Evaluate Information Risk and Assurance to Defend Information Technology

Understanding Taxonomy of Cyber Risks for Cybersecurity Insurance of Financial Industry in

Cloud Computing

Sam Adam Elnagdy1, Meikang Qiu2∗, Keke Gai3

Abstract—The dramatical development of Web-based tech- nology has been empowering enormous change in various domains. Cloud-based solutions have remarkably widened business models with multiple value creation channels. The financial industry is a major beneficiary of leveraging these emerging new technologies, such as big data and cloud-related services. This great changing trend has also led to a great concern in cybersecurity. Under this background, cybersecu- rity insurance is a growing domain in the financial industry. However, cybersecurity insurance industry also encounters a variety of cyber concerns while the Web-based approaches are applied. This paper focuses on this issue and review a broad scope of materials to gain a deep understanding of taxonomy of cyber security risks for cybersecurity insurance. The findings of this work can guide the cybersecurity insurance practitioners to avoid as much risk as possible as well as create potential solutions to the possible risks.

Index Terms—Cyber security, risk taxonomy, cybersecurity insurance, financial industry, cloud computing

I. INTRODUCTION

Contemporarily, cybersecurity risks have been consid-

ered a significant issue in multiple fields. Considering a

higher-level security requirement, the financial industry also

has a critical concern when Financial Service Institutions (FSIs) is applying networking-based solutions [1]–[3]. A

remarkable growing demand of storing sensitive data in

digital forms has enabled many implementations of new

technologies, such as cloud computing [4], big data [5],

and Internet-of-Things (IoT). This development trend has led to distributed storage and a broad utilization of virtual

machines [6]. Without the geographical limitations, data can

be transmitted, shared, stored, and operated over the wired

or wireless network systems. This ongoing change not only

empowers financial firms’ capability in value creations but

also brings a variety of challenges to financial practitioners

and enterprises [7].

1 S. Elnagdy is with Department of Computer Science, Pace University, New York, NY, 10038, [email protected];

2 M. Qiu is with Department of Computer Science, Pace University, New York, NY 10038, USA, [email protected];

3 K. Gai is with Department of Computer Science, Pace University, New York, NY 10038, USA, [email protected];

∗ M. Qiu is the corresponding author of this paper. Email address: [email protected].

∗∗ This work is supported by NSF CNS-1457506 and NSF CNS- 1359557. (Prof. M. Qiu).

There are numerous existing cyber risks threatening FSIs

that are using networking-related solutions [8]. The fun-

damental of avoiding and solving these threats is to be

aware of those cyber risks [9]. However, understanding

cyber risks taxonomy is a challenging task due to the

high complexity of the entity-entity relations and the broad

crossed disciplines. This paper addresses this issue and

accomplishes a survey that focuses on the cybersecurity

issues related to cybersecurity insurance in the financial

industry.

The significance of our research work is solid and

straightforward. Most current implemented cybersecurity

insurance solutions are struggling with a few obstructions,

such as immature information sharing mechanism, unde-

veloped cyber incident analytics, and lack of effective risk

management for cybersecurity [10]. These issues can result

in arguable definitions of itemizing cybersecurity insurance

coverages or lead to ambiguous representations of cyber

incidents’ contexts. Therefore, a common solution to con-

temporary cybersecurity insurance in the financial industry

is simply cover most aspects in cybersecurity, which also

simply causes remarkable high costs. For achieving lower

costs, a granular awareness of the cyber risk facts in

the financial industry is required and urgent. Therefore,

our research work is important for both researchers and

practitioners in the domain of cybersecurity insurance.

Fig. 1 represents a mapping structure of our survey

work. Three vital aspects of cybersecurity insurance are

covered in our work, namely risk management, taxonomy,

and techniques. A few sub-objects are involved in each

aspect. This paper follows the structure shown in Fig. 1

to concisely represent the knowledge structure at the target

field.

In addition, the main contributions of this paper are:

1) We build up a knowledge structure of cybersecurity

insurance in order to assist practitioners to obtain a

granular cognition of the knowledge. A deep awareness

of cybersecurity insurance will provide a potential of

reducing the insurance cost.

2) The findings and discussions in this paper provide

researchers with references and a guideline of future

research work.

2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing

978-1-5090-0946-6/16 $31.00 © 2016 IEEE

DOI 10.1109/CSCloud.2016.46

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Fig. 1: Mapping structure of the survey work

We organize the rest of this paper by structurializing the

knowledge structure concerning the cybersecurity insurance.

Section II reviews crucial risk management methods in

cybersecurity insurance. Next, Section III converges main

techniques contemporarily used in cybersecurity insurance.

Furthermore, we summarize and categorize cyber incidents

taxonomy of cybersecurity insurance in Section IV. Finally,

we provide the conclusions in Section V.

II. RISK MANAGEMENT IN CYBERSECURITY

INSURANCE

A. Risk Management Applications

Risk management is a significant component of ensuring

financial safe, which is also an important aspect of cyber-

security insurance [11]. The implementations of financial

risk management have played a critical role in securing

financial operations, because the risk management methods

will determine the practical effects from risk predictions to

solution designs. The target risk management model needs

to be validated by financial analysts and data experts in

order to ensure the flexibility and correctness of the applied

model [12]–[14].

Moreover, with the development of the Internet technolo-

gies, the coverage of the risk management is also attached

to the cyber threats analyses and model validations [15].

Focusing on current financial firms, many cyber risks are

categorized into three layers, including strategical, tactical,

and operational layers [16], [17]. Each layer requires spe-

cific demands, management method, and designed solutions.

For the purpose of protecting information, for instance, there

are mainly three aspects that are needed to be considered,

which include confidentiality, integrity, and availability [18].

Furthermore, there are many techniques currently being

applied in financial risk management domains. For instance,

establishing a secure business operation system is an effi-

cient option for most financial companies to increase the

security level without adding much hardware facilities [19],

[20]. Most contemporary popular analysis approaches derive

from statistics, which concentrate on the structured data

[21], [22]. This situation is switching to a broader appli-

cation scope due to the emergence of big data techniques.

Semi-structured and unstructured data have been brought

into the financial cyber risk models in recent researches

[23], [24]. The higher-level data complexity required the

optimizations of big data [25].

B. Major Concerns of Cybersecurity Insurance

According to the Homeland Security Department’s report,

there are a number of major concerns in current cybersecu-

rity insurance [26]. These concerns can hardly build up a

healthy relationship with the coverage satisfaction.

First, establishing a comprehensive cybersecurity insur-

ance system is challenging because rare terms can be

derived from the existing commercial insurance policies.

This phenomenon results in a few negative effects, such as a

wide robust coverage and ambiguous obligation statements.

Second, applying current cybersecurity insurance will bring

a heavy financial burden to FSIs due to the non-classified

insurance policies with a broad coverage scope. Most cyber-

security insurance policies try to cover a larger sized cyber

incidents in order to avoid the confusing obligations. Finally,

hardware infrastructure’s physical damages may provide

adversaries with attack opportunities; however, this growing

risks are rarely addressed by current cybersecurity insurance

policies due to the complexity of identifying the causality

of the cyber incidents.

Moreover, Pal et al. [27] accomplished an investigation

in the effects of cybersecurity insurance from the mar-

keting reaction perspective. According to this work, the

authors addressed two market types, namely monopolistic

and competitive cybersecurity insurance markets. Based on

a broad data gathering, there were two findings of this

research. First, there was no sufficient diversity between

two market types when the contract discriminations among

insurance clients were not applied, even though it seemed

that an equilibrium point existed. Second, applying contract

discrimination in monopoly markets could improve network

security. This research implied that markets had limited

impacts on reducing concerns of cybersecurity insurance.

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C. Major Challenges

Identifying the obligations is a complicated issue in which

a large volume of interconnections and interrelations exist

between entities. The cause effect relationships between

cyber incidents and cyber risks are hard to be accurately

defined, because there are rarely one-to-one relations. Most

situations represent a multi-to-multi relation. This is also

one of the reasons why current cybersecurity insurance

policies mainly offer broad service scopes.

Moreover, an ambiguous representation of cybersecurity

entities can also cause confusions when direction and in-

direct relations synchronously exist. Sometimes, a chain

reaction occurs such that the cause effect relations are

difficult to be identified from the intercrossed complex entity

networks. The cause of the chain reactions could be the data

destruction, hacking attacks, Denial of Service (DoS), and illegal adversaries [28].

Furthermore, the boundary between hardware and soft-

ware is not so clear in cybersecurity insurance domain [29],

[30]. The reason why the challenge exists is because any

part’s damage may weaken the other part. For example,

physical infrastructure damages will increase the chance of

being attacked by adversaries at data transmission layer or

database layer. The indirect relations between infrastructures

or applications can produce unanticipated vulnerabilities. In

a cloud system, a serious data bleach can be created if

adversaries can successfully map the cloud infrastructure.

Next, some previous researches proved that one of the

vital challenges of cybersecurity is human-related operating

errors [31]. The argument of this perspective stated that

many system threats took place when end users open some

phishing document by accident or have improper operations

without effective protection mechanism.

III. MAIN TECHNIQUES IN CYBERSECURITY

INSURANCE

We focus on three popular techniques that are forming

the mainstream of cybersecurity insurance in this section.

Three techniques include big data, cloud computing, and

data analytic techniques.

A. Big Data in Cybersecurity Insurance

The technology of big data is an emerging term that

represents the techniques gaining valuable information from

large volume of data [32]–[34]. The implementations of big

data can be found in a large scope of fields, which have

been also addressed by recent researches [35]. For example,

using big data techniques can efficiently avoid fraud or

counterfeit information over the e-commerce platform [36].

The analysis results of big data can be used as a reference

for detecting improper commerical behaviors. In addition,

Monte Carlo simulation is often considered an effective

method of obtaining results from a dramatical large sized

dataset [37].

Moreover, improving efficiency of big data analysis has

been explored by recent researches as well [38]. For exam-

ple, a classification algorithm was proposed to increase the

classification performance using ensemble feature selections

for imbalanced-class dataset [39]. In the financial industry,

meta-analytics methods were proved as an effective method

for forecasting risks when meta-regression was applied

[40]. Another research focusing on optimizing computing

efficiency proposed a scheme using pre-cache techniques to

achieve high performance of telehealth systems [41].

In addition, one major characteristic of current cyberse-

curity insurance is that most analyses mainly address the

structured data. Many data mining techniques can simply

dig information from a pool of structured data that derive

from different data sources [42], [43]. This is also a major

concern in the privacy protection domain. For instance,

financial customers’ accounts consist of abundant sensitive

information. Sometimes, FSIs need to share some data with

the third party for the purpose of data analysis. In order to

protect financial customers’ privacy, FSIs usually provide

only partial dataset rather than the whole dataset. This

approach is facing a great challenge due to the broad imple-

mentations of data mining in big data. Sensitive information

can be gained when the third parts use data mining and data

integration techniques. The privacy leakage can take place

when the third party combines various data components

from the same data source.

B. Cloud Computing Implementations

Cloud computing is another emerging technical term

that provides a flexible remote computing resource sharing

method. Platform-as-a-Service (PaaS) is one of popular service models that has been broadly accepted by many

service offering organizations [44], [45]. The performance

enhancement has been paid sufficient attentions in various

perspectives [46], [47]. Many algorithms have been pro-

posed in order to increase cloud systems’ performance, such

as genetic algorithm [48], [49], dynamic programming [50]–

[52], and Greedy algorithm [53], [54].

Considering privacy and security issues, many previous

researches had explored higher-level cloud-based protec-

tions for financial firms [43], [55]. For example, recent

research has proved that using attribute-based semantic

access controls could proactively secure data owners’ data

in mobile cloud computing [56]. In this method, data

attributes are emphasized and it aimed to avoid cyber risks

by identifying data users’ statuses. Similarly, multimedia big

data in cloud computing could also secured by identifying

the data users’ behaviors and standings [57].

Moreover, Gai et al. [58] converge most recent intru-

sion detection techniques for mobile cloud computing in

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heterogeneous 5G. The findings of this search also include

summarizing intrusion types in wireless networks. Another

survey was done by Thakur et al. [59] who focused on cyber

threats and security models. These prior investigations pro-

vide theoretical fundamental for categorizing cyber threats.

C. Risk Analytics Techniques in Cybersecurity Insurance

Risk analytics techniques have been dramatically im-

proved in recent years. Most current analytics techniques

derive from a few classic analytic tools, such as Bayesian Tree, Associated Rules, and Regression. For example, se- curity risk management could use dynamic bayesian attack

graphs to reduce the impact of adversaries [60].

Moreover, the emerging mainstream of big data is shift-

ing many domains from analytics-oriented to intelligent-

oriented approaches. For example, Zeng and Lusch [61]

argued that future business approaches would swift from

transaction-based to ecosystems-based models. They em-

phasizes the information gains from the systemic-based data

pools by which the rich knowledge pools are generated.

Similar insights were also addressed by other scholars who

attempted to explore the efficient information acquisitions

[62], [63]. New terms have been created for describing this

new trend, such as data-centric or data-intensive approach

[64].

IV. CYBER INCIDENTS TAXONOMY IN CYBERSECURITY

INSURANCE

A. Taxonomy Methods

Taxonomy is an effective approach for organizing the

knowledge body by classifying entities. In the field of

cybersecurity insurance, the vital issue is finding out the

methods of creating taxonomy of cybersecurity, which is

the fundamental of organizing cyber incidents and relevant

technical issues in a group-based manner. Many prior re-

searches have proved that using taxonomy-based approaches

can categorize objects for reaching certain purposes [65],

[66]. The method of the taxonomy usually depends on the

perspective defining the variety.

Next, many perspectives have been addressed by the prior

researches in cybersecurity. One approach is to categorize

security issues into two main perspectives, including interior

and exterior perspectives [67]. For instance, cyber attackers

can be grouped into two categories, which are insider and

outsider attackers. This method is often used for analyzing

risks related to user attributes and infrastructure utilizations.

Another method of taxonomy is to use attributes or

features to diversify objects. This method usually applies

the data mining techniques to extract the characteristics of

data, entities, or objects. For example, analyzing correlations

among a group of interrelated objects is an approach for

classifying the target, such as networking traffics [68] and

e-commercial performance [69]. This goal can be achieved

by using a variety of machine learning techniques, such as

Naive Bayes predictions [70] or decision trees.

Moreover, knowledge graph is an approach representing

the relations between entities in the system, which has

been paid a high attention by researchers recently. Combing

knowledge graph with ontology is an efficient approach

for representing knowledge and managing information [71].

One major advantage of using ontology-based solution

with combing knowledge graphs is that relations between

entities can be defined by using updated languages, such as

Web Ontology Language (OWL). Utilizing this advantage is considered an effective alternative for identifying the

complicated direct or indirect relations between entities

[72], [73]. Performances can be also improved while other

techniques are integrated, such as cloud computing [74].

B. Semantic Web

Semantic web is an efficient Web platform supporting

ontology-based solutions [75]. Many prior researches ex-

plored the implementations in practice, from telehealth [76]

to financial services [56]. Recent researches also addressed

the integrated syntax and semantic validations for service

computing [77]. Many researches have proved that using

semantic Web techniques can enable to build up quality

service models with linked services within a network en-

vironment [78], [79]. Integrating Web services with other

computing resources can empower the whole computing

system and provision flexible service delivery methods [80].

Moreover, ontology-based solutions are often integrated

with semantic Web to solve the security and privacy is-

sues. Therefore, using semantic Web and ontology-based

knowledge representation can be approach of identifying

the complex direct and indirect relations in cybersecurity

insurance, which is a vital component of understanding

cybersecurity insurance’s risks and concerns.

V. CONCLUSIONS AND FUTURE WORK

This paper represented a survey in taxonomy of cyberse-

curity risks in the domain of cybersecurity insurance. The

literature review covered three crucial aspects of cyberse-

curity insurance, including risk management, main active

techniques, and cyber incidents. An empirical study was

shown in this work as well. Our findings could be used as

a reference for further research in cybersecurity insurance.

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