, reported in [AXEL00], indicated

that current systems have not overcome the problem of the base-rate fallacy. See

Appendix I for a brief background on the mathematics of this problem.

[BALA98] lists the following as desirable for an IDS. It must

• Run continually with minimal human supervision.

• Be fault tolerant in the sense that it must be able to recover from system

crashes and reinitializations.

• Resist subversion. The IDS must be able to monitor itself and detect if it has

been modified by an attacker.

• Impose a minimal overhead on the system where it is running.

• Be able to be configured according to the security policies of the system that is

being monitored.

• Be able to adapt to changes in system and user behavior over time.

• Be able to scale to monitor a large number of hosts.

• Provide graceful degradation of service in the sense that if some components

of the IDS stop working for any reason, the rest of them should be affected as

little as possible.

• Allow dynamic reconfiguration; that is, the ability to reconfigure the IDS

without having to restart it.

Run continually

Be fault tolerant

Resist subversion

Impose a minimal overhead on system

Configured according to system security policies

Adapt to changes in systems and users

Scale to monitor large numbers of systems

Provide graceful degradation of service

Allow dynamic reconfiguration

Analysis Approaches

Anomaly detection

Signature/Heuristic detection

Involves the collection of data relating to the behavior of legitimate users over a period of time

Current observed behavior is analyzed to determine whether this behavior is that of a legitimate user or that of an intruder

Uses a set of known malicious data patterns or attack rules that are compared with current behavior

Also known as misuse detection

Can only identify known attacks for which it has patterns or rules

IDSs typically use one of the following alternative approaches to analyze sensor

data to detect intrusions:

1. Anomaly detection: Involves the collection of data relating to the behavior

of legitimate users over a period of time. Then current observed behavior is

analyzed to determine with a high level of confidence whether this behavior is

that of a legitimate user or alternatively that of an intruder.

2. Signature or Heuristic detection: Uses a set of known malicious data patterns

(signatures) or attack rules (heuristics) that are compared with current behavior

to decide if is that of an intruder. It is also known as misuse detection. This

approach can only identify known attacks for which it has patterns or rules.

In essence, anomaly approaches aim to define normal, or expected, behavior, in

order to identify malicious or unauthorized behavior. Signature or heuristic-based

approaches directly define malicious or unauthorized behavior. They can quickly

and efficiently identify known attacks. However only anomaly detection is able to

detect unknown, zero-day attacks, as it starts with known good behavior and identifies

anomalies to it. Given this advantage, clearly anomaly detection would be the

preferred approach, were it not for the difficulty in collecting and analyzing the data

required, and the high level of false alarms, as we discuss in the following sections.

Anomaly Detection

A variety of classification approaches are used:

The anomaly detection approach involves first developing a model of legitimate

user behavior by collecting and processing sensor data from the normal operation

of the monitored system in a training phase. This may occur at distinct times, or

there may be a continuous process of monitoring and evolving the model over time.

Once this model exists, current observed behavior is compared with the model in

order to classify it as either legitimate or anomalous activity in a detection phase.

A variety of classification approaches are used, which [GARC09] broadly categorized

as:

• Statistical: Analysis of the observed behavior using univariate, multivariate,

or time-series models of observed metrics.

• Knowledge based: Approaches use an expert system that classifies observed

behavior according to a set of rules that model legitimate behavior.

• Machine-learning: Approaches automatically determine a suitable classification

model from the training data using data mining techniques.

They also note two key issues that affect the relative performance of these alternatives,

being the efficiency and cost of the detection process.

The monitored data is first parameterized into desired standard metrics that

will then be analyzed. This step ensures that data gathered from a variety of possible

sources is provided in standard form for analysis.

Statistical approaches use the captured sensor data to develop a statistical

profile of the observed metrics. The earliest approaches used univariate models,

where each metric was treated as an independent random variable. However this

was too crude to effectively identify intruder behavior. Later, multivariate models

considered correlations between the metrics, which better levels of discrimination

observed. Time-series models use the order and time between observed events to

better classify the behavior. The advantages of these statistical approaches include

their relative simplicity and low computation cost, and lack of assumptions about

behavior expected. Their disadvantages include the difficulty in selecting suitable

metrics to obtain a reasonable balance between false positives and false negatives,

and that not all behaviors can be modeled using these approaches.

Knowledge based approaches classify the observed data using a set of rules.

These rules are developed during the training phase, usually manually, to characterize

the observed training data into distinct classes. Formal tools may be used to

describe these rules, such as a finite-state machine or a standard description language.

They are then used to classify the observed data in the detection phase. The

advantages of knowledge-based approaches include their robustness and flexibility.

Their main disadvantage is the difficulty and time required to develop high-quality

knowledge from the data, and the need for human experts to assist with this process.

Machine-learning approaches use data mining techniques to automatically

develop a model using the labeled normal training data. This model is then able

to classify subsequently observed data as either normal or anomalous. A key disadvantage

is that this process typically requires significant time and computational

resources. Once the model is generated however, subsequent analysis is generally

fairly efficient.

A variety of machine-learning approaches have been tried, with varying success.

These include:

• Bayesian networks: Encode probabilistic relationships among observed metrics.

• Markov models: Develop a model with sets of states, some possibly hidden,

interconnected by transition probabilities.

• Neural networks: Simulate human brain operation with neurons and synapse

between them, that classify observed data.

• Fuzzy logic: Uses fuzzy set theory where reasoning is approximate, and can

accommodate uncertainty.

• Genetic algorithms: Uses techniques inspired by evolutionary biology, including

inheritance, mutation, selection and recombination, to develop classification

rules.

• Clustering and outlier detection: Group the observed data into clusters based

on some similarity or distance measure, and then identify subsequent data as

either belonging to a cluster or as an outlier.

The advantages of the machine-learning approaches include their flexibility, adaptability,

and ability to capture interdependencies between the observed metrics.

Their disadvantages include their dependency on assumptions about accepted

behavior for a system, their currently unacceptably high false alarm rate, and their

high resource cost.

A key limitation of anomaly detection approaches used by IDSs, particularly

the machine-learning approaches, is that they are generally only trained with legitimate

data, unlike many of the other applications surveyed in [CHAN09] where

both legitimate and anomalous training data is used. The lack of anomalous training

data, which occurs given the desire to detect currently unknown future attacks,

limits the effectiveness of some of the techniques listed above.

Statistical

Analysis of the observed behavior using univariate, multivariate, or time-series models of observed metrics

Knowledge based

Approaches use an expert system that classifies observed behavior according to a set of rules that model legitimate behavior

Machine-learning

Approaches automatically determine a suitable classification model from the training data using data mining techniques

Signature or Heuristic Detection

Signature or heuristic techniques detect intrusion by observing events in the system

and applying either a set of signature patterns to the data, or a set of rules that

characterize the data, leading to a decision regarding whether the observed data

indicates normal or anomalous behavior.

Signature approaches match a large collection of known patterns of malicious

data against data stored on a system or in transit over a network. The signatures

need to be large enough to minimize the false alarm rate, while still detecting a

sufficiently large fraction of malicious data. This approach is widely used in antivirus

products, in network traffic scanning proxies, and in NIDS. The advantages

of this approach include the relatively low cost in time and resource use, and its

wide acceptance. Disadvantages include the significant effort required to constantly

identify and review new malware to create signatures able to identify it, and the

inability to detect zero-day attacks for which no signatures exist.

Rule-based heuristic identification involves the use of rules for identifying

known penetrations or penetrations that would exploit known weaknesses. Rules

can also be defined that identify suspicious behavior, even when the behavior is

within the bounds of established patterns of usage. Typically, the rules used in

these systems are specific to the machine and operating system. The most fruitful

approach to developing such rules is to analyze attack tools and scripts collected on

the Internet. These rules can be supplemented with rules generated by knowledgeable

security personnel. In this latter case, the normal procedure is to interview

system administrators and security analysts to collect a suite of known penetration

scenarios and key events that threaten the security of the target system.

The SNORT system, which we discuss later in Section 8.9 is an example of a

rule-based NIDS. A large collection of rules exists for it to detect a wide variety of

network attacks.

Signature approaches

Match a large collection of known patterns of malicious data against data stored on a system or in transit over a network

The signatures need to be large enough to minimize the false alarm rate, while still detecting a sufficiently large fraction of malicious data

Widely used in anti-virus products, network traffic scanning proxies, and in NIDS

Rule-based heuristic identification

Involves the use of rules for identifying known penetrations or penetrations that would exploit known weaknesses

Rules can also be defined that identify suspicious behavior, even when the behavior is within the bounds of established patterns of usage

Typically rules used are specific

SNORT is an example of a rule-based NIDS

Host-Based Intrusion Detection (HIDS)

Adds a specialized layer of security software to vulnerable or sensitive systems

Can use either anomaly or signature and heuristic approaches

Monitors activity to detect suspicious behavior

Primary purpose is to detect intrusions, log suspicious events, and send alerts

Can detect both external and internal intrusions

Host-based IDSs (HIDSs) add a specialized layer of security software to vulnerable

or sensitive systems; such as database servers and administrative systems. The HIDS

monitors activity on the system in a variety of ways to detect suspicious behavior.

In some cases, an IDS can halt an attack before any damage is done, as we discuss

in Section 9.6, but its main purpose is to detect intrusions, log suspicious events, and

send alerts.

The primary benefit of a HIDS is that it can detect both external and internal

intrusions, something that is not possible either with network-based IDSs or firewalls.

As we discuss in the previous section, host-based IDSs can use either anomaly

or signature and heuristic approaches to detect unauthorized behavior on the monitored

host. We now review some common data sources and sensors used in HIDS,

and then continue with a discussion of how the anomaly, signature and heuristic

approaches are used in HIDS, and then consider distributed HIDS.

Data Sources and Sensors

As noted previously, a fundamental component of intrusion detection is the sensor

that collects data. Some record of ongoing activity by users must be provided as

input to the analysis component of the IDS. Common data sources include:

• System call traces: A record of the sequence of systems calls by processes on

a system, is widely acknowledged as the preferred data source for HIDS since

the pioneering work of Forrest [CREE13]. While these work well on Unix and

Linux systems, they are problematic on Windows systems due to the extensive

use of DLLs that obscure which processes use specific system calls.

• Audit (log file) records: Most modern operating systems include accounting

software that collects information on user activity. The advantage of using this

information is that no additional collection software is needed. The disadvantages

are that the audit records may not contain the needed information or may not

contain it in a convenient form, and that intruders may attempt to manipulate

these records to hide their actions.

• File integrity checksums: A common approach to detecting intruder activity

on a system is to periodically scan critical files for changes from the desired

baseline, by comparing a current cryptographic checksums for these files, with

a record of known good values. Disadvantages include the need to generate

and protect the checksums using known good files, and the difficulty monitoring

changing files. Tripwire is a well-known system using this approach.

• Registry access: An approach used on Windows systems is to monitor access

to the registry, given the amount of information and access to it used by programs

on these systems. However this source is very Windows specific, and

has recorded limited success.

The sensor gathers data from the chosen source, filters the gathered data to

remove any unwanted information and to standardize the information format, and

forwards the result to the IDS analyzer, which may be local or remote.

A fundamental component of intrusion detection is the sensor that collects data

Common data sources include:

System call traces

Audit (log file) records

File integrity checksums

Registry access

Table 8.2

Linux System Calls and Windows DLLs Monitored

(Table can be found on page 264 in the textbook)

The majority of work on anomaly-based HIDS has been done on UNIX and Linux

systems, given the ease of gathering suitable data for this work. While some earlier

work used audit or accounting records, the majority is based on system call traces.

System calls are the means by which programs access core kernel functions, providing

a wide range of interactions with the low-level operating system functions.

Hence they provide detailed information on process activity that can be used to

classify it as normal or anomalous. Table 8.2 (a) lists the system calls used in current

Ubuntu Linux systems as an example. This data is typically gathered using an OS

hook, such as the BSM audit module. Most modern operating systems have highly

reliable options for collecting this type of information.

The system call traces are then analyzed by a suitable decision engine.

[CREE13] notes that the original work by Forrest et al introduced the Sequence

Time-Delay Embedding (STIDE) algorithm, based on artificial immune system

approaches, that compares observed sequences of system calls with sequences from

the training phase to obtain a mismatch ratio that determines whether the sequence

is normal or not. Later work has used other alternatives, such as Hidden Markov

Models (HMM), Artificial Neural Networks (ANN), Support Vector Machines

(SVM), or Extreme Learning Machines (ELM) to make this classification.

[CREE13] notes that these approaches all report providing reasonable

intruder detection rates of 95-99% while having false positive rates of less than 5%,

though on older test datasets. He updates these results using recent contemporary

data and example attacks, with a more extensive feature extraction process from the

system call traces and an ELM decision engine capable of a very high detection rate

while maintaining reasonable false positive rates. This approach should lead to even

more effective production HIDS products in the near future.

Windows systems have traditionally not used anomaly based HIDS, as the

wide usage of Dynamic Link Libraries (DLLs) as an intermediary between process

requests for operating system functions and the actual system call interface, has

hindered the effective use of system call traces to classify process behavior. Some

work was done using either audit log entries, or registry file updates as a data source,

but neither approach was very successful. [CREE13] reports a new approach that

uses traces of key DLL function calls as an alternative data source, with results comparable

to that found with Linux system call trace HIDS. Table 8.2 (b) lists the key

DLLs and executables monitored. Note that all of the distinct functions within these

DLLs, numbering in their thousands, are monitored, forming the equivalent to the

system call list presented in Table 8.2a. The adoption of this approach should

lead to the development of more effective Windows HIDS, capable of detecting

zero-day attacks, unlike the current generation of signature and heuristic Windows

HIDS that we discuss later.

While using system call traces provides arguably the richest information source

for a HIDS, it does impose a moderate load on the monitored system to gather and

classify this data. And as we noted earlier, the training phase for many of the decision

engines requires very significant time and computational resources. Hence others

have trialed approaches based on audit (log) records. However these both have

a lower detection rate than the system call trace approaches (80% reported), and

are more susceptible to intruder manipulation.

A further alternative to examining current process behavior, is to look for

changes to important files on the monitored host. This uses a cryptographic checksum

to check for any changes from the known good baseline for the monitored files.

Typically all program binaries, scripts, and configuration files are monitored, either

on each access, or on a periodic scan of the file system. The tripwire system is a

widely used implementation of this approach, and is available for all major operating

systems including Linux, Mac OS, and Windows. This approach is very sensitive

to changes in the monitored files, as a result of intruder activity or for any other

reason. However it cannot detect changes made to processes once they are running

on the system. Other difficulties include determining which files to monitor, since

a surprising number of files change in an operational system, having access to a

known good copy of each monitored file to establish the baseline value, and protecting

the database of file signatures.

Traditionally, work on host-based IDSs focused on single-system stand-alone operation.

The typical organization, however, needs to defend a distributed collection

of hosts supported by a LAN or internetwork. Although it is possible to mount a

defense by using stand-alone IDSs on each host, a more effective defense can be

achieved by coordination and cooperation among IDSs across the network.

Porras points out the following major issues in the design of a distributed IDS

[PORR92]:

• A distributed IDS may need to deal with different sensor data formats. In a

heterogeneous environment, different systems may use different sensors and

approaches to gathering data for intrusion detection use.

• One or more nodes in the network will serve as collection and analysis points

for the data from the systems on the network. Thus, either raw sensor data or

summary data must be transmitted across the network. Therefore, there is a

requirement to assure the integrity and confidentiality of these data. Integrity

is required to prevent an intruder from masking his or her activities by altering

the transmitted audit information. Confidentiality is required because the

transmitted audit information could be valuable.

• Either a centralized or decentralized architecture can be used. With a centralized

architecture, there is a single central point of collection and analysis of all

sensor data. This eases the task of correlating incoming reports but creates a

potential bottleneck and single point of failure. With a decentralized architecture,

there is more than one analysis center, but these must coordinate their

activities and exchange information.

A good example of a distributed IDS is one developed at the University of

California at Davis [HEBE92, SNAP91]; a similar approach has been taken for a

project at Purdue [SPAF00, BALA98]. Figure 8.2 shows the overall architecture,

which consists of three main components:

1. Host agent module: An audit collection module operating as a background

process on a monitored system. Its purpose is to collect data on security-related

events on the host and transmit these to the central manager. Figure 8.3

shows details of the agent module architecture.

2. LAN monitor agent module: Operates in the same fashion as a host agent

module except that it analyzes LAN traffic and reports the results to the central

manager.

3. Central manager module: Receives reports from LAN monitor and host

agents and processes and correlates these reports to detect intrusion.

The scheme is designed to be independent of any operating system or system

auditing implementation. Figure 8.3 shows the general approach that is taken. The

agent captures each audit record produced by the native audit collection system. A

filter is applied that retains only those records that are of security interest. These

records are then reformatted into a standardized format referred to as the host

audit record (HAR). Next, a template-driven logic module analyzes the records for

suspicious activity. At the lowest level, the agent scans for notable events that are

of interest independent of any past events. Examples include failed files, accessing

system files, and changing a file’s access control. At the next higher level, the agent

looks for sequences of events, such as known attack patterns (signatures). Finally,

the agent looks for anomalous behavior of an individual user based on a historical

profile of that user, such as number of programs executed, number of files accessed,

and the like.

When suspicious activity is detected, an alert is sent to the central manager.

The central manager includes an expert system that can draw inferences from

received data. The manager may also query individual systems for copies of HARs

to correlate with those from other agents.

The LAN monitor agent also supplies information to the central manager.

The LAN monitor agent audits host-host connections, services used, and volume of

traffic. It searches for significant events, such as sudden changes in network load,

the use of security-related services, and suspicious network activities.

The architecture depicted in Figures 8.2 and 8.3 is quite general and flexible.

It offers a foundation for a machine-independent approach that can expand from

stand-alone intrusion detection to a system that is able to correlate activity from

a number of sites and networks to detect suspicious activity that would otherwise

remain undetected.

Network-Based IDS (NIDS)

A network-based IDS (NIDS) monitors traffic at selected points on a network or

interconnected set of networks. The NIDS examines the traffic packet by packet in

real time, or close to real time, to attempt to detect intrusion patterns. The NIDS

may examine network-, transport-, and/or application-level protocol activity. Note

the contrast with a host-based IDS; a NIDS examines packet traffic directed toward

potentially vulnerable computer systems on a network. A host-based system examines

user and software activity on a host.

A typical NIDS facility includes a number of sensors to monitor packet traffic, one

or more servers for NIDS management functions, and one or more management consoles

for the human interface. The analysis of traffic patterns to detect intrusions may

be done at the sensor, at the management server, or some combination of the two.

Monitors traffic at selected points on a network

Examines traffic packet by packet in real or close to real time

May examine network, transport, and/or application-level protocol activity

Comprised of a number of sensors, one or more servers for NIDS management functions, and one or more management consoles for the human interface

Analysis of traffic patterns may be done at the sensor, the management server or a combination of the two

Sensors can be deployed in one of two modes: inline and passive. An inline

sensor is inserted into a network segment so that the traffic that it is monitoring

must pass through the sensor. One way to achieve an inline sensor is to combine

NIDS sensor logic with another network device, such as a firewall or a LAN

switch. This approach has the advantage that no additional separate hardware

devices are needed; all that is required is NIDS sensor software. An alternative

is a stand-alone inline NIDS sensor. The primary motivation for the use of

inline sensors is to enable them to block an attack when one is detected. In this

case the device is performing both intrusion detection and intrusion prevention

functions.

More commonly, passive sensors are used. A passive sensor monitors a

copy of network traffic; the actual traffic does not pass through the device. From

the point of view of traffic flow, the passive sensor is more efficient than the

inline sensor, because it does not add an extra handling step that contributes to

packet delay.

Figure 8.4 illustrates a typical passive sensor configuration. The sensor connects

to the network transmission medium, such as a fiber optic cable, by a direct

physical tap. The tap provides the sensor with a copy of all network traffic being

carried by the medium. The network interface card (NIC) for this tap usually does

not have an IP address configured for it. All traffic into this NIC is simply collected

with no protocol interaction with the network. The sensor has a second NIC that

connects to the network with an IP address and enables the sensor to communicate

with a NIDS management server.

Consider an organization with multiple sites, each of which has one or more LANs,

with all of the networks interconnected via the Internet or some other WAN

technology. For a comprehensive NIDS strategy, one or more sensors are needed

at each site. Within a single site, a key decision for the security administrator is the

placement of the sensors.

Figure 8.5 illustrates a number of possibilities. In general terms, this configuration

is typical of larger organizations. All Internet traffic passes through an external firewall

that protects the entire facility. Traffic from the outside world, such as customers and

vendors that need access to public services, such as Web and mail, is monitored. The

external firewall also provides a degree of protection for those parts of the network

that should only be accessible by users from other corporate sites. Internal firewalls

may also be used to provide more specific protection to certain parts of the network.

A common location for a NIDS sensor is just inside the external firewall

( location 1 in the figure). This position has a number of advantages:

• Sees attacks, originating from the outside world, that penetrate the network’s

perimeter defenses (external firewall).

• Highlights problems with the network firewall policy or performance.

• Sees attacks that might target the Web server or ftp server.

• Even if the incoming attack is not recognized, the IDS can sometimes recognize

the outgoing traffic that results from the compromised server.

Instead of placing a NIDS sensor inside the external firewall, the security

administrator may choose to place a NIDS sensor between the external firewall and

the Internet or WAN (location 2 ). In this position, the sensor can monitor all network

traffic, unfiltered. The advantages of this approach are as follows:

• Documents number of attacks originating on the Internet that target the network

• Documents types of attacks originating on the Internet that target the network

A sensor at location 2 has a higher processing burden than any sensor located

elsewhere on the site network.

In addition to a sensor at the boundary of the network, on either side of the

external firewall, the administrator may configure a firewall and one or more sensors

to protect major backbone networks, such as those that support internal servers

and database resources (location 3). The benefits of this placement include the

following:

• Monitors a large amount of a network’s traffic, thus increasing the possibility

of spotting attacks

• Detects unauthorized activity by authorized users within the organization’s

security perimeter

Thus, a sensor at location 3 is able to monitor for both internal and external

attacks. Because the sensor monitors traffic to only a subset of devices at the site, it can

be tuned to specific protocols and attack types, thus reducing the processing burden.

Finally, the network facilities at a site may include separate LANs that support

user workstations and servers specific to a single department. The administrator

could configure a firewall and NIDS sensor to provide additional protection for

all of these networks or target the protection to critical subsystems, such as personnel

and financial networks (location 4). A sensor used in this latter fashion provides

the following benefits:

• Detects attacks targeting critical systems and resources

• Allows focusing of limited resources to the network assets considered of

greatest value

As with a sensor at location 3, a sensor at location 4 can be tuned to specific

protocols and attack types, thus reducing the processing burden.

Intrusion Detection Techniques

Attacks suitable for

Signature detection

Attacks suitable for

Anomaly detection

Application layer reconnaissance and attacks

Transport layer reconnaissance and attacks

Network layer reconnaissance and attacks

Unexpected application services

Policy violations

Denial-of-service (DoS) attacks

Scanning

Worms

As with host-based intrusion detection, network-based intrusion detection makes

use of signature detection and anomaly detection. Unlike the case with HIDS, a

number of commercial anomaly NIDS products are available [GARC09]. One of

the best known is the Statistical Packet Anomaly Detection Engine (SPADE),

available as a plug-in for the Snort system that we discuss later.

NIST SP 800-94 (Guide to Intrusion Detection and Prevention

Systems, July 2012) lists the following as examples of that types of attacks that

are suitable for signature detection:

• Application layer reconnaissance and attacks: Most NIDS technologies

analyze several dozen application protocols. Commonly analyzed ones include

Dynamic Host Configuration Protocol (DHCP), DNS, Finger, FTP, HTTP,

Internet Message Access Protocol (IMAP), Internet Relay Chat (IRC),

Network File System (NFS), Post Office Protocol (POP), rlogin/rsh, Remote

Procedure Call (RPC), Session Initiation Protocol (SIP), Server Message

Block (SMB), SMTP, SNMP, Telnet, and Trivial File Transfer Protocol

(TFTP), as well as database protocols, instant messaging applications, and

peer-to-peer file sharing software. The NIDS is looking for attack patterns

that have been identified as targeting these protocols. Examples of attack include

buffer overflows, password guessing, and malware transmission.

• Transport layer reconnaissance and attacks: NIDSs analyze TCP and UDP

traffic and perhaps other transport layer protocols. Examples of attacks are

unusual packet fragmentation, scans for vulnerable ports, and TCP-specific

attacks such as SYN floods.

• Network layer reconnaissance and attacks: NIDSs typically analyze IPv4, IPv6,

ICMP, and IGMP at this level. Examples of attacks are spoofed IP addresses

and illegal IP header values.

• Unexpected application services: The NIDS attempts to determine if the

activity on a transport connection is consistent with the expected application

protocol. An example is a host running an unauthorized application service.

• Policy violations: Examples include use of inappropriate Web sites and use of

forbidden application protocols.

NIST SP 800-94 lists the following as examples

of the types of attacks that are suitable for anomaly detection:

• Denial-of-service (DoS) attacks: Such attacks involve either significantly

increased packet traffic or significantly increase connection attempts, in an

attempt to overwhelm the target system. These attacks are analyzed in Chapter 7.

Anomaly detection is well suited to such attacks.

• Scanning: A scanning attack occurs when an attacker probes a target network

or system by sending different kinds of packets. Using the responses received

from the target, the attacker can learn many of the system’s characteristics and

vulnerabilities. Thus, a scanning attack acts as a target identification tool for

an attacker. Scanning can be detected by atypical flow patterns at the application

layer (e.g., banner grabbing3), transport layer (e.g., TCP and UDP port

scanning), and network layer (e.g., ICMP scanning).

• Worms: Worms spreading among hosts can be detected in more than one

way. Some worms propagate quickly and use large amounts of bandwidth.

Worms can also be detected because they can cause hosts to communicate

with each other that typically do not, and they can also cause hosts to use ports

that they normally do not use. Many worms also perform scanning. Chapter 6

discusses worms in detail.

Stateful Protocol Analysis (SPA)

Subset of anomaly detection that compares observed network traffic against predetermined universal vendor supplied profiles of benign protocol traffic

This distinguishes it from anomaly techniques trained with organization specific traffic protocols

Understands and tracks network, transport, and application protocol states to ensure they progress as expected

A key disadvantage is the high resource use it requires

NIST SP 800-94 details this subset of anomaly

detection that compares observed network traffic against predetermined universal

vendor supplied profiles of benign protocol traffic. This distinguishes it from anomaly

techniques trained with organization specific traffic profiles. SPA understands and

tracks network, transport, and application protocol states to ensure they progress as

expected. A key disadvantage of SPA is the high resource use it requires.

Logging of Alerts

Typical information logged by a NIDS sensor includes:

Timestamp

Connection or session ID

Event or alert type

Rating

Network, transport, and application layer protocols

Source and destination IP addresses

Source and destination TCP or UDP ports, or ICMP types and codes

Number of bytes transmitted over the connection

Decoded payload data, such as application requests and responses

State-related information

When a sensor detects a potential violation, it sends an alert and logs information

related to the event. The NIDS analysis module can use this information to refine

intrusion detection parameters and algorithms. The security administrator can use

this information to design prevention techniques. Typical information logged by a

NIDS sensor includes the following:

• Timestamp (usually date and time)

• Connection or session ID (typically a consecutive or unique number assigned to

each TCP connection or to like groups of packets for connectionless protocols)

• Event or alert type

• Rating (e.g., priority, severity, impact, confidence)

• Network, transport, and application layer protocols

• Source and destination IP addresses

• Source and destination TCP or UDP ports, or ICMP types and codes

• Number of bytes transmitted over the connection

• Decoded payload data, such as application requests and responses

• State-related information (e.g., authenticated username)

In recent years, the concept of communicating IDSs has evolved to schemes that

involve distributed systems that cooperate to identify intrusions and to adapt to

changing attack profiles. These combine in a central IDS, the complementary information

sources used by HIDS with host-based process and data details, and NIDS

with network events and data, to manage and coordinate intrusion detection and

response in an organization’s IT infrastructure. Two key problems have always

confronted systems such as IDSs, firewalls, virus and worm detectors, and so on.

First, these tools may not recognize new threats or radical modifications of existing

threats. And second, it is difficult to update schemes rapidly enough to deal

with quickly spreading attacks. A separate problem for perimeter defenses, such as

firewalls, is that the modern enterprise has loosely defined boundaries, and hosts

are generally able to move in and out. Examples are hosts that communicate using

wireless technology and employee laptops that can be plugged into network ports.

Attackers have exploited these problems in several ways. The more traditional

attack approach is to develop worms and other malicious software that spreads ever more

rapidly and to develop other attacks (such as DoS attacks) that strike with overwhelming

force before a defense can be mounted. This style of attack is still prevalent. But more

recently, attackers have added a quite different approach: Slow the spread of the attack so

that it will be more difficult to detect by conventional algorithms [ANTH07].

A way to counter such attacks is to develop cooperated systems that can recognize

attacks based on more subtle clues and then adapt quickly. In this approach,

anomaly detectors at local nodes look for evidence of unusual activity. For example,

a machine that normally makes just a few network connections might suspect that an

attack is under way if it is suddenly instructed to make connections at a higher rate.

With only this evidence, the local system risks a false positive if it reacts to the suspected

attack (say by disconnecting from the network and issuing an alert) but it risks

a false negative if it ignores the attack or waits for further evidence. In an adaptive,

cooperative system, the local node instead uses a peer-to-peer “gossip” protocol to

inform other machines of its suspicion, in the form of a probability that the network

is under attack. If a machine receives enough of these messages so that a threshold is

exceeded, the machine assumes an attack is under way and responds. The machine

may respond locally to defend itself and also send an alert to a central system.

An example of this approach is a scheme developed by Intel and referred to

as autonomic enterprise security [AGOS06]. Figure 8.6 illustrates the approach.

This approach does not rely solely on perimeter defense mechanisms, such as

firewalls, or on individual host-based defenses. Instead, each end host and each

network device (e.g., routers) is considered to be a potential sensor and may have

the sensor software module installed. The sensors in this distributed configuration

can exchange information to corroborate the state of the network (i.e., whether an

attack is under way).

The Intel designers provide the following motivation for this approach:

1. IDSs deployed selectively may miss a network-based attack or may be slow

to recognize that an attack is under way. The use of multiple IDSs that

share information has been shown to provide greater coverage and more

rapid response to attacks, especially slowly growing attacks (e.g., [BAIL05],

[RAJA05]).

2. Analysis of network traffic at the host level provides an environment in which

there is much less network traffic than found at a network device such as a

router. Thus, attack patterns will stand out more, providing in effect a higher

signal-to-noise ratio.

3. Host-based detectors can make use of a richer set of data, possibly using

application data from the host as input into the local classifier.

NIST SP 800-94 notes that adistributed or hybrid IDS can be constructed using multiple products from

a single vendor, designed to share and exchange data [SCAR12]. This is clearly an

easier, but may not be the most cost-effective or comprehensive solution. Alternatively,

specialized security information and event management (SIEM) software

exists that can import and analyze data from a variety of sources, sensors, and

products. Such software may well rely on standardized protocols, such as Intrusion

Detection Exchange Format we discuss in the next section. An analogy may help

clarify the advantage of this distributed approach. Suppose that a single host is subject

to a prolonged attack and that the host is configured to minimize false positives.

Early on in the attack, no alert is sounded because the risk of false positive is high.

If the attack persists, the evidence that an attack is under way becomes stronger and

the risk of false positive decreases. However, much time has passed. Now consider

many local sensors, each of which suspect the onset of an attack and all of which collaborate.

Because numerous systems see the same evidence, an alert can be issued

with a low false positive risk. Thus, instead of a long period of time, we use a large

number of sensors to reduce false positives and still detect attacks. A number of

vendors now offer this type of product.

We now summarize the principal elements of this approach, illustrated in Figure

8.6. A central system is configured with a default set of security policies. Based

on input from distributed sensors, these policies are adapted and specific actions

are communicated to the various platforms in the distributed system. The devicespecific

policies may include immediate actions to take or parameter settings to be

adjusted. The central system also communicates collaborative policies to all platforms

that adjust the timing and content of collaborative gossip messages. Three

types of input guide the actions of the central system:

• Summary events: Events from various sources are collected by intermediate

collection points such as firewalls, IDSs, or servers that serve a specific segment

of the enterprise network. These events are summarized for delivery to

the central policy system.

• DDI events: Distributed detection and inference (DDI) events are alerts that

are generated when the gossip traffic enables a platform to conclude that an

attack is under way.

• PEP events: Policy enforcement points (PEPs) reside on trusted, self-defending

platforms and intelligent IDSs. These systems correlate distributed

information, local decisions, and individual device actions to detect intrusions

that may not be evident at the host level.

IETF Intrusion Detection Working Group

Purpose is to define data formats and exchange procedures for sharing information of interest to intrusion detection and response systems and to management systems that may need to interact with them

The working group issued the following RFCs in 2007:

To facilitate the development of distributed IDSs that can function across a wide

range of platforms and environments, standards are needed to support interoperability.

Such standards are the focus of the IETF Intrusion Detection Working

Group. The purpose of the working group is to define data formats and exchange

procedures for sharing information of interest to intrusion detection and response

systems and to management systems that may need to interact with them. The

working group issued the following RFCs in 2007:

• Intrusion Detection Message Exchange Requirements (RFC 4766): This

document defines requirements for the Intrusion Detection Message

Exchange Format (IDMEF). The document also specifies requirements for a

communication protocol for communicating IDMEF.

• The Intrusion Detection Message Exchange Format (RFC 4765): This

document describes a data model to represent information exported by

intrusion detection systems and explains the rationale for using this model.

An implementation of the data model in the Extensible Markup Language

(XML) is presented, an XML Document Type Definition is developed, and

examples are provided.

• The Intrusion Detection Exchange Protocol (RFC 4767): This document

describes the Intrusion Detection Exchange Protocol (IDXP), an

application-level protocol for exchanging data between intrusion detection

entities. IDXP supports mutual-authentication, integrity, and confidentiality

over a connection-oriented protocol.

Intrusion Detection Message Exchange Requirements (RFC 4766)

Document defines requirements for the Intrusion Detection Message Exchange Format (IDMEF)

Also specifies requirements for a communication protocol for communicating IDMEF

The Intrusion Detection Message Exchange Format (RFC 4765)

Document describes a data model to represent information exported by intrusion detection systems and explains the rationale for using this model

An implementation of the data model in the Extensible Markup Language (XML) is presented, and XML Document Type Definition is developed, and examples are provided

The Intrusion Detection Exchange Protocol (RFC 4767)

Document describes the Intrusion Detection Exchange Protocol (IDXP), an application level protocol for exchanging data between intrusion detection entities

IDXP supports mutual authentication, integrity, and confidentiality over a connection oriented protocol

Figure 8.7 illustrates the key elements of the model on which the intrusion

detection message exchange approach is based. This model does not correspond

to any particular product or implementation, but its functional components are the

key elements of any IDS. The functional components are as follows:

• Data source: The raw data that an IDS uses to detect unauthorized or undesired

activity. Common data sources include network packets, operating system

audit logs, application audit logs, and system-generated checksum data.

• Sensor: Collects data from the data source. The sensor forwards events to the

analyzer.

• Analyzer: The ID component or process that analyzes the data collected by

the sensor for signs of unauthorized or undesired activity or for events that

might be of interest to the security administrator. In many existing IDSs, the

sensor and the analyzer are part of the same component.

• Administrator: The human with overall responsibility for setting the security

policy of the organization, and, thus, for decisions about deploying and

configuring the IDS. This may or may not be the same person as the operator

of the IDS. In some organizations, the administrator is associated with the

network or systems administration groups. In other organizations, it’s an

independent position.

Manager: The ID component or process from which the operator manages

the various components of the ID system. Management functions typically

include sensor configuration, analyzer configuration, event notification management,

data consolidation, and reporting.

• Operator: The human that is the primary user of the IDS manager. The operator

often monitors the output of the IDS and initiates or recommends further

action.

In this model, intrusion detection proceeds in the following manner. The sensor

monitors data sources looking for suspicious activity , such as network sessions

showing unexpected telnet activity, operating system log file entries showing a user

attempting to access files to which he or she is not authorized to have access, and

application log files showing persistent login failures. The sensor communicates suspicious

activity to the analyzer as an event , which characterizes an activity within a

given period of time. If the analyzer determines that the event is of interest, it sends

an alert to the manager component that contains information about the unusual

activity that was detected, as well as the specifics of the occurrence. The manager

component issues a notification to the human operator. A response can be initiated

automatically by the manager component or by the human operator. Examples of

responses include logging the activity; recording the raw data (from the data source)

that characterized the event; terminating a network, user, or application session; or

altering network or system access controls. The security policy is the predefined,

formally documented statement that defines what activities are allowed to take

place on an organization’s network or on particular hosts to support the organization’s

requirements. This includes, but is not limited to, which hosts are to be denied

external network access.

The specification defines formats for event and alert messages, message types,

and exchange protocols for communication of intrusion detection information.

Honeypots

Decoy systems designed to:

Lure a potential attacker away from critical systems

Collect information about the attacker’s activity

Encourage the attacker to stay on the system long enough for administrators to respond

Systems are filled with fabricated information that a legitimate user of the system wouldn’t access

Resources that have no production value

Therefore incoming communication is most likely a probe, scan, or attack

Initiated outbound communication suggests that the system has probably been compromised

A further component of intrusion detection technology is the honeypot. Honeypots

are decoy systems that are designed to lure a potential attacker away from critical

systems. Honeypots are designed to:

• Divert an attacker from accessing critical systems.

• Collect information about the attacker’s activity.

• Encourage the attacker to stay on the system long enough for administrators

to respond.

These systems are filled with fabricated information designed to appear valuable

but that a legitimate user of the system would not access. Thus, any access to

the honeypot is suspect. The system is instrumented with sensitive monitors and

event loggers that detect these accesses and collect information about the attacker’s

activities. Because any attack against the honeypot is made to seem successful,

administrators have time to mobilize and log and track the attacker without ever

exposing productive systems.

The honeypot is a resource that has no production value. There is no legitimate

reason for anyone outside the network to interact with a honeypot. Thus, any

attempt to communicate with the system is most likely a probe, scan, or attack. Conversely,

if a honeypot initiates outbound communication, the system has probably

been compromised.

Honeypot Classifications

Low interaction honeypot

Consists of a software package that emulates particular IT services or systems well enough to provide a realistic initial interaction, but does not execute a full version of those services or systems

Provides a less realistic target

Often sufficient for use as a component of a distributed IDS to warn of imminent attack

High interaction honeypot

A real system, with a full operating system, services and applications, which are instrumented and deployed where they can be accessed by attackers

Is a more realistic target that may occupy an attacker for an extended period

However, it requires significantly more resources

If compromised could be used to initiate attacks on other systems

Honeypots are typically classified as being either low or high interaction.

• Low interaction honeypot: Consists of a software package that emulates particular

IT services or systems well enough to provide a realistic initial interaction,

but does not execute a full version of those services or systems.

• High interaction honeypot: Is a real system, with a full operating system, services

and applications, which are instrumented and deployed where they can be

accessed by attackers.

A high interaction honeypot is a more realistic target that may occupy an

attacker for an extended period. However, it requires significantly more resources,

and if compromised could be used to initiate attacks on other systems. This may

result in unwanted legal or reputational issues for the organization running it. A low

interaction honeypot provides a less realistic target, able to identify intruders using

the earlier stages of the attack methodology we discussed earlier in this chapter.

This is often sufficient for use as a component of a distributed IDS to warn of imminent

attack. “The Honeynet Project” provides a range of resources and packages

for such systems.

Initial efforts involved a single honeypot computer with IP addresses designed

to attract hackers. More recent research has focused on building entire honeypot

networks that emulate an enterprise, possibly with actual or simulated traffic and

data. Once hackers are within the network, administrators can observe their behavior

in detail and figure out defenses.

Honeypots can be deployed in a variety of locations. Figure 8.8 illustrates

some possibilities. The location depends on a number of factors, such as the type

of information the organization is interested in gathering and the level of risk that

organizations can tolerate to obtain the maximum amount of data.

A honeypot outside the external firewall ( location 1 ) is useful for tracking

attempts to connect to unused IP addresses within the scope of the network. A honeypot

at this location does not increase the risk for the internal network. The danger

of having a compromised system behind the firewall is avoided. Further, because

the honeypot attracts many potential attacks, it reduces the alerts issued by the firewall

and by internal IDS sensors, easing the management burden. The disadvantage

of an external honeypot is that it has little or no ability to trap internal attackers,

especially if the external firewall filters traffic in both directions.

The network of externally available services, such as Web and mail, often

called the DMZ (demilitarized zone), is another candidate for locating a honeypot

( location 2 ). The security administrator must assure that the other systems in the

DMZ are secure against any activity generated by the honeypot. A disadvantage of

this location is that a typical DMZ is not fully accessible, and the firewall typically

blocks traffic to the DMZ the attempts to access unneeded services. Thus, the firewall

either has to open up the traffic beyond what is permissible, which is risky, or

limit the effectiveness of the honeypot.

A fully internal honeypot ( location 3 ) has several advantages. Its most important

advantage is that it can catch internal attacks. A honeypot at this location can

also detect a misconfigured firewall that forwards impermissible traffic from the

Internet to the internal network. There are several disadvantages. The most serious

of these is if the honeypot is compromised so that it can attack other internal

systems. Any further traffic from the Internet to the attacker is not blocked by the

firewall because it is regarded as traffic to the honeypot only. Another difficulty for

this honeypot location is that, as with location 2, the firewall must adjust its filtering

to allow traffic to the honeypot, thus complicating firewall configuration and potentially

compromising the internal network.

An emerging related technology is the use of honeyfiles, that emulate legitimate

documents with realistic, enticing names and possibly content. These documents

should not be accessed by legitimate users of a system, but rather act as bait

for intruders exploring a system. Any access of them is assumed to be suspicious

[WHIT13]. Appropriate generation, placement, and monitoring of honeyfiles is an

area of current research.

Snort is an open source, highly configurable and portable host-based or network-based

IDS. Snort is referred to as a lightweight IDS, which has the following characteristics:

• Easily deployed on most nodes (host, server, router) of a network

• Efficient operation that uses small amount of memory and processor time

• Easily configured by system administrators who need to implement a specific

security solution in a short amount of time

Snort can perform real-time packet capture, protocol analysis, and content searching

and matching. Snort can detect a variety of attacks and probes, based on a set of

rules configured by a system administrator.

A Snort installation consists of four logical components ( Figure 8.9 ):

• Packet decoder: The packet decoder processes each captured packet to

identify and isolate protocol headers at the data link, network, transport, and

application layers. The decoder is designed to be as efficient as possible and its

primary work consists of setting pointers so that the various protocol headers

can be easily extracted.

• Detection engine: The detection engine does the actual work of intrusion

detection. This module analyzes each packet based on a set of rules defined

for this configuration of Snort by the security administrator. In essence, each

packet is checked against all the rules to determine if the packet matches

the characteristics defined by a rule. The first rule that matches the decoded

packet triggers the action specified by the rule. If no rule matches the packet,

the detection engine discards the packet.

• Logger: For each packet that matches a rule, the rule specifies what logging

and alerting options are to be taken. When a logger option is selected, the logger

stores the detected packet in human readable format or in a more compact

binary format in a designated log file. The security administrator can then use

the log file for later analysis.

• Alerter: For each detected packet, an alert can be sent. The alert option in the

matching rule determines what information is included in the event notification.

The event notification can be sent to a file, to a UNIX socket, or to a

database. Alerting may also be turned off during testing or penetration studies.

Using the UNIX socket, the alert can be sent to a management machine

elsewhere on the network.

A Snort implementation can be configured as a passive sensor, which monitors

traffic but is not in the main transmission path of the traffic, or an inline sensor,

through which all packet traffic must pass. In the latter case, Snort can perform

intrusion prevention as well as intrusion detection. We defer a discussion of intrusion

prevention to Chapter 9 .

Snort uses a simple, flexible rule definition language that generates the rules used

by the detection engine. Although the rules are simple and straightforward to write,

they are powerful enough to detect a wide variety of hostile or suspicious traffic.

Each rule consists of a fixed header and zero or more options (Figure 8.10).

Table 8.3

Snort Rule Actions

The header has the following elements:

• Action: The rule action tells Snort what to do when it finds a packet that

matches the rule criteria. Table 8.3 lists the available actions. The last three

actions in the list (drop, reject, sdrop) are only available in inline mode.

• Protocol: Snort proceeds in the analysis if the packet protocol matches this field.

The current version of Snort (2.9) recognizes four protocols: TCP, UDP, ICMP,

and IP. Future releases of Snort will support a greater range of protocols.

• Source IP address: Designates the source of the packet. The rule may specify a

specific IP address, any IP address, a list of specific IP addresses, or the negation

of a specific IP address or list. The negation indicates that any IP address

other than those listed is a match.

• Source port: This field designates the source port for the specified protocol (e.g.,

a TCP port). Port numbers may be specified in a number of ways, including specific

port number, any ports, static port definitions, ranges, and by negation.

• Direction: This field takes on one of two values: unidirectional (->) or bidirectional

(<->). The bidirectional option tells Snort to consider the address/

port pairs in the rule as either source followed by destination or destination

followed by source. The bidirectional option enables Snort to monitor both

sides of a conversation.

• Destination IP address: Designates the destination of the packet.

• Destination port: Designates the destination port.

Following the rule header may be one or more rule options. Each option

consists of an option keyword, which defines the option; followed by arguments,

which specify the details of the option. In the written form, the set of rule options is

separated from the header by being enclosed in parentheses. Snort rule options are

separated from each other using the semicolon (;) character. Rule option keywords

are separated from their arguments with a colon (:) character.

Table 8.4

Examples of

Snort Rule Options

(Table can be found on page 283 in textbook.)

There are four major categories of rule options:

• Meta-data: Provide information about the rule but do not have any affect during

detection

• Payload: Look for data inside the packet payload and can be interrelated

• Non-payload: Look for non-payload data

• Post-detection: Rule-specific triggers that happen after a rule has matched a

packet

Table 8.4 provides examples of options in each category.

Summary

Host-based intrusion detection

Data sources and sensors

Anomaly HIDS

Signature or heuristic HIDS

Distributed HIDS

Network-based intrusion detection

Types of network sensors

NIDS sensor deployment

Intrusion detection techniques

Logging of alerts

Example system: Snort

Snort architecture

Snort rules

Intruders

Intruder behavior

Intrusion detection

Basic principles

The base-rate fallacy

Requirements

Analysis approaches

Anomaly detection

Signature or heuristic detection

Distributed or hybrid intrusion detection

Intrusion detection exchange format

Honeypots

Chapter 8 summary.

Figure 8.1 Profiles of Behavior of Intruders and Authorized Users

overlap in observed or expected behavior

profile of intruder behavior

profile of authorized user

behavior

Measurable behavior parameter

average behavior of intruder

average behavior of authorized user

Probability density function

Figure 8.1 Profiles of Behavior of Intruders and Authorized Users

overlap in observed

or expected behavior

profile of

intruder behavior

profile of

authorized user

behavior

Measurable behavior

parameter

average behavior

of intruder

average behavior

of authorized user

Probability

density function

Table 8.2 Linux System Calls and Windows DLLs Monitored

(a) Ubuntu Linux System Calls

accept, access, acct, adjtime, aiocancel, aioread, aiowait, aiowrite, alarm, async_daemon, auditsys, bind, chdir, chmod, chown, chroot, close, connect, creat, dup, dup2, execv, execve, exit, exportfs, fchdir, fchmod, fchown, fchroot, fcntl, flock, fork, fpathconf, fstat, fstat, fstatfs, fsync, ftime, ftruncate, getdents, getdirentries, getdomainname, getdopt, getdtablesize, getfh, getgid, getgroups, gethostid, gethostname, getitimer, getmsg, getpagesize, getpeername, getpgrp, getpid, getpriority, getrlimit, getrusage, getsockname, getsockopt, gettimeofday, getuid, gtty, ioctl, kill, killpg, link, listen, lseek, lstat, madvise, mctl, mincore, mkdir, mknod, mmap, mount, mount, mprotect, mpxchan, msgsys, msync, munmap, nfs_mount, nfssvc, nice, open, pathconf, pause, pcfs_mount, phys, pipe, poll, profil, ptrace, putmsg, quota, quotactl, read, readlink, readv, reboot, recv, recvfrom, recvmsg, rename, resuba, rfssys, rmdir, sbreak, sbrk, select, semsys, send, sendmsg, sendto, setdomainname, setdopt, setgid, setgroups, sethostid, sethostname, setitimer, setpgid, setpgrp, setpgrp, setpriority, setquota, setregid, setreuid, setrlimit, setsid, setsockopt, settimeofday, setuid, shmsys, shutdown, sigblock, sigpause, sigpending, sigsetmask, sigstack, sigsys, sigvec, socket, socketaddr, socketpair, sstk, stat, stat, statfs, stime, stty, swapon, symlink, sync, sysconf, time, times, truncate, umask, umount, uname, unlink, unmount, ustat, utime, utimes, vadvise, vfork, vhangup, vlimit, vpixsys, vread, vtimes, vtrace, vwrite, wait, wait3, wait4, write, writev

(b) Key Windows DLLs and Executables

comctl32 kernel32 msvcpp msvcrt mswsock ntdll ntoskrnl user32 ws2_32

Table 8.2 Linux System Calls and Windows DLLs Monitored

(a) Ubuntu Linux System Calls

accept, access, acct, adjtime, aiocancel, aioread, aiowait, aiowrite, alarm, async_daemon,

auditsys, bind, chdir, chmod, chown, chroot, close, connect, creat, dup, dup2, execv, execve,

exit, exportfs, fchdir, fchmod, fchown, fchroot, fcntl, flock, fork, fpathconf, fstat, fstat,

fstatfs, fsync, ftime, ftruncate, getdents, getdirentries, getdomainname, getdopt, getdtablesize,

getfh, getgid, getgroups, gethostid, gethostname, getitimer, getmsg, getpagesize,

getpeername, getpgrp, getpid, getpriority, getrlimit, getrusage, getsockname, getsockopt,

gettimeofday, getuid, gtty, ioctl, kill, killpg, link, listen, lseek, lstat, madvise, mctl, mincore,

mkdir, mknod, mmap, mount, mount, mprotect, mpxchan, msgsys, msync, munmap,

nfs_mount, nfssvc, nice, open, pathconf, pause, pcfs_mount, phys, pipe, poll, profil, ptrace,

putmsg, quota, quotactl, read, readlink, readv, reboot, recv, recvfrom, recvmsg, rename,

resuba, rfssys, rmdir, sbreak, sbrk, select, semsys, send, sendmsg, sendto, setdomainname,

setdopt, setgid, setgroups, sethostid, sethostname, setitimer, setpgid, setpgrp, setpgrp,

setpriority, setquota, setregid, setreuid, setrlimit, setsid, setsockopt, settimeofday, setuid,

shmsys, shutdown, sigblock, sigpause, sigpending, sigsetmask, sigstack, sigsys, sigvec,

socket, socketaddr, socketpair, sstk, stat, stat, statfs, stime, stty, swapon, symlink, sync,

sysconf, time, times, truncate, umask, umount, uname, unlink, unmount, ustat, utime, utimes,

vadvise, vfork, vhangup, vlimit, vpixsys, vread, vtimes, vtrace, vwrite, wait, wait3, wait4,

write, writev

(b) Key Windows DLLs and Executables

comctl32

kernel32

msvcpp

msvcrt

mswsock

ntdll

ntoskrnl

user32

ws2_32

Central Manager

LAN Monitor Host Host

Agent module

Router

Internet

Figure 8.2 Architecture for Distributed Intrusion Detection

Manager module

Central Manager

LAN Monitor

Host Host

Agent

module

Router

Internet

Figure 8.2 Architecture for Distributed Intrusion Detection

Manager

module

OS audit information

Alerts

Modifications

Query/ response

Notable activity;

Signatures; Noteworthy

sessions

Host audit record (HAR)

Figure 8.3 Agent Architecture

Filter for security interest

Reformat function

OS audit function

Analysis module

Templates

Central manager

Logic module

OS audit

information

Alerts

Modifications

Query/

response

Notable

activity;

Signatures;

Noteworthy

sessions

Host audit record (HAR)

Figure 8.3 Agent Architecture

Filter for

security

interest

Reformat

function

OS audit

function

Analysis

module

Templates

Central

manager

Logic

module

NIDS sensor

Figure 8.4 Passive NIDS Sensor

Network traffic

Monitoring interface (no IP, promiscuous mode)

Management interface (with IP)

NIDS

sensor

Figure 8.4 Passive NIDS Sensor

Network traffic

Monitoring interface

(no IP, promiscuous mode)

Management interface

(with IP)

Internet

workstation networks

external firewall

internal firewall

LAN switch or router

Figure 8.5 Example of NIDS Sensor Deployment

internal server and data resource

networks

service network (Web, Mail, DNS, etc.)

Internet

workstation

networks

external

firewall

internal

firewall

internal

firewall

LAN switch

or router

LAN switch

or router

LAN switch

or router

Figure 8.5 Example of NIDS Sensor Deployment

internal server

and data resource

networks

service network

(Web, Mail, DNS, etc.)

Distributed detection and inference

Platform policies

Figure 8.6 Overall Architecture of an Autonomic Enterprise Security System

Platform policies

Adaptive feedback based policies

Network policies

PEP events

PEP = policy enforcement point DDI = distributed detection and inference

DDI events

Summary events

Platform events

Collaborative policies

goss ip

Distributed detection

and inference

Platform

policies

Figure 8.6 Overall Architecture of an Autonomic Enterprise Security System

Platform

policies

Platform

policies

Adaptive feedback

based policies

Network

policies

PEP

events

PEP = policy enforcement point

DDI = distributed detection and infer ence

DDI

events

Summary

events

Platform

events

Platform

events

Collaborative

policies

Data

sour ce

Sens or

Ana lyze

Man ager

Response

Activity

Event

Alert

Notification

Operator

Administrator

Security policy

Figure 8.7 Model For Intrusion Detection Message Exchange

Security policy

Data

sour

Sensor

Analyzer

Manager

Response

Activity

Event

Alert

Notification

Operator

Administrator

Security

policy

Figure 8.7 Model For Intrusion Detection Message Exchange

Security

policy

Internet

External firewall

Honeypot

LAN switch or router

Figure 8.8 Example of Honeypot Deployment

Internal network

Service network (Web, Mail, DNS, etc.)

Internet

External

firewall

Honeypot

LAN switch

or router

LAN switch

or router

Figure 8.8 Example of Honeypot Deployment

Internal

network

Service network

(Web, Mail, DNS, etc.)

Packet Decoder

Figure 8.9 Snort Architecture

Detection Engine

Log

Alert

Packet Decoder

Figure 8.9 Snort Architecture

Detection

Engine

Log

Alert

Action Protocol Source

IP address Source

Port Direction

Dest IP address

Dest Port

(a) Rule Header

Option Keyword

Option Arguments

• • •

(b) Options

Figure 8.10 Snort Rule Formats

Action Protocol

Source

IP address

Source

Port

Direction

Dest

IP address

Dest

Port

(a) Rule Header

Option

Keyword

Option

Arguments

• • •

(b) Options

Figure 8.10 Snort Rule Formats

Action Description alert Generate an alert using the selected alert method, and then log the packet. log Log the packet.

pass Ignore the packet. activate Alert and then turn on another dynamic rule. dynamic Remain idle until activated by an activate rule , then act as a log rule.

drop Make iptables drop the packet and log the packet.

reject Make iptables drop the packet, log it, and then send a TCP reset if the protocol is TCP or an ICMP port unreachable message if the protocol is UDP.

sdrop Make iptables drop the packet but does not log it.

Action Description

alert Generate an alert using the selected alert method, and then log the packet.

log Log the packet.

pass Ignore the packet.

activate Alert and then turn on another dynamic rule.

dynamic Remain idle until activated by an activate rule , then act as a log rule.

drop Make iptables drop the packet and log the packet.

reject

Make iptables drop the packet, log it, and then send a TCP reset if the

protocol is TCP or an ICMP port unreachable message if the protocol is

UDP.

sdrop Make iptables drop the packet but does not log it.

meta-data

msg Defines the message to be sent when a packet generates an event.

reference Defines a link to an external attack identification system, which provides additional information.

classtype Indicates what type of attack the packet attempted.

payload

content Enables Snort to perform a case-sensitive search for specific content (text and/or binary) in the packet payload.

depth Specifies how far into a packet Snort should search for the specified pattern. Depth modifies the previous content keyword in the rule.

offset Specifies where to start searching for a pattern within a packet. Offset modifies the previous content keyword in the rule.

nocase Snort should look for the specific pattern, ignoring case. Nocase modifies the previous content keyword in the rule.

non-payload

ttl Check the IP time-to-live value. This option was intended for use in the detection of traceroute attempts.

id Check the IP ID field for a specific value. Some tools (exploits, scanners and other odd programs) set this field specifically for various purposes, for example, the value 31337 is very popular with some hackers.

dsize Test the packet payload size. This may be used to check for abnormally sized packets. In many cases, it is useful for detecting buffer overflows.

flags Test the TCP flags for specified settings.

seq Look for a specific TCP header sequence number.

icmp-id Check for a specific ICMP ID value. This is useful because some covert channel programs use static ICMP fields when they communicate. This option was developed to detect the stacheldraht DDoS agent.

post-detection

logto Log packets matching the rule to the specified filename.

session Extract user data from TCP Sessions. There are many cases where seeing what users are typing in telnet, rlogin, ftp, or even web sessions is very useful.