cyber security challenges for society literature review

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Cyber security: challenges for society-literature review.

Cyber security is the activity of protecting information and information systems (networks, computers, data bases, data centers and applications) with appropriate procedural and technological security measures. Firewalls, antivirus software, and other technological solutions for safeguarding personal data and computer networks are essential but not sufficient to ensure security. As the authors’ nation rapidly building its Cyber-Infrastructure, it is equally important that they educate their population to work properly with this infrastructure. Cyber-Ethics, Cyber-Safety, and Cyber-Security issues need to be integrated in the educational process beginning at an early age.

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Cyber security: challenges for society- literature review

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Applications of Digital Watermarking to Cyber Security (Cyber Watermarking)

Security in cyber crime, a detailed study on cyber security frameworks, origin of cyber warfare and how the espionage changed: a historical overview, concerns about cybersecurity: the implications of the use of ict for citizens and companies, improving cyber security and mission assurance via cyber preparedness (cyber prep) levels, a cyber era approach for building awareness in cyber security for educational system in india, an intrusion-detection model, using cp-nets as a guide for countermeasure selection, related papers (5), measures to design secure cyber-physical things, recommendations on future operational environments command control and cyber security., effectively integrating physical and cyber security: wins international best practice guide 4.11., latest trends and future directions of cyber security information systems, emerging cyber security threats in organization, trending questions (1).

Cyber-Ethics, Cyber-Safety, and Cyber-Security issues need to be integrated in the educational process beginning at an early age.

Evolution of Cybersecurity Concerns: A Systematic Literature Review

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• Franco E Yin R Sankaranarayanan B (2024) Building Critical Statewide Cybersecurity Capabilities: The Wisconsin Model Proceedings of the 25th Annual International Conference on Digital Government Research 10.1145/3657054.3657083 (224-231) Online publication date: 11-Jun-2024 https://dl.acm.org/doi/10.1145/3657054.3657083
• Handri E Indra Sensuse D Tarigan A (2024) Developing an Agile Cybersecurity Framework With Organizational Culture Approach Using Q Methodology IEEE Access 10.1109/ACCESS.2024.3432160 12 (108835-108850) Online publication date: 2024 https://doi.org/10.1109/ACCESS.2024.3432160
• Prümmer J (2024) The Role of Cognition in Developing Successful Cybersecurity Training Programs – Passive vs. Active Engagement Augmented Cognition 10.1007/978-3-031-61572-6_13 (185-199) Online publication date: 29-Jun-2024 https://dl.acm.org/doi/10.1007/978-3-031-61572-6_13

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A systematic literature review of how cybersecurity-related behavior has been assessed

Information and Computer Security

ISSN : 2056-4961

Article publication date: 20 April 2023

Issue publication date: 30 October 2023

Cybersecurity attacks on critical infrastructures, businesses and nations are rising and have reached the interest of mainstream media and the public’s consciousness. Despite this increased awareness, humans are still considered the weakest link in the defense against an unknown attacker. Whatever the reason, naïve-, unintentional- or intentional behavior of a member of an organization, the result of an incident can have a considerable impact. A security policy with guidelines for best practices and rules should guide the behavior of the organization’s members. However, this is often not the case. This paper aims to provide answers to how cybersecurity-related behavior is assessed.

Design/methodology/approach

Research questions were formulated, and a systematic literature review (SLR) was performed by following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. The SLR initially identified 2,153 articles, and the paper reviews and reports on 26 articles.

The assessment of cybersecurity-related behavior can be classified into three components, namely, data collection, measurement scale and analysis. The findings show that subjective measurements from self-assessment questionnaires are the most frequently used method. Measurement scales are often composed based on existing literature and adapted by the researchers. Partial least square analysis is the most frequently used analysis technique. Even though useful insight and noteworthy findings regarding possible differences between manager and employee behavior have appeared in some publications, conclusive answers to whether such differences exist cannot be drawn.

Research limitations/implications

Research gaps have been identified, that indicate areas of interest for future work. These include the development and employment of methods for reducing subjectivity in the assessment of cybersecurity-related behavior.

Originality/value

To the best of the authors’ knowledge, this is the first SLR on how cybersecurity-related behavior can be assessed. The SLR analyzes relevant publications and identifies current practices as well as their shortcomings, and outlines gaps that future research may bridge.

• Cybersecurity
• Human behavior
• Assessment process

Kannelønning, K. and Katsikas, S.K. (2023), "A systematic literature review of how cybersecurity-related behavior has been assessed", Information and Computer Security , Vol. 31 No. 4, pp. 463-477. https://doi.org/10.1108/ICS-08-2022-0139

Emerald Publishing Limited

Copyright © 2023, Kristian Kannelønning and Sokratis K. Katsikas.

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial & non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

The importance of information systems (IS) security has increased because the number of unwanted incidents continues to rise in the last decades. Several avenues or paths can be taken by organizations to secure their IS. Technical solutions like whitelisting, firewalls and antivirus software enhance security, but research has shown that when people within the organization do not follow policies and guidelines these technical safeguards will be in vain.

1.1 Aims of the paper

Of the 26 articles included in this review, 10 used some variations of the phrase humans are the weakest link in cybersecurity in either the abstract or introduction. All articles cite multiple authors, accumulating a significant number of previous works, all claiming the same statement. One might agree with Kruger et al. (2020) that it is common knowledge that humans are the weakest link in information security.

Given the premise that humans are the weakest link and the acknowledgment that technology cannot be the single solution for security ( McCormac et al. , 2017 ), research should investigate how organizations can assess the cybersecurity-related behavior of their employees. Identifying, evaluating and summarizing the methods and findings of all relevant literature resources addressing the issue, thereby systematizing the available knowledge and making it more accessible to researchers, while also identifying relevant research gaps, are the aims of this systematic literature review (SLR).

1.2 Background

Recent years have shown that cyberattacks are a global issue, such as the extensive power outage causing a blackout across Argentina and Uruguay in 2019 ( Kilskar, 2020 ). In January 2018, nearly 3 million, or roughly 50% of the Norwegian population’s medical records, were compromised by a cyberattack. Threats can vary from viruses, worms, trojan horses, denial of service, botnets, man-in-the-middle and zero-day ones ( Pirbhulal et al. , 2021 ). The above-listed threats include technical terms with a distinctive flair and uniqueness that is hard to comprehend for employees without a technical background. Moreover, most information security issues are complicated and fully understanding them requires advanced technical knowledge.

Definition of information security;

information security objectives or the framework for setting information security objectives;

principles to guide all activities relating to information security;

commitment to satisfy applicable requirements related to information security;

commitment to continual improvement of the information security management system;

assignment of responsibilities for information security management to defined roles; and

procedures for handling exemptions and exceptions. ( ISO, 2022 )

The extent to which an employee is aware of and complies with information security policy defines the extent of their information security awareness (ISA). ISA is critical in mitigating the risks associated with cybersecurity and is defined by two components, namely, understanding and compliance . Compliance is the employees’ commitment to follow best-practice rules defined by the organization ( Reeves et al. , 2020 ). Ajzen (1991) defines a person’s intention to comply as the individual’s motivation to perform a described behavior. The intention to comply captures the motivational factors that influence behavior. As a general rule, the stronger the effort, the willingness to perform a behavior, the more likely it will be performed.

Several frameworks or theories can be applied to research human behavior. For cybersecurity, behavior can be viewed through lenses and theories borrowed from disciplines such as criminology (e.g. deterrence theory), psychology (e.g. theory of planned behavior) and health psychology (e.g. protection motivation theory) ( Moody et al. , 2018 ; Herath and Rao, 2009 ). The most commonly used models in the context of cybersecurity are the general deterrence theory, the theory of planned behavior and the protection motivation theory ( Alassaf and Alkhalifah, 2021 ).

Staff’s attitude and awareness can pose a security problem. In those settings, it is relevant to consider why the situation exists and what can be done about it. In many cases, a key reason will be the limited extent to which security is understood, accepted and practiced across the organization ( Furnell and Thomson, 2009 ). As a mitigating step toward compliance, decision-makers will need guidance on achieving compliance and discouraging misuse when developing information security policies ( Sommestad et al. , 2014 ). Therefore, the ability to assess behavior is a prerequisite for decision-makers in their quest to develop the organizations’ information security policies. The development and responsibility for implementing policies lie within the purview of management ( Höne and Eloff, 2002 ). Accordingly, understanding the differences in cybersecurity-related behavior between management and employees will benefit the development of more secure organizations.

1.3 Structure of the paper

The rest of this paper is organized as follows: Section 2 describes the methodology for conducting the SLR; the research questions; the record search process; and the assessment criteria. In Section 3, the results and the findings are presented. A discussion of the findings is presented in Section 4. Section 5 summarizes our conclusions and outlines directions for future research.

This section discusses the fundamental stages of conducting an SLR. The SLR constructs are obtained by following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement ( Page et al. , 2021 ) and ( Fink, 2019 ; Weidt and Silva, 2016 ).

The foremost step is to investigate if a similar review has already been conducted. Searching for and studying other reviews help refine both research questions and search strings. The search did not discover any similar reviews. Keywords, search strings and research questions were collected and categorized in a literature index tool and used to optimize search strings and verify that this review’s chosen research questions are relevant and valuable to the body of knowledge.

A research review is explicit about the research questions, search strategy, inclusion and exclusion criteria, data extraction method and steps taken for analysis. Research reviews are, unlike subjective reviews, comprehensible and easily reproducible ( Fink, 2019 ). The remainder of this section elaborates on the components of the performed SLR.

2.1 Research questions

How is cybersecurity-related behavior assessed?

Are there differences between manager and employee behavior in a cybersecurity context?

2.2 Record searching process

Various search strings were used in this SLR, depending on the database. The keywords were kept unchanged, but the syntax of each database differs; hence, the search strings have minor differences. This study includes the following databases: Scopus, IEEE, Springer, Engineering Village, ScienceDirect and ACM. In some form of syntax, the keywords (exact and stemmed words) were used: Cyber, Security, Information, policy, compliance, measure, behavior. As an example, the following is the search used in Scopus: TITLE-ABS-KEY ((information AND security AND policy OR information AND security AND compliance OR policy AND compliance) AND (information AND security AND behavior)) AND PUBYEAR > 2001. To increase the precision of the searches, title, abstract and keywords were used as a limiter in all the databases.

2.3 Assessment criteria

studies from organization reports, guidelines, technical opinion reports;

research design – exclude reviews, editorials and testimonials, as using secondary data (data from other reviews, etc.) would make this review a tertiary one; and

nonresearch literature.

written in English;

published in 2001–2022;

original studies using theoretical or empirical data; and

studies published in Journals, Conference Proceedings and books/book sections.

2.4 Analysis of included articles

The result presented in this review is based on the abstraction of data from the articles. The descriptive synthesized results are based on the reviewers’ experience and the quality and content of the available literature ( Fink, 2019 ). All results are based on an abstraction of data except for those in Section 3.3.4, where the NVIVO software was used to uncover the most frequently used words from a compiled text of all analysis sections from each and every article in the review.

3.1 Identification, screening, eligibility and inclusion mechanism

This research returned 2,153 records. The first step before any analysis is to remove any duplicates. After removing duplicates, a total of 1,611 unique records remained. Following the recommendation from Weidt and Silva (2016) , the first analysis step is screening by title and abstract. A total of 1,517 records were found to be irrelevant for this review, leaving 94 articles for additional screening. The (optional) second screening, depending on the number of articles, involves an analysis of each article’s introduction and conclusion. For this study, an analysis of the method section was also included in the second screening step. This narrowed the number down to 28, where another 2 articles were excluded because of the lack of empirical data and irrelevance to the topic being reviewed, leaving the total number of 26 articles for complete text analysis. Figure 1 , adapted from Page et al. (2021) depicts the screening process.

3.2 Trend and classification of included studies

Of the 26 selected articles, 19 were published in journals, and the remaining 7 in conferences, or 73% and 27%, respectively (see Figure 2 ). The figure also demonstrates the increased interest in the subject in the past two years.

3.3 Findings

3.3.1 how is cybersecurity-related behavior assessed.

Of the selected 26 articles in this review, 24 or 92% provide insight into how cybersecurity-related behavior is assessed. A three-step process emerges as the way to assess such behavior: First, information from subjects needs to be collected. This is referred to as data collection . Second, a measurement scale is deployed to ensure that the data collected is relevant and encompasses the research topic. The final step is the data analysis.

3.3.2 Data collection.

Two forms of data can be collected, qualitative or quantitative. Both of these types of data can be subjective or objective; neither is exclusive to the other. The most common way to collect subjective data is using a questionnaire with questions whose answers fit into a five- or seven-point Likert scale. Within a survey, questions may be asked that are subjective, biased or misleading when viewed alone, but the results can easily be used quantitatively ( O'Brien, 1999 ). With the ubiquity of qualitative data, the interest in quantifying and being able to assign “good” numerical values and make the data susceptible to more meaningful analysis has been a topic for research since the first methods for quantification first began to appear around 1940 ( Young, 1981 ).

Subjective data can lead to inaccurate or skewed results. In contrast, objective data are free from the subject’s opinions. This can be, for example, the number of attacks prevented or the number of employees clicking the link in a phishing campaign ( Black et al. , 2008 ).

The SLR revealed six types of data collection methods, namely, self-assessment questionnaire (SAQ); interview; vignette; experiment with vignettes; affective computing and sentiment analysis; and clicking data from a phishing campaign. An overview of all articles and the data collection method used in each is presented in Table 1 .

The most prominent form of data collection is self-assessment (SA). This subjective data collection method is defined by Boekaerts (1991) as a form of appraisal that compares one’s behavioral outcomes to an internal or external standard. In total, 22 of the 24 articles used SA as the primary data collection method. The most common way to collect data is through a questionnaire (SAQ). A total of 17 or 71% of the articles used an SAQ as their sole method for data collection.

Of the remaining five articles with results stemming from subjective data, two used vignettes in combination with a regular SAQ. Vignettes are hypothetical scenarios in which the subject reads and forms an opinion based on the information. Barlow et al. (2013) performed a factorial survey method (FSM) experiment with vignettes by using randomly manipulated elements into sentences in the scenarios instead of static text. Both regular questionnaires and vignettes use the same Likert scale.

The average number of respondents in the included papers is n = 356, with 52% males and 48% females. The most common way to deploy the SAQ is through online Web platforms, e.g. a by-invitation-only webpage at a market research company. Pen and paper were only used twice. Market research companies and management distribution are the two most used recruitment strategies. The two methods are used in 73% of the papers, or 84% of the time, if articles that did not specify recruitment are excluded.

Two studies used interviews to collect information: one used interviews with an SAQ, and the other used interviews as the sole input. Interviews provide in-depth information and are suitable for uncovering the “how” and “why” of critical events as well as the insights reflecting the participants’ relativist perspectives ( Yin, 2018 ).

Only two studies used objective, quantitative data: Kruger et al. (2020) used affective computing and sentiment analysis. With the help of a deep learning neural network, the study accurately classified opinions as positive, neutral or negative based on facial expressions. Jalali et al. (2020) used a phishing campaign in conjunction with an SAQ to investigate whether there were any differences between intention to comply and actual compliance.

3.3.3 Measurement scale.

A measurement scale ensures that the collected data encompass a topic or subject and do not miss any crucial facets. The role of a measurement scale is to ensure that the data collected is holistic and reproducible. Researchers can use predefined scales developed by others or self-developed ones. Those of the reviewed articles that use the latter form of scale are often not fully transparent about the content of the scale.

This SLR shows that 13 of the 22 articles that used a measurement scale used an unspecified scale. The most frequently (in seven papers) used specified scale is the Human Aspect of Information Security Questionnaire (HAIS-Q), developed by Parsons et al. (2014) . When used in conjunction with other scales, HAIS-Q is often the most prominent.

Several pitfalls exist and must be considered when researchers select their measurement scale. If choosing to develop an unspecified scale, as found to be the most deployed alternative in this SLR, length, wording, familiarity with the topic, natural sequence of time and questions in a logical order are some of the topics that researchers should be mindful of ( Fink, 2015 ). Especially the length of the questionnaire is significant; how much time do the respondents have to spend answering the survey? Another critical element when designing a measurement scale instead of using an existing one is validity and reliability. Proper pilot testing is required when choosing not to use an already-validated survey ( Fink, 2015 ).

The HAIS-Q is designed to measure information security awareness related to information security in the workplace ( McCormac et al. , 2017 ). The Knowledge, Attitude and Behavior (KAB) model is at the center of HAIS-Q. The hypothesis is that when computer users gain more knowledge, their attitude toward policies will improve, translating into more risk-averse behavior ( Pollini et al. , 2021 ). The HAIS-Q comprises 63 questions covering 7 focus areas (internet use, email use, social networking site use, password management, incident reporting, information handling and mobile computing). Each focus area is divided into equal parts for KAB, resulting in 21 questions for each KAB element divided by the seven focus areas. For a detailed overview of the other scales used in conjunction with HAIS-Q, see the last column in Table 1 .

The KAB model that underpins HAIS-Q has been criticized by researchers when used in, e.g. health and climate research. Both Parsons et al. (2014) and McCormac et al. (2016) cite McGuire (1969) who suggest that the problem is not with the model itself but with how it is applied. Parsons et al. (2014) highlight essential differences between environmental and health studies and the field of information security. Much ambiguity and unclear or contradictory information exist in the two former topics, while most organizations have an information security policy, either written or informal, indicating what is expected from employees ( Parsons et al. , 2014 ). Barlow et al. (2013) advocate using scenarios instead of direct questions, like in HAIS-Q, because it is difficult to assess actual deviant behavior by observation or direct questioning.

Another critique of the HAIS-Q is the length of the questionnaire. With 63 questions, respondents might lose interest, be inattentive to the questions and sometimes give false answers ( Velki et al. , 2019 ). On the contrary, Parsons et al. (2017) show that the HAIS-Q questionnaire is a reliable and validated measurement scale and accommodates some of the concerns raised by Fink (2015) .

Pollini et al. (2021) advise that, when using one, the questionnaire only considers the individual level and may not capture a holistic and accurate measurement of the organizations. Therefore, in their study, HAIS-Q questionnaires were deployed at the individual level, and interviews were used to assess the organizational level.

3.3.4 Analysis.

To uncover how the included articles had analyzed their results, NVIVO, a qualitative data analysis software, was used to identify the most frequently used words in each article. An accumulative document from each article’s analysis section was analyzed in NVIVO. All articles use some sort of validation and statistical verification of the collected data. The use of word count provides both a structured presentation and an unbiased account of how often keywords affiliated with the technical part of the analysis are used. The result from NVIVO shows that partial least square (PLS) is the most frequently used method. Herman Wold first coined PLS in 1975; it can be preferable in cases where constructs are measured primarily by formative indicators, e.g. managerial research, or when the sample size is small ( Haenlein and Kaplan, 2004 ). This result is also in line with the finding in Kurowski (2019) : “Most of policy compliance research uses partial least squares, regression modeling or correlation analyses.”

3.3.4.1 Are there differences between manager and employee intention and behavior in a cybersecurity context?

Only five articles, or 19%, provide insight into the second research question. However, none provides a clear-cut response to this research question. There is a consensus in all five articles that organizational culture is a cornerstone for security and policy-compliant behavior ( Reeves et al. , 2020 ; Hwang et al. , 2017 ; Alzahrani, 2021 ; Parsons et al. , 2015 ; Li et al. , 2019 ).

Among the articles, there is also a broad agreement that peers’ behavior, the influence that peers have on our behavior, is vital for a positive cybersecurity outcome ( Li et al. , 2019 ; Alzahrani, 2021 ; Hwang et al. , 2017 ). Peer- and policy-compliant behavior can only be achieved when the organization has a positive cybersecurity culture. The development of organizational culture often comes from the top management; hence, the development and continued improvement of culture will be assigned to management ( Li et al. , 2019 ; Reeves et al. , 2020 ). One interesting finding in the context of developing or harnessing a security culture is that managers have a much lower information security awareness; Reeves et al. (2020) therefore recommend that future training should be targeted to management. This small paradox is at least something to dwell on, given that culture is built from the top.

All the articles provide reasons for noncompliance in their findings. In a hectic environment, employee workload has been shown to negatively impact compliance ( Jalali et al. , 2020 ). Connected to workload are work goals. Security will draw the shortest straw when goals and security do not align. If security is viewed as a hindrance, noncompliant behavior will arise ( Reeves et al. , 2020 ; Hwang et al. , 2017 ; Alzahrani, 2021 ; Parsons et al. , 2015 ). Also, when employees lack knowledge or have not been given sufficient information about the organization’s security policies, compliant behavior will be impacted ( Hwang et al. , 2017 ; Alzahrani, 2021 ; Parsons et al. , 2015 ; Li et al. , 2019 ).

4. Discussion

The findings of this SLR have shown that there is an overweight of subjective data collected to measure cybersecurity. Over 90% of the included articles use subjective data to measure behavior. Only one article relies solely on objective measurements. The availability and ease of use regarding subjective methods might be the reason. An interview can be done without much cost or planning, whereas using objective methods will require more resources, e.g. a phishing campaign.

However, the use of subjective data can lead to biased responses from the subjects. This bias can be problematic. According to Kurowski (2019) , “For instance, survey reports of church attendance and rates of exercise are found to be double the actual frequency when self-reported.” Almost all articles address the issue of biased measurement. Many refer to Podsakoff et al. (2003) and the recommendation therein to assure respondents that their identity will be kept anonymous. It seems like anonymization is an acceptable way to remove the risk of bias for several researchers. However, as Kurowski (2019) finds, there does exist bias in today’s research. In his paper, to test for a biased response, two questionnaires were used, one using standard, straightforward compliance questions and one using vignettes, see Table 1 . Kurowski (2019) found that generic questionnaires may capture biased policy compliance measures. If an individual reports policy compliance on the literature-based scale, it may mean any of the following: An individual is indeed compliant; an individual does not know the policy and does not act compliant; or an individual thinks they are compliant with the policy because they behave securely, but do not know the policy. This does not imply that existing research fails to measure policy compliance entirely, but it fails to measure it reliably ( Kurowski, 2019 ).

Jalali et al. (2020) included objective and subjective measurements. They compared the employees’ intention to comply with their actual compliance by examining whether the employees had clicked the link in the phishing campaign or not. They found no significant relationship between the intention to comply and the actual behavior. This result is not in line with previous studies that used self-reported data, a method that leaves room for socially desirable answers ( Podsakoff et al. , 2003 ), or previous answers could influence later answers ( Jalali, 2014 ).

Even the HAIS-Q, the single most used questionnaire, used seven times in this SLR, does not refrain from biased responses. Even though the questionnaire was validated and tested by Parsons et al. (2017) , when researched to uncover biased responses by McCormac et al. (2017) , showed that social desirability bias can be present. This means that further research is needed to exclude biased responses from HAIS-Q.

5. Conclusion

This SLR, which started with 2,153 unique articles and was reduced during several analysis steps to 26 articles, provides insights into the predefined research questions.

When excluding all preparational work before a study is performed, the assessment of behavior can be classified into three components: data collection , measurement scale and lastly, analysis . This research found that subjective data are collected to a much larger extent than objective data, in the context of cybersecurity, with online SAQ as the most prominent way to collect data. Measurement scales are often composed based on existing literature and adapted by the researchers. The most commonly used questionnaire is HAIS-Q, developed by Parsons et al. (2014) . Finally, an analysis is performed to test for internal and external validation of the collected data. PLS analysis is the most frequent technique in selected articles. Although a clear path to assess behavior is uncovered, the proposed self-assessment method can produce biased data. Thus, future research should address the problem of objectively assessing cybersecurity-related behavior and the factors affecting it.

The second research question, i.e. whether there exist differences between manager and employee behavior, was not conclusively answered. Of the relatively small number of articles, several provide insights and noteworthy findings but not conclusive answers to this research question. In light of the significance of the matter for improving the cybersecurity culture in an organization, this constitutes another interesting research gap.

Future research should bridge the above research gaps, and studies should include employees and management from the same organization. This will require more planning and coordination than simply deploying a questionnaire online. Extra effort in anonymizing personal data must be in place because subjects come from the same organization. The uncertainty surrounding anonymization and the risk of biased responses concerning anonymization must be mitigated. This can be obtained by, e.g. using a hybrid method consisting of objective and subjective data collection, e.g. self-assessment questionnaires and phishing campaigns. Future research should collect holistic data within a market, country, segment or similar, as research into compliance is context-dependable ( Jalali et al. , 2020 ).

The SLR screening process

Trend and classification of included studies

Overview of reviewed articles

Ajzen , I. ( 1991 ), “ The theory of planned behavior ”, Organizational Behavior and Human Decision Processes , Vol. 50 No. 2 , pp. 179 - 211 .

Alassaf , M. and Alkhalifah , A. ( 2021 ), “ Exploring the influence of direct and indirect factors on information security policy compliance: a systematic literature review ”, IEEE Access , Vol. 9 , pp. 162687 - 162705 .

Al-Omari , A. , El-Gayar , O. and Deokar , A. ( 2012 ), “ Security policy compliance: user acceptance perspective ”, 2012 45th HI International Conference on System Sciences , IEEE , pp. 3317 - 3326 .

Alzahrani , L. ( 2021 ), “ Factors impacting users’ compliance with information security policies: an empirical study ”, International Journal of Advanced Computer Science and Applications , Vol. 12 No. 10 .

Ameen , N. , Tarhini , A. , Shah , M.H. , Madichie , N. , Paul , J. and Choudrie , J. ( 2021 ), “ Keeping customers’ data secure: a cross-cultural study of cybersecurity compliance among the Gen-Mobile workforce ”, Computers in Human Behavior , Vol. 114 , p. 106531 , doi: 10.1016/j.chb.2020.106531 .

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Further readings

Bulgurcu , B. , Cavusoglu , H. and Benbasat , I. ( 2010 ), “ Information security policy compliance: an empirical study of rationality-based beliefs and information security awareness ”, MIS Quarterly , Vol. 34 No. 3 , pp. 523 - 548 .

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Acknowledgements

This work was supported by the Research Council of Norway under Project nr 323131 “How to improve Cyber Security performance by researching human behavior and improve processes in an industrial environment” and Project nr 310105 “Norwegian Centre for Cyber Security in Critical Sectors (NORCICS).”

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Cyber risk and cybersecurity: a systematic review of data availability

Frank cremer.

1 University of Limerick, Limerick, Ireland

Barry Sheehan

Michael fortmann.

2 TH Köln University of Applied Sciences, Cologne, Germany

Arash N. Kia

Martin mullins, finbarr murphy, stefan materne, associated data.

Cybercrime is estimated to have cost the global economy just under USD 1 trillion in 2020, indicating an increase of more than 50% since 2018. With the average cyber insurance claim rising from USD 145,000 in 2019 to USD 359,000 in 2020, there is a growing necessity for better cyber information sources, standardised databases, mandatory reporting and public awareness. This research analyses the extant academic and industry literature on cybersecurity and cyber risk management with a particular focus on data availability. From a preliminary search resulting in 5219 cyber peer-reviewed studies, the application of the systematic methodology resulted in 79 unique datasets. We posit that the lack of available data on cyber risk poses a serious problem for stakeholders seeking to tackle this issue. In particular, we identify a lacuna in open databases that undermine collective endeavours to better manage this set of risks. The resulting data evaluation and categorisation will support cybersecurity researchers and the insurance industry in their efforts to comprehend, metricise and manage cyber risks.

Supplementary Information

The online version contains supplementary material available at 10.1057/s41288-022-00266-6.

Introduction

Globalisation, digitalisation and smart technologies have escalated the propensity and severity of cybercrime. Whilst it is an emerging field of research and industry, the importance of robust cybersecurity defence systems has been highlighted at the corporate, national and supranational levels. The impacts of inadequate cybersecurity are estimated to have cost the global economy USD 945 billion in 2020 (Maleks Smith et al. 2020 ). Cyber vulnerabilities pose significant corporate risks, including business interruption, breach of privacy and financial losses (Sheehan et al. 2019 ). Despite the increasing relevance for the international economy, the availability of data on cyber risks remains limited. The reasons for this are many. Firstly, it is an emerging and evolving risk; therefore, historical data sources are limited (Biener et al. 2015 ). It could also be due to the fact that, in general, institutions that have been hacked do not publish the incidents (Eling and Schnell 2016 ). The lack of data poses challenges for many areas, such as research, risk management and cybersecurity (Falco et al. 2019 ). The importance of this topic is demonstrated by the announcement of the European Council in April 2021 that a centre of excellence for cybersecurity will be established to pool investments in research, technology and industrial development. The goal of this centre is to increase the security of the internet and other critical network and information systems (European Council 2021 ).

This research takes a risk management perspective, focusing on cyber risk and considering the role of cybersecurity and cyber insurance in risk mitigation and risk transfer. The study reviews the existing literature and open data sources related to cybersecurity and cyber risk. This is the first systematic review of data availability in the general context of cyber risk and cybersecurity. By identifying and critically analysing the available datasets, this paper supports the research community by aggregating, summarising and categorising all available open datasets. In addition, further information on datasets is attached to provide deeper insights and support stakeholders engaged in cyber risk control and cybersecurity. Finally, this research paper highlights the need for open access to cyber-specific data, without price or permission barriers.

The identified open data can support cyber insurers in their efforts on sustainable product development. To date, traditional risk assessment methods have been untenable for insurance companies due to the absence of historical claims data (Sheehan et al. 2021 ). These high levels of uncertainty mean that cyber insurers are more inclined to overprice cyber risk cover (Kshetri 2018 ). Combining external data with insurance portfolio data therefore seems to be essential to improve the evaluation of the risk and thus lead to risk-adjusted pricing (Bessy-Roland et al. 2021 ). This argument is also supported by the fact that some re/insurers reported that they are working to improve their cyber pricing models (e.g. by creating or purchasing databases from external providers) (EIOPA 2018 ). Figure  1 provides an overview of pricing tools and factors considered in the estimation of cyber insurance based on the findings of EIOPA ( 2018 ) and the research of Romanosky et al. ( 2019 ). The term cyber risk refers to all cyber risks and their potential impact.

An overview of the current cyber insurance informational and methodological landscape, adapted from EIOPA ( 2018 ) and Romanosky et al. ( 2019 )

Besides the advantage of risk-adjusted pricing, the availability of open datasets helps companies benchmark their internal cyber posture and cybersecurity measures. The research can also help to improve risk awareness and corporate behaviour. Many companies still underestimate their cyber risk (Leong and Chen 2020 ). For policymakers, this research offers starting points for a comprehensive recording of cyber risks. Although in many countries, companies are obliged to report data breaches to the respective supervisory authority, this information is usually not accessible to the research community. Furthermore, the economic impact of these breaches is usually unclear.

As well as the cyber risk management community, this research also supports cybersecurity stakeholders. Researchers are provided with an up-to-date, peer-reviewed literature of available datasets showing where these datasets have been used. For example, this includes datasets that have been used to evaluate the effectiveness of countermeasures in simulated cyberattacks or to test intrusion detection systems. This reduces a time-consuming search for suitable datasets and ensures a comprehensive review of those available. Through the dataset descriptions, researchers and industry stakeholders can compare and select the most suitable datasets for their purposes. In addition, it is possible to combine the datasets from one source in the context of cybersecurity or cyber risk. This supports efficient and timely progress in cyber risk research and is beneficial given the dynamic nature of cyber risks.

Cyber risks are defined as “operational risks to information and technology assets that have consequences affecting the confidentiality, availability, and/or integrity of information or information systems” (Cebula et al. 2014 ). Prominent cyber risk events include data breaches and cyberattacks (Agrafiotis et al. 2018 ). The increasing exposure and potential impact of cyber risk have been highlighted in recent industry reports (e.g. Allianz 2021 ; World Economic Forum 2020 ). Cyberattacks on critical infrastructures are ranked 5th in the World Economic Forum's Global Risk Report. Ransomware, malware and distributed denial-of-service (DDoS) are examples of the evolving modes of a cyberattack. One example is the ransomware attack on the Colonial Pipeline, which shut down the 5500 mile pipeline system that delivers 2.5 million barrels of fuel per day and critical liquid fuel infrastructure from oil refineries to states along the U.S. East Coast (Brower and McCormick 2021 ). These and other cyber incidents have led the U.S. to strengthen its cybersecurity and introduce, among other things, a public body to analyse major cyber incidents and make recommendations to prevent a recurrence (Murphey 2021a ). Another example of the scope of cyberattacks is the ransomware NotPetya in 2017. The damage amounted to USD 10 billion, as the ransomware exploited a vulnerability in the windows system, allowing it to spread independently worldwide in the network (GAO 2021 ). In the same year, the ransomware WannaCry was launched by cybercriminals. The cyberattack on Windows software took user data hostage in exchange for Bitcoin cryptocurrency (Smart 2018 ). The victims included the National Health Service in Great Britain. As a result, ambulances were redirected to other hospitals because of information technology (IT) systems failing, leaving people in need of urgent assistance waiting. It has been estimated that 19,000 cancelled treatment appointments resulted from losses of GBP 92 million (Field 2018 ). Throughout the COVID-19 pandemic, ransomware attacks increased significantly, as working from home arrangements increased vulnerability (Murphey 2021b ).

Besides cyberattacks, data breaches can also cause high costs. Under the General Data Protection Regulation (GDPR), companies are obliged to protect personal data and safeguard the data protection rights of all individuals in the EU area. The GDPR allows data protection authorities in each country to impose sanctions and fines on organisations they find in breach. “For data breaches, the maximum fine can be €20 million or 4% of global turnover, whichever is higher” (GDPR.EU 2021 ). Data breaches often involve a large amount of sensitive data that has been accessed, unauthorised, by external parties, and are therefore considered important for information security due to their far-reaching impact (Goode et al. 2017 ). A data breach is defined as a “security incident in which sensitive, protected, or confidential data are copied, transmitted, viewed, stolen, or used by an unauthorized individual” (Freeha et al. 2021 ). Depending on the amount of data, the extent of the damage caused by a data breach can be significant, with the average cost being USD 392 million 1 (IBM Security 2020 ).

This research paper reviews the existing literature and open data sources related to cybersecurity and cyber risk, focusing on the datasets used to improve academic understanding and advance the current state-of-the-art in cybersecurity. Furthermore, important information about the available datasets is presented (e.g. use cases), and a plea is made for open data and the standardisation of cyber risk data for academic comparability and replication. The remainder of the paper is structured as follows. The next section describes the related work regarding cybersecurity and cyber risks. The third section outlines the review method used in this work and the process. The fourth section details the results of the identified literature. Further discussion is presented in the penultimate section and the final section concludes.

Related work

Due to the significance of cyber risks, several literature reviews have been conducted in this field. Eling ( 2020 ) reviewed the existing academic literature on the topic of cyber risk and cyber insurance from an economic perspective. A total of 217 papers with the term ‘cyber risk’ were identified and classified in different categories. As a result, open research questions are identified, showing that research on cyber risks is still in its infancy because of their dynamic and emerging nature. Furthermore, the author highlights that particular focus should be placed on the exchange of information between public and private actors. An improved information flow could help to measure the risk more accurately and thus make cyber risks more insurable and help risk managers to determine the right level of cyber risk for their company. In the context of cyber insurance data, Romanosky et al. ( 2019 ) analysed the underwriting process for cyber insurance and revealed how cyber insurers understand and assess cyber risks. For this research, they examined 235 American cyber insurance policies that were publicly available and looked at three components (coverage, application questionnaires and pricing). The authors state in their findings that many of the insurers used very simple, flat-rate pricing (based on a single calculation of expected loss), while others used more parameters such as the asset value of the company (or company revenue) or standard insurance metrics (e.g. deductible, limits), and the industry in the calculation. This is in keeping with Eling ( 2020 ), who states that an increased amount of data could help to make cyber risk more accurately measured and thus more insurable. Similar research on cyber insurance and data was conducted by Nurse et al. ( 2020 ). The authors examined cyber insurance practitioners' perceptions and the challenges they face in collecting and using data. In addition, gaps were identified during the research where further data is needed. The authors concluded that cyber insurance is still in its infancy, and there are still several unanswered questions (for example, cyber valuation, risk calculation and recovery). They also pointed out that a better understanding of data collection and use in cyber insurance would be invaluable for future research and practice. Bessy-Roland et al. ( 2021 ) come to a similar conclusion. They proposed a multivariate Hawkes framework to model and predict the frequency of cyberattacks. They used a public dataset with characteristics of data breaches affecting the U.S. industry. In the conclusion, the authors make the argument that an insurer has a better knowledge of cyber losses, but that it is based on a small dataset and therefore combination with external data sources seems essential to improve the assessment of cyber risks.

Several systematic reviews have been published in the area of cybersecurity (Kruse et al. 2017 ; Lee et al. 2020 ; Loukas et al. 2013 ; Ulven and Wangen 2021 ). In these papers, the authors concentrated on a specific area or sector in the context of cybersecurity. This paper adds to this extant literature by focusing on data availability and its importance to risk management and insurance stakeholders. With a priority on healthcare and cybersecurity, Kruse et al. ( 2017 ) conducted a systematic literature review. The authors identified 472 articles with the keywords ‘cybersecurity and healthcare’ or ‘ransomware’ in the databases Cumulative Index of Nursing and Allied Health Literature, PubMed and Proquest. Articles were eligible for this review if they satisfied three criteria: (1) they were published between 2006 and 2016, (2) the full-text version of the article was available, and (3) the publication is a peer-reviewed or scholarly journal. The authors found that technological development and federal policies (in the U.S.) are the main factors exposing the health sector to cyber risks. Loukas et al. ( 2013 ) conducted a review with a focus on cyber risks and cybersecurity in emergency management. The authors provided an overview of cyber risks in communication, sensor, information management and vehicle technologies used in emergency management and showed areas for which there is still no solution in the literature. Similarly, Ulven and Wangen ( 2021 ) reviewed the literature on cybersecurity risks in higher education institutions. For the literature review, the authors used the keywords ‘cyber’, ‘information threats’ or ‘vulnerability’ in connection with the terms ‘higher education, ‘university’ or ‘academia’. A similar literature review with a focus on Internet of Things (IoT) cybersecurity was conducted by Lee et al. ( 2020 ). The review revealed that qualitative approaches focus on high-level frameworks, and quantitative approaches to cybersecurity risk management focus on risk assessment and quantification of cyberattacks and impacts. In addition, the findings presented a four-step IoT cyber risk management framework that identifies, quantifies and prioritises cyber risks.

Datasets are an essential part of cybersecurity research, underlined by the following works. Ilhan Firat et al. ( 2021 ) examined various cybersecurity datasets in detail. The study was motivated by the fact that with the proliferation of the internet and smart technologies, the mode of cyberattacks is also evolving. However, in order to prevent such attacks, they must first be detected; the dissemination and further development of cybersecurity datasets is therefore critical. In their work, the authors observed studies of datasets used in intrusion detection systems. Khraisat et al. ( 2019 ) also identified a need for new datasets in the context of cybersecurity. The researchers presented a taxonomy of current intrusion detection systems, a comprehensive review of notable recent work, and an overview of the datasets commonly used for assessment purposes. In their conclusion, the authors noted that new datasets are needed because most machine-learning techniques are trained and evaluated on the knowledge of old datasets. These datasets do not contain new and comprehensive information and are partly derived from datasets from 1999. The authors noted that the core of this issue is the availability of new public datasets as well as their quality. The availability of data, how it is used, created and shared was also investigated by Zheng et al. ( 2018 ). The researchers analysed 965 cybersecurity research papers published between 2012 and 2016. They created a taxonomy of the types of data that are created and shared and then analysed the data collected via datasets. The researchers concluded that while datasets are recognised as valuable for cybersecurity research, the proportion of publicly available datasets is limited.

The main contributions of this review and what differentiates it from previous studies can be summarised as follows. First, as far as we can tell, it is the first work to summarise all available datasets on cyber risk and cybersecurity in the context of a systematic review and present them to the scientific community and cyber insurance and cybersecurity stakeholders. Second, we investigated, analysed, and made available the datasets to support efficient and timely progress in cyber risk research. And third, we enable comparability of datasets so that the appropriate dataset can be selected depending on the research area.

Methodology

Process and eligibility criteria.

The structure of this systematic review is inspired by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework (Page et al. 2021 ), and the search was conducted from 3 to 10 May 2021. Due to the continuous development of cyber risks and their countermeasures, only articles published in the last 10 years were considered. In addition, only articles published in peer-reviewed journals written in English were included. As a final criterion, only articles that make use of one or more cybersecurity or cyber risk datasets met the inclusion criteria. Specifically, these studies presented new or existing datasets, used them for methods, or used them to verify new results, as well as analysed them in an economic context and pointed out their effects. The criterion was fulfilled if it was clearly stated in the abstract that one or more datasets were used. A detailed explanation of this selection criterion can be found in the ‘Study selection’ section.

Information sources

In order to cover a complete spectrum of literature, various databases were queried to collect relevant literature on the topic of cybersecurity and cyber risks. Due to the spread of related articles across multiple databases, the literature search was limited to the following four databases for simplicity: IEEE Xplore, Scopus, SpringerLink and Web of Science. This is similar to other literature reviews addressing cyber risks or cybersecurity, including Sardi et al. ( 2021 ), Franke and Brynielsson ( 2014 ), Lagerström (2019), Eling and Schnell ( 2016 ) and Eling ( 2020 ). In this paper, all databases used in the aforementioned works were considered. However, only two studies also used all the databases listed. The IEEE Xplore database contains electrical engineering, computer science, and electronics work from over 200 journals and three million conference papers (IEEE 2021 ). Scopus includes 23,400 peer-reviewed journals from more than 5000 international publishers in the areas of science, engineering, medicine, social sciences and humanities (Scopus 2021 ). SpringerLink contains 3742 journals and indexes over 10 million scientific documents (SpringerLink 2021 ). Finally, Web of Science indexes over 9200 journals in different scientific disciplines (Science 2021 ).

A search string was created and applied to all databases. To make the search efficient and reproducible, the following search string with Boolean operator was used in all databases: cybersecurity OR cyber risk AND dataset OR database. To ensure uniformity of the search across all databases, some adjustments had to be made for the respective search engines. In Scopus, for example, the Advanced Search was used, and the field code ‘Title-ABS-KEY’ was integrated into the search string. For IEEE Xplore, the search was carried out with the Search String in the Command Search and ‘All Metadata’. In the Web of Science database, the Advanced Search was used. The special feature of this search was that it had to be carried out in individual steps. The first search was carried out with the terms cybersecurity OR cyber risk with the field tag Topic (T.S. =) and the second search with dataset OR database. Subsequently, these searches were combined, which then delivered the searched articles for review. For SpringerLink, the search string was used in the Advanced Search under the category ‘Find the resources with all of the words’. After conducting this search string, 5219 studies could be found. According to the eligibility criteria (period, language and only scientific journals), 1581 studies were identified in the databases:

• Scopus: 135
• Springer Link: 548
• Web of Science: 534

An overview of the process is given in Fig.  2 . Combined with the results from the four databases, 854 articles without duplicates were identified.

Literature search process and categorisation of the studies

Study selection

In the final step of the selection process, the articles were screened for relevance. Due to a large number of results, the abstracts were analysed in the first step of the process. The aim was to determine whether the article was relevant for the systematic review. An article fulfilled the criterion if it was recognisable in the abstract that it had made a contribution to datasets or databases with regard to cyber risks or cybersecurity. Specifically, the criterion was considered to be met if the abstract used datasets that address the causes or impacts of cyber risks, and measures in the area of cybersecurity. In this process, the number of articles was reduced to 288. The articles were then read in their entirety, and an expert panel of six people decided whether they should be used. This led to a final number of 255 articles. The years in which the articles were published and the exact number can be seen in Fig.  3 .

Distribution of studies

Data collection process and synthesis of the results

For the data collection process, various data were extracted from the studies, including the names of the respective creators, the name of the dataset or database and the corresponding reference. It was also determined where the data came from. In the context of accessibility, it was determined whether access is free, controlled, available for purchase or not available. It was also determined when the datasets were created and the time period referenced. The application type and domain characteristics of the datasets were identified.

This section analyses the results of the systematic literature review. The previously identified studies are divided into three categories: datasets on the causes of cyber risks, datasets on the effects of cyber risks and datasets on cybersecurity. The classification is based on the intended use of the studies. This system of classification makes it easier for stakeholders to find the appropriate datasets. The categories are evaluated individually. Although complete information is available for a large proportion of datasets, this is not true for all of them. Accordingly, the abbreviation N/A has been inserted in the respective characters to indicate that this information could not be determined by the time of submission. The term ‘use cases in the literature’ in the following and supplementary tables refers to the application areas in which the corresponding datasets were used in the literature. The areas listed there refer to the topic area on which the researchers conducted their research. Since some datasets were used interdisciplinarily, the listed use cases in the literature are correspondingly longer. Before discussing each category in the next sections, Fig.  4 provides an overview of the number of datasets found and their year of creation. Figure  5 then shows the relationship between studies and datasets in the period under consideration. Figure  6 shows the distribution of studies, their use of datasets and their creation date. The number of datasets used is higher than the number of studies because the studies often used several datasets (Table ​ (Table1). 1 ).

Distribution of dataset results

Correlation between the studies and the datasets

Distribution of studies and their use of datasets

Percentage contribution of datasets for each place of origin

Most of the datasets are generated in the U.S. (up to 58.2%). Canada and Australia rank next, with 11.3% and 5% of all the reviewed datasets, respectively.

Additionally, to create value for the datasets for the cyber insurance industry, an assessment of the applicability of each dataset has been provided for cyber insurers. This ‘Use Case Assessment’ includes the use of the data in the context of different analyses, calculation of cyber insurance premiums, and use of the information for the design of cyber insurance contracts or for additional customer services. To reasonably account for the transition of direct hyperlinks in the future, references were directed to the main websites for longevity (nearest resource point). In addition, the links to the main pages contain further information on the datasets and different versions related to the operating systems. The references were chosen in such a way that practitioners get the best overview of the respective datasets.

Case datasets

This section presents selected articles that use the datasets to analyse the causes of cyber risks. The datasets help identify emerging trends and allow pattern discovery in cyber risks. This information gives cybersecurity experts and cyber insurers the data to make better predictions and take appropriate action. For example, if certain vulnerabilities are not adequately protected, cyber insurers will demand a risk surcharge leading to an improvement in the risk-adjusted premium. Due to the capricious nature of cyber risks, existing data must be supplemented with new data sources (for example, new events, new methods or security vulnerabilities) to determine prevailing cyber exposure. The datasets of cyber risk causes could be combined with existing portfolio data from cyber insurers and integrated into existing pricing tools and factors to improve the valuation of cyber risks.

A portion of these datasets consists of several taxonomies and classifications of cyber risks. Aassal et al. ( 2020 ) propose a new taxonomy of phishing characteristics based on the interpretation and purpose of each characteristic. In comparison, Hindy et al. ( 2020 ) presented a taxonomy of network threats and the impact of current datasets on intrusion detection systems. A similar taxonomy was suggested by Kiwia et al. ( 2018 ). The authors presented a cyber kill chain-based taxonomy of banking Trojans features. The taxonomy built on a real-world dataset of 127 banking Trojans collected from December 2014 to January 2016 by a major U.K.-based financial organisation.

In the context of classification, Aamir et al. ( 2021 ) showed the benefits of machine learning for classifying port scans and DDoS attacks in a mixture of normal and attack traffic. Guo et al. ( 2020 ) presented a new method to improve malware classification based on entropy sequence features. The evaluation of this new method was conducted on different malware datasets.

To reconstruct attack scenarios and draw conclusions based on the evidence in the alert stream, Barzegar and Shajari ( 2018 ) use the DARPA2000 and MACCDC 2012 dataset for their research. Giudici and Raffinetti ( 2020 ) proposed a rank-based statistical model aimed at predicting the severity levels of cyber risk. The model used cyber risk data from the University of Milan. In contrast to the previous datasets, Skrjanc et al. ( 2018 ) used the older dataset KDD99 to monitor large-scale cyberattacks using a cauchy clustering method.

Amin et al. ( 2021 ) used a cyberattack dataset from the Canadian Institute for Cybersecurity to identify spatial clusters of countries with high rates of cyberattacks. In the context of cybercrime, Junger et al. ( 2020 ) examined crime scripts, key characteristics of the target company and the relationship between criminal effort and financial benefit. For their study, the authors analysed 300 cases of fraudulent activities against Dutch companies. With a similar focus on cybercrime, Mireles et al. ( 2019 ) proposed a metric framework to measure the effectiveness of the dynamic evolution of cyberattacks and defensive measures. To validate its usefulness, they used the DEFCON dataset.

Due to the rapidly changing nature of cyber risks, it is often impossible to obtain all information on them. Kim and Kim ( 2019 ) proposed an automated dataset generation system called CTIMiner that collects threat data from publicly available security reports and malware repositories. They released a dataset to the public containing about 640,000 records from 612 security reports published between January 2008 and 2019. A similar approach is proposed by Kim et al. ( 2020 ), using a named entity recognition system to extract core information from cyber threat reports automatically. They created a 498,000-tag dataset during their research (Ulven and Wangen 2021 ).

Within the framework of vulnerabilities and cybersecurity issues, Ulven and Wangen ( 2021 ) proposed an overview of mission-critical assets and everyday threat events, suggested a generic threat model, and summarised common cybersecurity vulnerabilities. With a focus on hospitality, Chen and Fiscus ( 2018 ) proposed several issues related to cybersecurity in this sector. They analysed 76 security incidents from the Privacy Rights Clearinghouse database. Supplementary Table 1 lists all findings that belong to the cyber causes dataset.

Impact datasets

This section outlines selected findings of the cyber impact dataset. For cyber insurers, these datasets can form an important basis for information, as they can be used to calculate cyber insurance premiums, evaluate specific cyber risks, formulate inclusions and exclusions in cyber wordings, and re-evaluate as well as supplement the data collected so far on cyber risks. For example, information on financial losses can help to better assess the loss potential of cyber risks. Furthermore, the datasets can provide insight into the frequency of occurrence of these cyber risks. The new datasets can be used to close any data gaps that were previously based on very approximate estimates or to find new results.

Eight studies addressed the costs of data breaches. For instance, Eling and Jung ( 2018 ) reviewed 3327 data breach events from 2005 to 2016 and identified an asymmetric dependence of monthly losses by breach type and industry. The authors used datasets from the Privacy Rights Clearinghouse for analysis. The Privacy Rights Clearinghouse datasets and the Breach level index database were also used by De Giovanni et al. ( 2020 ) to describe relationships between data breaches and bitcoin-related variables using the cointegration methodology. The data were obtained from the Department of Health and Human Services of healthcare facilities reporting data breaches and a national database of technical and organisational infrastructure information. Also in the context of data breaches, Algarni et al. ( 2021 ) developed a comprehensive, formal model that estimates the two components of security risks: breach cost and the likelihood of a data breach within 12 months. For their survey, the authors used two industrial reports from the Ponemon institute and VERIZON. To illustrate the scope of data breaches, Neto et al. ( 2021 ) identified 430 major data breach incidents among more than 10,000 incidents. The database created is available and covers the period 2018 to 2019.

With a direct focus on insurance, Biener et al. ( 2015 ) analysed 994 cyber loss cases from an operational risk database and investigated the insurability of cyber risks based on predefined criteria. For their study, they used data from the company SAS OpRisk Global Data. Similarly, Eling and Wirfs ( 2019 ) looked at a wide range of cyber risk events and actual cost data using the same database. They identified cyber losses and analysed them using methods from statistics and actuarial science. Using a similar reference, Farkas et al. ( 2021 ) proposed a method for analysing cyber claims based on regression trees to identify criteria for classifying and evaluating claims. Similar to Chen and Fiscus ( 2018 ), the dataset used was the Privacy Rights Clearinghouse database. Within the framework of reinsurance, Moro ( 2020 ) analysed cyber index-based information technology activity to see if index-parametric reinsurance coverage could suggest its cedant using data from a Symantec dataset.

Paté-Cornell et al. ( 2018 ) presented a general probabilistic risk analysis framework for cybersecurity in an organisation to be specified. The results are distributions of losses to cyberattacks, with and without considered countermeasures in support of risk management decisions based both on past data and anticipated incidents. The data used were from The Common Vulnerability and Exposures database and via confidential access to a database of cyberattacks on a large, U.S.-based organisation. A different conceptual framework for cyber risk classification and assessment was proposed by Sheehan et al. ( 2021 ). This framework showed the importance of proactive and reactive barriers in reducing companies’ exposure to cyber risk and quantifying the risk. Another approach to cyber risk assessment and mitigation was proposed by Mukhopadhyay et al. ( 2019 ). They estimated the probability of an attack using generalised linear models, predicted the security technology required to reduce the probability of cyberattacks, and used gamma and exponential distributions to best approximate the average loss data for each malicious attack. They also calculated the expected loss due to cyberattacks, calculated the net premium that would need to be charged by a cyber insurer, and suggested cyber insurance as a strategy to minimise losses. They used the CSI-FBI survey (1997–2010) to conduct their research.

In order to highlight the lack of data on cyber risks, Eling ( 2020 ) conducted a literature review in the areas of cyber risk and cyber insurance. Available information on the frequency, severity, and dependency structure of cyber risks was filtered out. In addition, open questions for future cyber risk research were set up. Another example of data collection on the impact of cyberattacks is provided by Sornette et al. ( 2013 ), who use a database of newspaper articles, press reports and other media to provide a predictive method to identify triggering events and potential accident scenarios and estimate their severity and frequency. A similar approach to data collection was used by Arcuri et al. ( 2020 ) to gather an original sample of global cyberattacks from newspaper reports sourced from the LexisNexis database. This collection is also used and applied to the fields of dynamic communication and cyber risk perception by Fang et al. ( 2021 ). To create a dataset of cyber incidents and disputes, Valeriano and Maness ( 2014 ) collected information on cyber interactions between rival states.

To assess trends and the scale of economic cybercrime, Levi ( 2017 ) examined datasets from different countries and their impact on crime policy. Pooser et al. ( 2018 ) investigated the trend in cyber risk identification from 2006 to 2015 and company characteristics related to cyber risk perception. The authors used a dataset of various reports from cyber insurers for their study. Walker-Roberts et al. ( 2020 ) investigated the spectrum of risk of a cybersecurity incident taking place in the cyber-physical-enabled world using the VERIS Community Database. The datasets of impacts identified are presented below. Due to overlap, some may also appear in the causes dataset (Supplementary Table 2).

Cybersecurity datasets

General intrusion detection.

General intrusion detection systems account for the largest share of countermeasure datasets. For companies or researchers focused on cybersecurity, the datasets can be used to test their own countermeasures or obtain information about potential vulnerabilities. For example, Al-Omari et al. ( 2021 ) proposed an intelligent intrusion detection model for predicting and detecting attacks in cyberspace, which was applied to dataset UNSW-NB 15. A similar approach was taken by Choras and Kozik ( 2015 ), who used machine learning to detect cyberattacks on web applications. To evaluate their method, they used the HTTP dataset CSIC 2010. For the identification of unknown attacks on web servers, Kamarudin et al. ( 2017 ) proposed an anomaly-based intrusion detection system using an ensemble classification approach. Ganeshan and Rodrigues ( 2020 ) showed an intrusion detection system approach, which clusters the database into several groups and detects the presence of intrusion in the clusters. In comparison, AlKadi et al. ( 2019 ) used a localisation-based model to discover abnormal patterns in network traffic. Hybrid models have been recommended by Bhattacharya et al. ( 2020 ) and Agrawal et al. ( 2019 ); the former is a machine-learning model based on principal component analysis for the classification of intrusion detection system datasets, while the latter is a hybrid ensemble intrusion detection system for anomaly detection using different datasets to detect patterns in network traffic that deviate from normal behaviour.

Agarwal et al. ( 2021 ) used three different machine learning algorithms in their research to find the most suitable for efficiently identifying patterns of suspicious network activity. The UNSW-NB15 dataset was used for this purpose. Kasongo and Sun ( 2020 ), Feed-Forward Deep Neural Network (FFDNN), Keshk et al. ( 2021 ), the privacy-preserving anomaly detection framework, and others also use the UNSW-NB 15 dataset as part of intrusion detection systems. The same dataset and others were used by Binbusayyis and Vaiyapuri ( 2019 ) to identify and compare key features for cyber intrusion detection. Atefinia and Ahmadi ( 2021 ) proposed a deep neural network model to reduce the false positive rate of an anomaly-based intrusion detection system. Fossaceca et al. ( 2015 ) focused in their research on the development of a framework that combined the outputs of multiple learners in order to improve the efficacy of network intrusion, and Gauthama Raman et al. ( 2020 ) presented a search algorithm based on Support Vector machine to improve the performance of the detection and false alarm rate to improve intrusion detection techniques. Ahmad and Alsemmeari ( 2020 ) targeted extreme learning machine techniques due to their good capabilities in classification problems and handling huge data. They used the NSL-KDD dataset as a benchmark.

With reference to prediction, Bakdash et al. ( 2018 ) used datasets from the U.S. Department of Defence to predict cyberattacks by malware. This dataset consists of weekly counts of cyber events over approximately seven years. Another prediction method was presented by Fan et al. ( 2018 ), which showed an improved integrated cybersecurity prediction method based on spatial-time analysis. Also, with reference to prediction, Ashtiani and Azgomi ( 2014 ) proposed a framework for the distributed simulation of cyberattacks based on high-level architecture. Kirubavathi and Anitha ( 2016 ) recommended an approach to detect botnets, irrespective of their structures, based on network traffic flow behaviour analysis and machine-learning techniques. Dwivedi et al. ( 2021 ) introduced a multi-parallel adaptive technique to utilise an adaption mechanism in the group of swarms for network intrusion detection. AlEroud and Karabatis ( 2018 ) presented an approach that used contextual information to automatically identify and query possible semantic links between different types of suspicious activities extracted from network flows.

Intrusion detection systems with a focus on IoT

In addition to general intrusion detection systems, a proportion of studies focused on IoT. Habib et al. ( 2020 ) presented an approach for converting traditional intrusion detection systems into smart intrusion detection systems for IoT networks. To enhance the process of diagnostic detection of possible vulnerabilities with an IoT system, Georgescu et al. ( 2019 ) introduced a method that uses a named entity recognition-based solution. With regard to IoT in the smart home sector, Heartfield et al. ( 2021 ) presented a detection system that is able to autonomously adjust the decision function of its underlying anomaly classification models to a smart home’s changing condition. Another intrusion detection system was suggested by Keserwani et al. ( 2021 ), which combined Grey Wolf Optimization and Particle Swam Optimization to identify various attacks for IoT networks. They used the KDD Cup 99, NSL-KDD and CICIDS-2017 to evaluate their model. Abu Al-Haija and Zein-Sabatto ( 2020 ) provide a comprehensive development of a new intelligent and autonomous deep-learning-based detection and classification system for cyberattacks in IoT communication networks that leverage the power of convolutional neural networks, abbreviated as IoT-IDCS-CNN (IoT-based Intrusion Detection and Classification System using Convolutional Neural Network). To evaluate the development, the authors used the NSL-KDD dataset. Biswas and Roy ( 2021 ) recommended a model that identifies malicious botnet traffic using novel deep-learning approaches like artificial neural networks gutted recurrent units and long- or short-term memory models. They tested their model with the Bot-IoT dataset.

With a more forensic background, Koroniotis et al. ( 2020 ) submitted a network forensic framework, which described the digital investigation phases for identifying and tracing attack behaviours in IoT networks. The suggested work was evaluated with the Bot-IoT and UINSW-NB15 datasets. With a focus on big data and IoT, Chhabra et al. ( 2020 ) presented a cyber forensic framework for big data analytics in an IoT environment using machine learning. Furthermore, the authors mentioned different publicly available datasets for machine-learning models.

A stronger focus on a mobile phones was exhibited by Alazab et al. ( 2020 ), which presented a classification model that combined permission requests and application programme interface calls. The model was tested with a malware dataset containing 27,891 Android apps. A similar approach was taken by Li et al. ( 2019a , b ), who proposed a reliable classifier for Android malware detection based on factorisation machine architecture and extraction of Android app features from manifest files and source code.

Literature reviews

In addition to the different methods and models for intrusion detection systems, various literature reviews on the methods and datasets were also found. Liu and Lang ( 2019 ) proposed a taxonomy of intrusion detection systems that uses data objects as the main dimension to classify and summarise machine learning and deep learning-based intrusion detection literature. They also presented four different benchmark datasets for machine-learning detection systems. Ahmed et al. ( 2016 ) presented an in-depth analysis of four major categories of anomaly detection techniques, which include classification, statistical, information theory and clustering. Hajj et al. ( 2021 ) gave a comprehensive overview of anomaly-based intrusion detection systems. Their article gives an overview of the requirements, methods, measurements and datasets that are used in an intrusion detection system.

Within the framework of machine learning, Chattopadhyay et al. ( 2018 ) conducted a comprehensive review and meta-analysis on the application of machine-learning techniques in intrusion detection systems. They also compared different machine learning techniques in different datasets and summarised the performance. Vidros et al. ( 2017 ) presented an overview of characteristics and methods in automatic detection of online recruitment fraud. They also published an available dataset of 17,880 annotated job ads, retrieved from the use of a real-life system. An empirical study of different unsupervised learning algorithms used in the detection of unknown attacks was presented by Meira et al. ( 2020 ).

New datasets

Kilincer et al. ( 2021 ) reviewed different intrusion detection system datasets in detail. They had a closer look at the UNS-NB15, ISCX-2012, NSL-KDD and CIDDS-001 datasets. Stojanovic et al. ( 2020 ) also provided a review on datasets and their creation for use in advanced persistent threat detection in the literature. Another review of datasets was provided by Sarker et al. ( 2020 ), who focused on cybersecurity data science as part of their research and provided an overview from a machine-learning perspective. Avila et al. ( 2021 ) conducted a systematic literature review on the use of security logs for data leak detection. They recommended a new classification of information leak, which uses the GDPR principles, identified the most widely publicly available dataset for threat detection, described the attack types in the datasets and the algorithms used for data leak detection. Tuncer et al. ( 2020 ) presented a bytecode-based detection method consisting of feature extraction using local neighbourhood binary patterns. They chose a byte-based malware dataset to investigate the performance of the proposed local neighbourhood binary pattern-based detection method. With a different focus, Mauro et al. ( 2020 ) gave an experimental overview of neural-based techniques relevant to intrusion detection. They assessed the value of neural networks using the Bot-IoT and UNSW-DB15 datasets.

Another category of results in the context of countermeasure datasets is those that were presented as new. Moreno et al. ( 2018 ) developed a database of 300 security-related accidents from European and American sources. The database contained cybersecurity-related events in the chemical and process industry. Damasevicius et al. ( 2020 ) proposed a new dataset (LITNET-2020) for network intrusion detection. The dataset is a new annotated network benchmark dataset obtained from the real-world academic network. It presents real-world examples of normal and under-attack network traffic. With a focus on IoT intrusion detection systems, Alsaedi et al. ( 2020 ) proposed a new benchmark IoT/IIot datasets for assessing intrusion detection system-enabled IoT systems. Also in the context of IoT, Vaccari et al. ( 2020 ) proposed a dataset focusing on message queue telemetry transport protocols, which can be used to train machine-learning models. To evaluate the performance of machine-learning classifiers, Mahfouz et al. ( 2020 ) created a dataset called Game Theory and Cybersecurity (GTCS). A dataset containing 22,000 malware and benign samples was constructed by Martin et al. ( 2019 ). The dataset can be used as a benchmark to test the algorithm for Android malware classification and clustering techniques. In addition, Laso et al. ( 2017 ) presented a dataset created to investigate how data and information quality estimates enable the detection of anomalies and malicious acts in cyber-physical systems. The dataset contained various cyberattacks and is publicly available.

In addition to the results described above, several other studies were found that fit into the category of countermeasures. Johnson et al. ( 2016 ) examined the time between vulnerability disclosures. Using another vulnerabilities database, Common Vulnerabilities and Exposures (CVE), Subroto and Apriyana ( 2019 ) presented an algorithm model that uses big data analysis of social media and statistical machine learning to predict cyber risks. A similar databank but with a different focus, Common Vulnerability Scoring System, was used by Chatterjee and Thekdi ( 2020 ) to present an iterative data-driven learning approach to vulnerability assessment and management for complex systems. Using the CICIDS2017 dataset to evaluate the performance, Malik et al. ( 2020 ) proposed a control plane-based orchestration for varied, sophisticated threats and attacks. The same dataset was used in another study by Lee et al. ( 2019 ), who developed an artificial security information event management system based on a combination of event profiling for data processing and different artificial network methods. To exploit the interdependence between multiple series, Fang et al. ( 2021 ) proposed a statistical framework. In order to validate the framework, the authors applied it to a dataset of enterprise-level security breaches from the Privacy Rights Clearinghouse and Identity Theft Center database. Another framework with a defensive aspect was recommended by Li et al. ( 2021 ) to increase the robustness of deep neural networks against adversarial malware evasion attacks. Sarabi et al. ( 2016 ) investigated whether and to what extent business details can help assess an organisation's risk of data breaches and the distribution of risk across different types of incidents to create policies for protection, detection and recovery from different forms of security incidents. They used data from the VERIS Community Database.

Datasets that have been classified into the cybersecurity category are detailed in Supplementary Table 3. Due to overlap, records from the previous tables may also be included.

This paper presented a systematic literature review of studies on cyber risk and cybersecurity that used datasets. Within this framework, 255 studies were fully reviewed and then classified into three different categories. Then, 79 datasets were consolidated from these studies. These datasets were subsequently analysed, and important information was selected through a process of filtering out. This information was recorded in a table and enhanced with further information as part of the literature analysis. This made it possible to create a comprehensive overview of the datasets. For example, each dataset contains a description of where the data came from and how the data has been used to date. This allows different datasets to be compared and the appropriate dataset for the use case to be selected. This research certainly has limitations, so our selection of datasets cannot necessarily be taken as a representation of all available datasets related to cyber risks and cybersecurity. For example, literature searches were conducted in four academic databases and only found datasets that were used in the literature. Many research projects also used old datasets that may no longer consider current developments. In addition, the data are often focused on only one observation and are limited in scope. For example, the datasets can only be applied to specific contexts and are also subject to further limitations (e.g. region, industry, operating system). In the context of the applicability of the datasets, it is unfortunately not possible to make a clear statement on the extent to which they can be integrated into academic or practical areas of application or how great this effort is. Finally, it remains to be pointed out that this is an overview of currently available datasets, which are subject to constant change.

Due to the lack of datasets on cyber risks in the academic literature, additional datasets on cyber risks were integrated as part of a further search. The search was conducted on the Google Dataset search portal. The search term used was ‘cyber risk datasets’. Over 100 results were found. However, due to the low significance and verifiability, only 20 selected datasets were included. These can be found in Table 2  in the “ Appendix ”.

Summary of Google datasets

The results of the literature review and datasets also showed that there continues to be a lack of available, open cyber datasets. This lack of data is reflected in cyber insurance, for example, as it is difficult to find a risk-based premium without a sufficient database (Nurse et al. 2020 ). The global cyber insurance market was estimated at USD 5.5 billion in 2020 (Dyson 2020 ). When compared to the USD 1 trillion global losses from cybercrime (Maleks Smith et al. 2020 ), it is clear that there exists a significant cyber risk awareness challenge for both the insurance industry and international commerce. Without comprehensive and qualitative data on cyber losses, it can be difficult to estimate potential losses from cyberattacks and price cyber insurance accordingly (GAO 2021 ). For instance, the average cyber insurance loss increased from USD 145,000 in 2019 to USD 359,000 in 2020 (FitchRatings 2021 ). Cyber insurance is an important risk management tool to mitigate the financial impact of cybercrime. This is particularly evident in the impact of different industries. In the Energy & Commodities financial markets, a ransomware attack on the Colonial Pipeline led to a substantial impact on the U.S. economy. As a result of the attack, about 45% of the U.S. East Coast was temporarily unable to obtain supplies of diesel, petrol and jet fuel. This caused the average price in the U.S. to rise 7 cents to USD 3.04 per gallon, the highest in seven years (Garber 2021 ). In addition, Colonial Pipeline confirmed that it paid a USD 4.4 million ransom to a hacker gang after the attack. Another ransomware attack occurred in the healthcare and government sector. The victim of this attack was the Irish Health Service Executive (HSE). A ransom payment of USD 20 million was demanded from the Irish government to restore services after the hack (Tidy 2021 ). In the car manufacturing sector, Miller and Valasek ( 2015 ) initiated a cyberattack that resulted in the recall of 1.4 million vehicles and cost manufacturers EUR 761 million. The risk that arises in the context of these events is the potential for the accumulation of cyber losses, which is why cyber insurers are not expanding their capacity. An example of this accumulation of cyber risks is the NotPetya malware attack, which originated in Russia, struck in Ukraine, and rapidly spread around the world, causing at least USD 10 billion in damage (GAO 2021 ). These events highlight the importance of proper cyber risk management.

This research provides cyber insurance stakeholders with an overview of cyber datasets. Cyber insurers can use the open datasets to improve their understanding and assessment of cyber risks. For example, the impact datasets can be used to better measure financial impacts and their frequencies. These data could be combined with existing portfolio data from cyber insurers and integrated with existing pricing tools and factors to better assess cyber risk valuation. Although most cyber insurers have sparse historical cyber policy and claims data, they remain too small at present for accurate prediction (Bessy-Roland et al. 2021 ). A combination of portfolio data and external datasets would support risk-adjusted pricing for cyber insurance, which would also benefit policyholders. In addition, cyber insurance stakeholders can use the datasets to identify patterns and make better predictions, which would benefit sustainable cyber insurance coverage. In terms of cyber risk cause datasets, cyber insurers can use the data to review their insurance products. For example, the data could provide information on which cyber risks have not been sufficiently considered in product design or where improvements are needed. A combination of cyber cause and cybersecurity datasets can help establish uniform definitions to provide greater transparency and clarity. Consistent terminology could lead to a more sustainable cyber market, where cyber insurers make informed decisions about the level of coverage and policyholders understand their coverage (The Geneva Association 2020).

In addition to the cyber insurance community, this research also supports cybersecurity stakeholders. The reviewed literature can be used to provide a contemporary, contextual and categorised summary of available datasets. This supports efficient and timely progress in cyber risk research and is beneficial given the dynamic nature of cyber risks. With the help of the described cybersecurity datasets and the identified information, a comparison of different datasets is possible. The datasets can be used to evaluate the effectiveness of countermeasures in simulated cyberattacks or to test intrusion detection systems.

In this paper, we conducted a systematic review of studies on cyber risk and cybersecurity databases. We found that most of the datasets are in the field of intrusion detection and machine learning and are used for technical cybersecurity aspects. The available datasets on cyber risks were relatively less represented. Due to the dynamic nature and lack of historical data, assessing and understanding cyber risk is a major challenge for cyber insurance stakeholders. To address this challenge, a greater density of cyber data is needed to support cyber insurers in risk management and researchers with cyber risk-related topics. With reference to ‘Open Science’ FAIR data (Jacobsen et al. 2020 ), mandatory reporting of cyber incidents could help improve cyber understanding, awareness and loss prevention among companies and insurers. Through greater availability of data, cyber risks can be better understood, enabling researchers to conduct more in-depth research into these risks. Companies could incorporate this new knowledge into their corporate culture to reduce cyber risks. For insurance companies, this would have the advantage that all insurers would have the same understanding of cyber risks, which would support sustainable risk-based pricing. In addition, common definitions of cyber risks could be derived from new data.

The cybersecurity databases summarised and categorised in this research could provide a different perspective on cyber risks that would enable the formulation of common definitions in cyber policies. The datasets can help companies addressing cybersecurity and cyber risk as part of risk management assess their internal cyber posture and cybersecurity measures. The paper can also help improve risk awareness and corporate behaviour, and provides the research community with a comprehensive overview of peer-reviewed datasets and other available datasets in the area of cyber risk and cybersecurity. This approach is intended to support the free availability of data for research. The complete tabulated review of the literature is included in the Supplementary Material.

This work provides directions for several paths of future work. First, there are currently few publicly available datasets for cyber risk and cybersecurity. The older datasets that are still widely used no longer reflect today's technical environment. Moreover, they can often only be used in one context, and the scope of the samples is very limited. It would be of great value if more datasets were publicly available that reflect current environmental conditions. This could help intrusion detection systems to consider current events and thus lead to a higher success rate. It could also compensate for the disadvantages of older datasets by collecting larger quantities of samples and making this contextualisation more widespread. Another area of research may be the integratability and adaptability of cybersecurity and cyber risk datasets. For example, it is often unclear to what extent datasets can be integrated or adapted to existing data. For cyber risks and cybersecurity, it would be helpful to know what requirements need to be met or what is needed to use the datasets appropriately. In addition, it would certainly be helpful to know whether datasets can be modified to be used for cyber risks or cybersecurity. Finally, the ability for stakeholders to identify machine-readable cybersecurity datasets would be useful because it would allow for even clearer delineations or comparisons between datasets. Due to the lack of publicly available datasets, concrete benchmarks often cannot be applied.

Below is the link to the electronic supplementary material.

Biographies

is a PhD student at the Kemmy Business School, University of Limerick, as part of the Emerging Risk Group (ERG). He is researching in joint cooperation with the Institute for Insurance Studies (ivwKöln), TH Köln, where he is working as a Research Assistant at the Cologne Research Centre for Reinsurance. His current research interests include cyber risks, cyber insurance and cybersecurity. Frank is a Fellow of the Chartered Insurance Institute (FCII) and a member of the German Association for Insurance Studies (DVfVW).

is a Lecturer in Risk and Finance at the Kemmy Business School at the University of Limerick. In his research, Dr Sheehan investigates novel risk metrication and machine learning methodologies in the context of insurance and finance, attentive to a changing private and public emerging risk environment. He is a researcher with significant insurance industry and academic experience. With a professional background in actuarial science, his research uses machine-learning techniques to estimate the changing risk profile produced by emerging technologies. He is a senior member of the Emerging Risk Group (ERG) at the University of Limerick, which has long-established expertise in insurance and risk management and has continued success within large research consortia including a number of SFI, FP7 and EU H2020 research projects. In particular, he contributed to the successful completion of three Horizon 2020 EU-funded projects, including PROTECT, Vision Inspired Driver Assistance Systems (VI-DAS) and Cloud Large Scale Video Analysis (Cloud-LSVA).

is a Professor at the Institute of Insurance at the Technical University of Cologne. His activities include teaching and research in insurance law and liability insurance. His research focuses include D&O, corporate liability, fidelity and cyber insurance. In addition, he heads the Master’s degree programme in insurance law and is the Academic Director of the Automotive Insurance Manager and Cyber Insurance Manager certificate programmes. He is also chairman of the examination board at the Institute of Insurance Studies.

Arash Negahdari Kia

is a postdoctoral Marie Cuire scholar and Research Fellow at the Kemmy Business School (KBS), University of Limerick (UL), a member of the Lero Software Research Center and Emerging Risk Group (ERG). He researches the cybersecurity risks of autonomous vehicles using machine-learning algorithms in a team supervised by Dr Finbarr Murphy at KBS, UL. For his PhD, he developed two graph-based, semi-supervised algorithms for multivariate time series for global stock market indices prediction. For his Master’s, he developed neural network models for Forex market prediction. Arash’s other research interests include text mining, graph mining and bioinformatics.

is a Professor in Risk and Insurance at the Kemmy Business School, University of Limerick. He worked on a number of insurance-related research projects, including four EU Commission-funded projects around emerging technologies and risk transfer. Prof. Mullins maintains strong links with the international insurance industry and works closely with Lloyd’s of London and XL Catlin on emerging risk. His work also encompasses the area of applied ethics as it pertains to new technologies. In the field of applied ethics, Dr Mullins works closely with the insurance industry and lectures on cultural and technological breakthroughs of high societal relevance. In that respect, Dr Martin Mullins has been appointed to a European expert group to advise EIOPA on the development of digital responsibility principles in insurance.

is Executive Dean Kemmy Business School. A computer engineering graduate, Finbarr worked for over 10 years in investment banking before returning to academia and completing his PhD in 2010. Finbarr has authored or co-authored over 70 refereed journal papers, edited books and book chapters. His research has been published in leading research journals in his discipline, such as Nature Nanotechnology, Small, Transportation Research A-F and the Review of Derivatives Research. A former Fulbright Scholar and Erasmus Mundus Exchange Scholar, Finbarr has delivered numerous guest lectures in America, mainland Europe, Israel, Russia, China and Vietnam. His research interests include quantitative finance and, more recently, emerging technological risk. Finbarr is currently engaged in several EU H2020 projects and with the Irish Science Foundation Ireland.

(FCII) has held the Chair of Reinsurance at the Institute of Insurance of TH Köln since 1998, focusing on the efficiency of reinsurance, industrial insurance and alternative risk transfer (ART). He studied mathematics and computer science with a focus on artificial intelligence and researched from 1988 to 1991 at the Fraunhofer Institute for Autonomous Intelligent Systems (AiS) in Schloß Birlinghoven. From 1991 to 2004, Prof. Materne worked for Gen Re (formerly Cologne Re) in various management positions in Germany and abroad, and from 2001 to 2003, he served as General Manager of Cologne Re of Dublin in Ireland. In 2008, Prof. Materne founded the Cologne Reinsurance Research Centre, of which he is the Director. Current issues in reinsurance and related fields are analysed and discussed with practitioners, with valuable contacts through the ‘Förderkreis Rückversicherung’ and the organisation of the annual Cologne Reinsurance Symposium. Prof. Materne holds various international supervisory boards, board of directors and advisory board mandates at insurance and reinsurance companies, captives, InsurTechs, EIOPA, as well as at insurance-scientific institutions. He also acts as an arbitrator and party representative in arbitration proceedings.

Open Access funding provided by the IReL Consortium.

Declarations

On behalf of all authors, the corresponding author states that there is no conflict of interest.

1 Average cost of a breach of more than 50 million records.

Publisher's Note

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A Survey of Challenges Associated with Cloud Computing Security

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• D. I. George Amalarethinam 8 &
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An increasing number of businesses are using cloud computing to maintain and manage data and applications using the internet and a remote server. Resource provisioning, facilitated by cloud computing, involves the delivery of computer system resources over the Internet with pay-per-use pricing. It includes servers, storage, databases, networking, software, analytics, application and intelligence. Cloud computing provides us with easy access to a wide variety of technologies. A versatile, economical, and tested platform for delivering commercial or consumer IT services online is employed. The storage of data, which is the core feature of cloud computing, is facilitated by cloud storage, which offers data security through the utilization of technologies like the Internet, virtualization, encryption, hashing, digital signatures, public key infrastructure, and single sign-on. This paper’s purpose is to present a survey of various Security Challenges and methodologies which are proposed by many researchers for securing Cloud-based data. Also, this study explores the many security concerns related to cloud computing service delivery architectures.

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Amalarethinam, D.I.G., Vinnarasi, J. (2024). A Survey of Challenges Associated with Cloud Computing Security. In: Mahmud, M., Ben-Abdallah, H., Kaiser, M.S., Ahmed, M.R., Zhong, N. (eds) Applied Intelligence and Informatics. AII 2023. Communications in Computer and Information Science, vol 2065. Springer, Cham. https://doi.org/10.1007/978-3-031-68639-9_31

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Review of smart-home security using the internet of things, 1. introduction.

• User awareness level: Users are always considered one of the weak links on the cyber risk chain, and usually, home users are not aware of the cyber threats related to smart-home devices, or may not know how to properly secure them. This can lead to weak passwords, false configurations, failure to update software, and other security vulnerabilities.
• Complexity: Smart homes can be complex systems with many devices, sensors, and services. This complexity can make it difficult to manage security and identify vulnerabilities.
• Interoperability and Heterogeneity: Smart-home devices are often developed by different manufacturers and use different communication protocols, which makes it difficult to ensure that they can all work together securely.
• Remote access: Smart-home devices often allow for remote access via Internet connection, which increases the risk of unauthorized access by attackers. This is especially concerning as many users may not secure their remote access properly.
• Limited resources: Smart-home devices often operate with limited resources, such as low power consumption, limited memory, and processing power. This makes it challenging to implement strong security protocols.
• Lack of regulation: There are currently no standardized security regulations for smart-home devices, which means that security measures can vary widely between different devices and manufacturers.

2. Related Works

3. smart-home ecosystem, 3.1. iot in smart homes, 3.2. infotainment devices, 3.3. physical home security and monitoring, 3.4. ambient living devices, 3.5. other aspects and actuators, 4. analysis and discussions of the iot setting, 4.1. architectural structure of iot ecosystems.

• International Organization for Standardization (ISO) and International Electrotechnical Commission (IEC): The ISO and IEC have developed several standards related to IoT, including ISO/IEC 30141 [ 93 ] which provides guidelines for the architecture and interoperability of IoT systems. While the ISO/IEC standards may not explicitly define the layers of an IoT architecture, they offer principles and recommendations for designing scalable and interoperable IoT solutions.
• Institute of Electrical and Electronics Engineers (IEEE): The IEEE has published numerous standards and guidelines for IoT, including IEEE P2413 [ 94 ] which defines an architectural framework for IoT. While IEEE P2413 does not prescribe specific layers, it outlines key architectural concepts and considerations for IoT systems.
• Industrial Internet Consortium (IIC): The IIC has developed the Industrial Internet Reference Architecture (IIRA) [ 95 ], which provides a comprehensive framework for designing industrial IoT solutions. While focused on industrial applications, the IIRA can be adapted for other IoT use-cases and includes concepts related to layered architectures.
• Open Connectivity Foundation (OCF): The OCF has developed standards for interoperability and connectivity in IoT devices and ecosystems [ 96 ]. While the OCF standards primarily focus on interoperability protocols, they also address architectural principles that may align with layered approaches.
• National Institute of Standards and Technology (NIST): The NIST has published several documents related to IoT security and architecture, including the NIST Cybersecurity Framework [ 97 ] and NIST Special Publication 800-183 [ 98 ], which provide guidance on IoT device cybersecurity. While not explicitly defining layers, these documents offer principles and recommendations for designing secure IoT architectures.
• Application: smart environment, smart home, smart city;
• Perception: movement sensors, smoke sensors, pressure sensors;
• Network: nodes, servers, topologies;
• Physical: smart phones, smart appliances, power supplies.

4.2. Threats Faced by a Smart Home and Related Countermeasures

4.2.1. application layer analysis, 4.2.2. perception layer analysis, 4.2.3. network layer analysis, 4.2.4. physical layer analysis, 4.2.5. summary of threats and countermeasures, 5. best-practices guide for a secure smart home.

• Identify Needs: Determine what you want your smart-home devices to accomplish. This could range from enhancing security to improve energy-efficiency or simply adding convenience to your daily routines.
• Select Compatible Devices: Choose devices that are compatible with each other and can be easily integrated into a single ecosystem. Look for devices that support common standards or platforms (e.g., Apple HomeKit, Google Home, Amazon Alexa).
• Update/Upgrade Regularly: Set a process to automatically or periodically seek for and install updates/upgrades. Both for firmware and application software.
• Disposal Policy: Set safe disposal strategies for all equipment, including secure deletion/destruction of data and even physical destruction of digital components and memories/storage.
• Device’s Security Controls: Set all potential defenses that are provided by the main manufacturer (e.g., pins, extra security code, networking safeguards, etc.).
• Minimize Exposure: Restrict connectivity to the least open/public LANs and networks that are necessary. Minimize the exposure of the system.
• Set User Privileges: Restrict the number of authorized users to the minimum required. For each user, restrict access rights to the least privileges required.
• Security Software: Set anti-virus, anti-malware, host-firewall, and host-IDS where applicable.
• Delete Unnecessary Elements: Remove services, applications, or other elements that are insecure or not in use by the current system (e.g., Telnet).
• Avoid Outdated Equipment: Do not use outdated devices that are not supported by the vendor anymore.
• Configure Before Deployment: Before incorporating a new device to your system, verify that it is updated/upgraded, and all security and configurations are properly set.
• Use Only Secure Versions: Install the latest secure and stable versions.
• Set And Update Before Use: Upon installation, proceed immediately with the proper updates/upgrades, configurations, and settings of security/privacy.
• Automate Updates: Set automated or periodic updates/upgrades.
• Strong Authentication: Use strong passwords, as well as multi-factor authentication, wherever possible.
• Application-Level Protections: Enable application-level firewalls, IDS, extra pins, or other defenses, wherever possible.
• Restrict Access: Restrict access rights/permissions and connectivity to the minimum required.
• Restrict Users: If applicable, restrict the number of users to the minimum required.
• Especially for the technicians/engineers, always check the validity of the elements that are about to be installed (e.g., check the digital certification of the website, as well as the digest of the downloaded software).
• For elements of unknown or less popular vendors, also check for recommendations from other users in related forums.
• Do not install less trusted applications/software in the core of the system, especially if you have not tested them in a less critical part of your setting.
• Monitor Operation: Where applicable, install security software for monitoring of the runtime environment and alerting.
• Set Build-In Security and Privacy Controls: Check the offered options and set the privacy policies to the minimum required.
• Secure Deletion: Apply secure removal strategies, logging out from all accounts and applications, revoking all acquired accesses/permissions, and securely erasing all permanent and temporary data.
• Respond to Incidents: Set a response strategy, including details of whom you have to call and your first actions in case you notice something strange. For example, if you start receiving unknown notifications of purchase attempts in your mobile banking, you block your cards immediately and call your bank’s 24/7 security service.
• Recover from Incidents: Set a recovery strategy in case something happens.
• Security Configuration: Set the highest possible protections and set as a high priority the protection of the equipment that facilitates networking, especially for the devices that have direct access/exposure to Internet, and especially the main router.
• Security Primitives: Activate or set additional firewalls and IDS/IPS. Use the most restrict policies possible.
• Usage Zones: Create different LANs and virtual LANs (VLANs) for different usage zones of the smart home.
• Remote Access: Consider setting an in-house virtual private network (VPN) for accessing the smart home remotely (e.g., the surveillance system).
• Least Privileges: Restrict the privileges of services that are exposed to the Internet.
• Network Monitoring: Install monitoring systems and periodically audit the activity of your system.
• Decrease Attack Surface: Disable insecure communication protocols and services (e.g., http), as well as elements that are not currently in use.
• Regular Updates: Similar with the devices, do not use outdated equipment.
• Strong Passwords: Use strong, unique passwords for your Wi-Fi network and each of your smart-home devices.
• Network Segmentation: Consider creating a separate Wi-Fi network for your smart devices to isolate them from the network you use for personal computing, reducing the risk of cross-device hacking.
• Regular Updates: Keep your router’s firmware and your smart devices’ software up to date to protect against known vulnerabilities.
• Follow Installation Guides: Carefully read and follow the installation instructions provided with your devices. This may include downloading an application, connecting to Wi-Fi, or performing initial setup steps.
• Optimal Placement: Place devices in locations where they can function effectively (e.g., smart cameras with a clear field of view, smart thermostats away from direct sunlight).
• Choose a Central Control System: Select a central hub or application that can control all your devices. This unifies control and makes managing your devices more convenient.
• Customize Settings: Adjust settings for each device according to your preferences. This may involve setting schedules, creating automation rules, or defining scenes.
• Test Operations: After setting up, test your devices individually and the system as a whole to ensure they work as expected.
• Troubleshoot Issues: If a device is not working correctly, consult the troubleshooting section of the device manual or contact customer support.
• User Training: Educate all household members on how to use the smart devices, emphasizing the importance of security practices, like not sharing passwords.
• Manage Your Passwords and Accounts: Consider utilizing password/account managers.
• Backup Your Data: Set a backup strategy.
• Regular Reviews: Regularly review your smart-home setup to ensure it continues to meet your needs. Adjust settings, add new devices, or remove unnecessary ones as needed.

6. Directions for Future Research

• Enhanced Biometric Security: Developing more sophisticated biometric authentication methods that leverage the unique capabilities of smart-home devices.
• Context-Aware Security Protocols: Creating security protocols that adapt to the user’s context and environment within the smart home.
• Decentralized Security Mechanisms: Exploring blockchain and other decentralized technologies for managing identity verification and ensuring data integrity.
• Privacy-Enhancing Techniques: Developing methods for protecting personal data captured by smart-home devices, using advanced anonymization techniques and local data processing.
• Secure Multi-User Interactions: Enhancing security for environments where multiple users interact with the same devices, like smart TV or AR/VR equipment.
• Robust Malware Detection: Implementing sophisticated systems for detecting malware in IoT devices, including smart locks and cameras.
• Physical and Network Security Integration: Investigating ways to integrate physical security measures with network security protocols across smart-home devices.
• Energy-Efficient Security Protocols: Creating security solutions that minimize energy consumption, particularly for devices like smart locks and smart plugs.
• Secure Device Management and Disposal: Ensuring secure lifecycle management of smart-home devices, from installation to disposal, to prevent data leaks.
• International Security Standards for IoT: Developing and promoting the adoption of global security standards for IoT devices to ensure consistent security practices.
• Anomaly Detection Using AI: Leveraging AI to detect and respond to unusual behavior or threats in smart-home environments.
• IoT Device Interoperability and Security: Ensuring that all interconnected smart-home devices adhere to strict security protocols to prevent vulnerabilities.
• Ethical Design and User Consent: Examining ethical issues in smart-home technology deployment, especially regarding surveillance and data-collection practices.
• Forensic Capabilities for IoT Security: Developing forensic tools and techniques for investigating and mitigating security incidents in smart homes.
• Consumer Awareness and Education: Enhancing user understanding of the potential risks and security practices associated with smart-home technologies.
• Regulatory Compliance and Privacy Laws: Addressing compliance with existing and emerging privacy laws and regulations that affect smart-home technologies.
• Advanced Encryption Methods: Researching more robust encryption techniques to secure data transmission between smart-home devices and external networks.
• Hybrid Energy-Efficient Privacy Preserving Schemes: Developing privacy-preserving protocols that balance energy efficiency with effective privacy protection, especially in communication-heavy IoT environments, like smart homes.

7. Conclusions

Author contributions, data availability statement, acknowledgments, conflicts of interest.

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Vardakis, G.; Hatzivasilis, G.; Koutsaki, E.; Papadakis, N. Review of Smart-Home Security Using the Internet of Things. Electronics 2024 , 13 , 3343. https://doi.org/10.3390/electronics13163343

Vardakis G, Hatzivasilis G, Koutsaki E, Papadakis N. Review of Smart-Home Security Using the Internet of Things. Electronics . 2024; 13(16):3343. https://doi.org/10.3390/electronics13163343

Vardakis, George, George Hatzivasilis, Eleftheria Koutsaki, and Nikos Papadakis. 2024. "Review of Smart-Home Security Using the Internet of Things" Electronics 13, no. 16: 3343. https://doi.org/10.3390/electronics13163343

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