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Item does not contain fulltext, Both intentional attacks and accidental technical failures can lead to abnormal behaviour in components of industrial control systems. In our previous work, we developed a framework for constructing Bayesian Network (BN) models to enable operators to distinguish between those two classes, including knowledge elicitation to construct the directed acyclic graph of BN models. In this paper, we add a systematic method for knowledge elicitation to construct the Conditional Probability Tables (CPTs) of BN models, thereby completing a holistic framework to distinguish between attacks and technical failures. In order to elicit reliable probabilities from experts, we need to reduce the workload of experts in probability elicitation by reducing the number of conditional probabilities to elicit and facilitating individual probability entry. We utilise DeMorgan models to reduce the number of conditional probabilities to elicit as they are suitable for modelling opposing influences i.e., combinations of influences that promote and inhibit the child event. To facilitate individual probability entry, we use probability scales with numerical and verbal anchors. We demonstrate the proposed approach using an example from the water management domain.

06 juli 2023, Item does not contain fulltext, Professionals working in both the physical and cybersecurity domain need to assess and evaluate security risks. As information on risks in general and security risks in particular is often imperfect and intractable, these professionals are facing a challenge in judging both likelihood and consequences, but how much do their existing psychological biases play a role in these judgments? In this paper, we present new empirical evidence on the perception of the information position and confidence levels of security professionals, the influence of detailed information and the conjunction fallacy, and the level of noise in security assessments. This paper adds to the literature by examining, for the first time, risk assessments by professionals in realistic, real life, security cases. The results show clear indications for overconfidence, comparative ignorance, influence of the conjunction fallacy, and influence of individual experience on security decision making in the professional security domain. The observed phenomena might have far reaching effects on security risk management in organizations and society.

Both intentional attacks and accidental technical failures can lead to abnormal behaviour in components of industrial control systems. In our previous work, we developed a framework for constructing Bayesian Network (BN) models to enable operators to distinguish between those two classes, including knowledge elicitation to construct the directed acyclic graph of BN models. In this paper, we add a systematic method for knowledge elicitation to construct the Conditional Probability Tables (CPTs) of BN models, thereby completing a holistic framework to distinguish between attacks and technical failures. In order to elicit reliable probabilities from experts, we need to reduce the workload of experts in probability elicitation by reducing the number of conditional probabilities to elicit and facilitating individual probability entry. We utilise DeMorgan models to reduce the number of conditional probabilities to elicit as they are suitable for modelling opposing influences i.e., combinations of influences that promote and inhibit the child event. To facilitate individual probability entry, we use probability scales with numerical and verbal anchors. We demonstrate the proposed approach using an example from the water management domain., Safety and Security Science, Organisation & Governance

Inaugural address, 24 maart 2022, Inaugural lecture, Contains fulltext : 248275.pdf (Author’s version postprint ) (Open Access)

Inaugural address, 24 maart 2022, Inaugural lecture, Contains fulltext : 248275.pdf (Author’s version postprint ) (Open Access)

31st USENIX Security Symposium ((August 10-12, 2022, Boston, MA, USA), Item does not contain fulltext, With the worldwide COVID-19 pandemic in 2020 and 2021 necessitating working from home, corporate Virtual Private Networks (VPNs) have become an important item securing the continued operation of companies around the globe. However, due to their different use case, corporate VPNs and how users interact with them differ from public VPNs, which are now commonly used by end-users. In this paper, we present a first explorative study of eleven experts' and seven non-experts' mental models in the context of corporate VPNs. We find a partial alignment of these models in the high-level technical understanding while diverging in important parameters of how, when, and why VPNs are being used. While, in general, experts have a deeper technical understanding of VPN technology, we also observe that even they sometimes hold false beliefs on security aspects of VPNs. In summary, we show that the mental models of corporate VPNs differ from those for related security technology, e.g., HTTPS. Our findings allow us to draft recommendations for practitioners to encourage a secure use of VPN technology (through training interventions, better communication, and system design changes in terms of device management). Furthermore, we identify avenues for future research, e.g., into experts' knowledge and balancing privacy and security between system operators and users.

31st USENIX Security Symposium ((August 10-12, 2022, Boston, MA, USA), Item does not contain fulltext, With the worldwide COVID-19 pandemic in 2020 and 2021 necessitating working from home, corporate Virtual Private Networks (VPNs) have become an important item securing the continued operation of companies around the globe. However, due to their different use case, corporate VPNs and how users interact with them differ from public VPNs, which are now commonly used by end-users. In this paper, we present a first explorative study of eleven experts' and seven non-experts' mental models in the context of corporate VPNs. We find a partial alignment of these models in the high-level technical understanding while diverging in important parameters of how, when, and why VPNs are being used. While, in general, experts have a deeper technical understanding of VPN technology, we also observe that even they sometimes hold false beliefs on security aspects of VPNs. In summary, we show that the mental models of corporate VPNs differ from those for related security technology, e.g., HTTPS. Our findings allow us to draft recommendations for practitioners to encourage a secure use of VPN technology (through training interventions, better communication, and system design changes in terms of device management). Furthermore, we identify avenues for future research, e.g., into experts' knowledge and balancing privacy and security between system operators and users.

Item does not contain fulltext, Given the significant privacy and security risks of Internet-of-Things (IoT) devices, it seems desirable to nudge consumers towards buying more secure devices and taking privacy into account in the purchase decision. In order to support this goal, this study examines the effect of security and privacy on IoT device purchase behaviour and assesses whether these effects are sensitive to framing, using a mixed methods approach. The first part of the study focuses on quantifying the effect of security and privacy compared to the effect of other device attributes such as price or functionality, by testing a causal model with choice models that have been developed from stated choice data. The second part aims to reveal the underlying mechanisms that determine the effect of privacy and security on purchase behaviour by means of a qualitative survey. The results suggest that security and privacy can strongly affect purchase behaviour, under the circumstances that privacyand security-related information is available and communicated in an understandable manner, allowing consumers to compare devices. Moreover, the results show that a description of security that focuses on gains is more effective in nudging consumers towards buying secure devices. Future efforts could build upon this study by comparing the effect of security and privacy to more device attributes, such as ease of use or cost reduction. The results can serve as a basis for interventions that nudge consumers towards buying more secure and privacy-friendly devices.

Item does not contain fulltext, Cyber operations are relatively a new phenomenon of the last two decades. During that period, they have increased in number, complexity, and agility, while their design and development have been processes well kept under secrecy. As a consequence, limited data(sets) regarding these incidents are available. Although various academic and practitioner public communities addressed some of the key points and dilemmas that surround cyber operations (such as attack, target identification and selection, and collateral damage), still methodologies and models are needed in order to plan, execute, and assess them in a responsibly and legally compliant way. Based on these facts, it is the aim of this article to propose a model that i)) estimates and classifies the effects of cyber operations, and ii) assesses proportionality in order to support targeting decisions in cyber operations. In order to do that, a multi-layered fuzzy model was designed and implemented by analysing real and virtual realistic cyber operations combined with interviews and focus groups with technical - military experts. The proposed model was evaluated on two cyber operations use cases in a focus group with four technical - military experts. Both the design and the results of the evaluation are revealed in this article.

Item does not contain fulltext

Contains fulltext : 240146.pdf (Publisher’s version ) (Open Access), Security professionals play a decisive role in security risk decision making, with important implications for security in organisations and society. Because of this subjective input in security understanding possible biases in this process is paramount. In this paper, well known biases as observed and described in prospect theory are studied in individual security risk decision making by security professionals. To this end, we distributed a questionnaire among security professionals including both original dilemmas from prospect theory and dilemmas adapted to the context of incident prevention. It was hypothesised that security professionals dealing with risks and decision making under risk on an almost daily basis would or should be less vulnerable to decision biases involving risks, in particular when framed in terms of incident prevention. The results show that security professionals are vulnerable to decision biases at the same scale as lay people, but some biases are weaker when decision problems are framed in terms of security as opposed to monetary gains and losses. Of the individual characteristics defining experience, only the general education level observably affects vulnerability for biases in security decision making in this study. A higher general education level leads to a significantly higher vulnerability to decision biases. By highlighting the vulnerability of security professionals to decision biases, this study contributes essential awareness and knowledge for improved decision making, for example by different representation of probabilities and uncertainty.

Item does not contain fulltext, In this contribution, we cover the major philosophical concepts describing the field of security engineering, drawing on literature from philosophy, criminology, and engineering disciplines, with a focus on computer science and cybersecurity. We show that security distinguishes itself as a topic in engineering because it entails a combination of (a) dealing with adversaries and adversarial risk, (b) the intricacies of non-functional requirements and associated verifiability problems, (c) poorly established guidelines for a "science of security" and associated metrics, and (d) a heavily politicized context. While outlining these topics, we will touch upon several key debates in the field, such as secret versus transparent security, and user versus designer responsibility.

Item does not contain fulltext, Individuals are regularly made responsible for risks they wish to take: one can consent to processing of personal data, and decide what to buy based on risk information on product labels. However, both large-scale processing of personal data and aggregated product choices may carry collective risks for society. In such situations, governance arrangements implying individual responsibility are at odds with uncertain collective risks from new technologies. We, therefore, investigate the governance challenges of what we call risk personalization: a form of governance for dealing with uncertain collective risks that allocates responsibility for governing those risks to individuals. We situate risk personalization at the intersection of two trends: governance of uncertain risk, and emphasis on individual responsibility. We then analyze three cases selected based on diversity: social media, nanomaterials, and Uber. Cross-case comparison highlights issues of risk personalization pertaining to (i) the nature of the risk, (ii) governance arrangements in place, and (iii) mechanisms for allocating responsibility to individuals. We identify governance challenges in terms of (i) meaningful choice, (ii) effectiveness in mitigating risk, and (iii) collective decision making capacity. We conclude that the risk personalization lens stimulates reflection on the effectiveness and legitimacy of risk governance in light of individual agency.

Item does not contain fulltext

Item does not contain fulltext, Risk analysis is an essential methodology for cybersecurity as it allows organizations to deal with cyber threats potentially affecting them, prioritize the defense of their assets, and decide what security controls should be implemented. Many risk analysis methods are present in cybersecurity models, compliance frameworks, and international standards. However, most of them employ risk matrices, which suffer shortcomings that may lead to suboptimal resource allocations. We propose a comprehensive framework for cybersecurity risk analysis, covering the presence of both intentional and nonintentional threats and the use of insurance as part of the security portfolio. A simplified case study illustrates the proposed framework, serving as template for more complex problems.

Contains fulltext : 237091.pdf (Publisher’s version ) (Open Access), In this paper, we provide an overview of how Safe-by-Design is conceived and applied in practice in a large number of engineering disciplines. We discuss the differences, commonalities, and possibilities for mutual learning found in those practices and identify several ways of putting those disciplinary outlooks in perspective. The considered engineering disciplines in the order of historically grown technologies are construction engineering, chemical engineering, aerospace engineering, urban engineering, software engineering, bio-engineering, nano-engineering, and finally cyber space engineering. Each discipline is briefly introduced, the technology at issue is described, the relevant or dominant hazards are examined, the social challenge(s) are observed, and the relevant developments in the field are described. Within each discipline the risk management strategies, the design principles promoting safety or safety awareness, and associated methods or tools are discussed. Possible dilemmas that the designers in the discipline face are highlighted. Each discipline is concluded by discussing the opportunities and bottlenecks in addressing safety. Commonalities and differences between the engineering disciplines are investigated, specifically on the design strategies for which empirical data have been collected. We argue that Safe-by-Design is best considered as a specific elaboration of Responsible Research and Innovation, with an explicit focus on safety in relation to other important values in engineering such as well-being, sustainability, equity, and affordability. Safe-by-Design provides for an intellectual venue where social science and the humanities (SSH) collaborate on technological developments and innovation by helping to proactively incorporate safety considerations into engineering practices, while navigating between the extremes of technological optimism and disproportionate precaution. As such, Safe-by-Design is also a practical tool for policymakers an

Contains fulltext : 237105.pdf (Publisher’s version ) (Open Access), Water management infrastructures such as floodgates are critical and increasingly operated by Industrial Control Systems (ICS). These systems are becoming more connected to the internet, either directly or through the corporate networks. This makes them vulnerable to cyber-attacks. Abnormal behaviour in floodgates operated by ICS could be caused by both (intentional) attacks and (accidental) technical failures. When operators notice abnormal behaviour, they should be able to distinguish between those two causes to take appropriate measures, because for example replacing a sensor in case of intentional incorrect sensor measurements would be ineffective and would not block corresponding the attack vector. In the previous work, we developed the attack-failure distinguisher framework for constructing Bayesian Network (BN) models to enable operators to distinguish between those two causes, including the knowledge elicitation method to construct the directed acyclic graph and conditional probability tables of BN models. As a full case study of the attack-failure distinguisher framework, this paper presents a BN model constructed to distinguish between attacks and technical failures for the problem of incorrect sensor measurements in floodgates, addressing the problem of floodgate operators. We utilised experts who associate themselves with the safety and/or security community to construct the BN model and validate the qualitative part of constructed BN model. The constructed BN model is usable in water management infrastructures to distinguish between intentional attacks and accidental technical failures in case of incorrect sensor measurements. This could help to decide on appropriate response strategies and avoid further complications in case of incorrect sensor measurements.

Risk assessments in the (cyber) security domain are often, if not always, based on subjective expert judgement. For the first time, to the best of our knowledge, the individual preferences of professionals from the security domain are studied. In on online survey they are asked to mention, rate and rank their preferences when assessing a security risk. The survey setup allows to differentiate between easily accessible or “on top of mind” attributes and guided or stimulated attributes. The security professionals are also challenged to both non-compensatory and compensatory decision making on the relevance of the attributes. The results of this explorative study indicate a clear difference and shift in the individual perceived relevance of attributes in these different settings. Another remarkable finding of this study is the predominant focus on impact attributes by the respondents and the less significant position of likelihood or probability. The majority of professionals seem to ignore likelihood in their security risk assessment. This might be due to so called probability neglect as introduced by other scholars. the security in organisations and society is depending on the assessment and judgement of these professionals, understanding their preferences and the influence of cognitive biases is paramount. This study contributes to this body of knowledge and might raise attention to this important topic in both the academic and professional security domain., Safety and Security Science

Water management infrastructures such as floodgates are critical and increasingly operated by Industrial Control Systems (ICS). These systems are becoming more connected to the internet, either directly or through the corporate networks. This makes them vulnerable to cyber-attacks. Abnormal behaviour in floodgates operated by ICS could be caused by both (intentional) attacks and (accidental) technical failures. When operators notice abnormal behaviour, they should be able to distinguish between those two causes to take appropriate measures, because for example replacing a sensor in case of intentional incorrect sensor measurements would be ineffective and would not block corresponding the attack vector. In the previous work, we developed the attack-failure distinguisher framework for constructing Bayesian Network (BN) models to enable operators to distinguish between those two causes, including the knowledge elicitation method to construct the directed acyclic graph and conditional probability tables of BN models. As a full case study of the attack-failure distinguisher framework, this paper presents a BN model constructed to distinguish between attacks and technical failures for the problem of incorrect sensor measurements in floodgates, addressing the problem of floodgate operators. We utilised experts who associate themselves with the safety and/or security community to construct the BN model and validate the qualitative part of constructed BN model. The constructed BN model is usable in water management infrastructures to distinguish between intentional attacks and accidental technical failures in case of incorrect sensor measurements. This could help to decide on appropriate response strategies and avoid further complications in case of incorrect sensor measurements., Safety and Security Science, Organisation and Governance

Given the significant privacy and security risks of Internet-of-Things (IoT) devices, it seems desirable to nudge consumers towards buying more secure devices and taking privacy into account in the purchase decision. In order to support this goal, this study examines the effect of security and privacy on IoT device purchase behaviour and assesses whether these effects are sensitive to framing, using a mixed methods approach. The first part of the study focuses on quantifying the effect of security and privacy compared to the effect of other device attributes such as price or functionality, by testing a causal model with choice models that have been developed from stated choice data. The second part aims to reveal the underlying mechanisms that determine the effect of privacy and security on purchase behaviour by means of a qualitative survey. The results suggest that security and privacy can strongly affect purchase behaviour, under the circumstances that privacy- and security-related information is available and communicated in an understandable manner, allowing consumers to compare devices. Moreover, the results show that a description of security that focuses on gains is more effective in nudging consumers towards buying secure devices. Future efforts could build upon this study by comparing the effect of security and privacy to more device attributes, such as ease of use or cost reduction. The results can serve as a basis for interventions that nudge consumers towards buying more secure and privacy-friendly devices., Organisation and Governance, Transport and Logistics

Contains fulltext : 240148.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 240146.pdf (Publisher’s version ) (Open Access), Security professionals play a decisive role in security risk decision making, with important implications for security in organisations and society. Because of this subjective input in security understanding possible biases in this process is paramount. In this paper, well known biases as observed and described in prospect theory are studied in individual security risk decision making by security professionals. To this end, we distributed a questionnaire among security professionals including both original dilemmas from prospect theory and dilemmas adapted to the context of incident prevention. It was hypothesised that security professionals dealing with risks and decision making under risk on an almost daily basis would or should be less vulnerable to decision biases involving risks, in particular when framed in terms of incident prevention. The results show that security professionals are vulnerable to decision biases at the same scale as lay people, but some biases are weaker when decision problems are framed in terms of security as opposed to monetary gains and losses. Of the individual characteristics defining experience, only the general education level observably affects vulnerability for biases in security decision making in this study. A higher general education level leads to a significantly higher vulnerability to decision biases. By highlighting the vulnerability of security professionals to decision biases, this study contributes essential awareness and knowledge for improved decision making, for example by different representation of probabilities and uncertainty.

Item does not contain fulltext, In this contribution, we cover the major philosophical concepts describing the field of security engineering, drawing on literature from philosophy, criminology, and engineering disciplines, with a focus on computer science and cybersecurity. We show that security distinguishes itself as a topic in engineering because it entails a combination of (a) dealing with adversaries and adversarial risk, (b) the intricacies of non-functional requirements and associated verifiability problems, (c) poorly established guidelines for a "science of security" and associated metrics, and (d) a heavily politicized context. While outlining these topics, we will touch upon several key debates in the field, such as secret versus transparent security, and user versus designer responsibility.

Contains fulltext : 237091.pdf (Publisher’s version ) (Open Access), In this paper, we provide an overview of how Safe-by-Design is conceived and applied in practice in a large number of engineering disciplines. We discuss the differences, commonalities, and possibilities for mutual learning found in those practices and identify several ways of putting those disciplinary outlooks in perspective. The considered engineering disciplines in the order of historically grown technologies are construction engineering, chemical engineering, aerospace engineering, urban engineering, software engineering, bio-engineering, nano-engineering, and finally cyber space engineering. Each discipline is briefly introduced, the technology at issue is described, the relevant or dominant hazards are examined, the social challenge(s) are observed, and the relevant developments in the field are described. Within each discipline the risk management strategies, the design principles promoting safety or safety awareness, and associated methods or tools are discussed. Possible dilemmas that the designers in the discipline face are highlighted. Each discipline is concluded by discussing the opportunities and bottlenecks in addressing safety. Commonalities and differences between the engineering disciplines are investigated, specifically on the design strategies for which empirical data have been collected. We argue that Safe-by-Design is best considered as a specific elaboration of Responsible Research and Innovation, with an explicit focus on safety in relation to other important values in engineering such as well-being, sustainability, equity, and affordability. Safe-by-Design provides for an intellectual venue where social science and the humanities (SSH) collaborate on technological developments and innovation by helping to proactively incorporate safety considerations into engineering practices, while navigating between the extremes of technological optimism and disproportionate precaution. As such, Safe-by-Design is also a practical tool for policymakers an

Contains fulltext : 240151.pdf (Publisher’s version ) (Open Access), Individuals are regularly made responsible for risks they wish to take: one can consent to processing of personal data, and decide what to buy based on risk information on product labels. However, both large-scale processing of personal data and aggregated product choices may carry collective risks for society. In such situations, governance arrangements implying individual responsibility are at odds with uncertain collective risks from new technologies. We, therefore, investigate the governance challenges of what we call risk personalization: a form of governance for dealing with uncertain collective risks that allocates responsibility for governing those risks to individuals. We situate risk personalization at the intersection of two trends: governance of uncertain risk, and emphasis on individual responsibility. We then analyze three cases selected based on diversity: social media, nanomaterials, and Uber. Cross-case comparison highlights issues of risk personalization pertaining to (i) the nature of the risk, (ii) governance arrangements in place, and (iii) mechanisms for allocating responsibility to individuals. We identify governance challenges in terms of (i) meaningful choice, (ii) effectiveness in mitigating risk, and (iii) collective decision making capacity. We conclude that the risk personalization lens stimulates reflection on the effectiveness and legitimacy of risk governance in light of individual agency.

Item does not contain fulltext

Contains fulltext : 237105.pdf (Publisher’s version ) (Open Access), Water management infrastructures such as floodgates are critical and increasingly operated by Industrial Control Systems (ICS). These systems are becoming more connected to the internet, either directly or through the corporate networks. This makes them vulnerable to cyber-attacks. Abnormal behaviour in floodgates operated by ICS could be caused by both (intentional) attacks and (accidental) technical failures. When operators notice abnormal behaviour, they should be able to distinguish between those two causes to take appropriate measures, because for example replacing a sensor in case of intentional incorrect sensor measurements would be ineffective and would not block corresponding the attack vector. In the previous work, we developed the attack-failure distinguisher framework for constructing Bayesian Network (BN) models to enable operators to distinguish between those two causes, including the knowledge elicitation method to construct the directed acyclic graph and conditional probability tables of BN models. As a full case study of the attack-failure distinguisher framework, this paper presents a BN model constructed to distinguish between attacks and technical failures for the problem of incorrect sensor measurements in floodgates, addressing the problem of floodgate operators. We utilised experts who associate themselves with the safety and/or security community to construct the BN model and validate the qualitative part of constructed BN model. The constructed BN model is usable in water management infrastructures to distinguish between intentional attacks and accidental technical failures in case of incorrect sensor measurements. This could help to decide on appropriate response strategies and avoid further complications in case of incorrect sensor measurements.

Contains fulltext : 245070.pdf (Publisher’s version ) (Open Access), Given the significant privacy and security risks of Internet-of-Things (IoT) devices, it seems desirable to nudge consumers towards buying more secure devices and taking privacy into account in the purchase decision. In order to support this goal, this study examines the effect of security and privacy on IoT device purchase behaviour and assesses whether these effects are sensitive to framing, using a mixed methods approach. The first part of the study focuses on quantifying the effect of security and privacy compared to the effect of other device attributes such as price or functionality, by testing a causal model with choice models that have been developed from stated choice data. The second part aims to reveal the underlying mechanisms that determine the effect of privacy and security on purchase behaviour by means of a qualitative survey. The results suggest that security and privacy can strongly affect purchase behaviour, under the circumstances that privacyand security-related information is available and communicated in an understandable manner, allowing consumers to compare devices. Moreover, the results show that a description of security that focuses on gains is more effective in nudging consumers towards buying secure devices. Future efforts could build upon this study by comparing the effect of security and privacy to more device attributes, such as ease of use or cost reduction. The results can serve as a basis for interventions that nudge consumers towards buying more secure and privacy-friendly devices.

Contains fulltext : 245034.pdf (Publisher’s version ) (Open Access), Cyber operations are relatively a new phenomenon of the last two decades. During that period, they have increased in number, complexity, and agility, while their design and development have been processes well kept under secrecy. As a consequence, limited data(sets) regarding these incidents are available. Although various academic and practitioner public communities addressed some of the key points and dilemmas that surround cyber operations (such as attack, target identification and selection, and collateral damage), still methodologies and models are needed in order to plan, execute, and assess them in a responsibly and legally compliant way. Based on these facts, it is the aim of this article to propose a model that i)) estimates and classifies the effects of cyber operations, and ii) assesses proportionality in order to support targeting decisions in cyber operations. In order to do that, a multi-layered fuzzy model was designed and implemented by analysing real and virtual realistic cyber operations combined with interviews and focus groups with technical - military experts. The proposed model was evaluated on two cyber operations use cases in a focus group with four technical - military experts. Both the design and the results of the evaluation are revealed in this article.

Contains fulltext : 240048.pdf (Publisher’s version ) (Open Access), Risk analysis is an essential methodology for cybersecurity as it allows organizations to deal with cyber threats potentially affecting them, prioritize the defense of their assets, and decide what security controls should be implemented. Many risk analysis methods are present in cybersecurity models, compliance frameworks, and international standards. However, most of them employ risk matrices, which suffer shortcomings that may lead to suboptimal resource allocations. We propose a comprehensive framework for cybersecurity risk analysis, covering the presence of both intentional and nonintentional threats and the use of insurance as part of the security portfolio. A simplified case study illustrates the proposed framework, serving as template for more complex problems.

Item does not contain fulltext, In this contribution, we cover the major philosophical concepts describing the field of security engineering, drawing on literature from philosophy, criminology, and engineering disciplines, with a focus on computer science and cybersecurity. We show that security distinguishes itself as a topic in engineering because it entails a combination of (a) dealing with adversaries and adversarial risk, (b) the intricacies of non-functional requirements and associated verifiability problems, (c) poorly established guidelines for a "science of security" and associated metrics, and (d) a heavily politicized context. While outlining these topics, we will touch upon several key debates in the field, such as secret versus transparent security, and user versus designer responsibility.

Contains fulltext : 237091.pdf (Publisher’s version ) (Open Access), In this paper, we provide an overview of how Safe-by-Design is conceived and applied in practice in a large number of engineering disciplines. We discuss the differences, commonalities, and possibilities for mutual learning found in those practices and identify several ways of putting those disciplinary outlooks in perspective. The considered engineering disciplines in the order of historically grown technologies are construction engineering, chemical engineering, aerospace engineering, urban engineering, software engineering, bio-engineering, nano-engineering, and finally cyber space engineering. Each discipline is briefly introduced, the technology at issue is described, the relevant or dominant hazards are examined, the social challenge(s) are observed, and the relevant developments in the field are described. Within each discipline the risk management strategies, the design principles promoting safety or safety awareness, and associated methods or tools are discussed. Possible dilemmas that the designers in the discipline face are highlighted. Each discipline is concluded by discussing the opportunities and bottlenecks in addressing safety. Commonalities and differences between the engineering disciplines are investigated, specifically on the design strategies for which empirical data have been collected. We argue that Safe-by-Design is best considered as a specific elaboration of Responsible Research and Innovation, with an explicit focus on safety in relation to other important values in engineering such as well-being, sustainability, equity, and affordability. Safe-by-Design provides for an intellectual venue where social science and the humanities (SSH) collaborate on technological developments and innovation by helping to proactively incorporate safety considerations into engineering practices, while navigating between the extremes of technological optimism and disproportionate precaution. As such, Safe-by-Design is also a practical tool for policymakers an

Item does not contain fulltext

Contains fulltext : 245070.pdf (Publisher’s version ) (Open Access), Given the significant privacy and security risks of Internet-of-Things (IoT) devices, it seems desirable to nudge consumers towards buying more secure devices and taking privacy into account in the purchase decision. In order to support this goal, this study examines the effect of security and privacy on IoT device purchase behaviour and assesses whether these effects are sensitive to framing, using a mixed methods approach. The first part of the study focuses on quantifying the effect of security and privacy compared to the effect of other device attributes such as price or functionality, by testing a causal model with choice models that have been developed from stated choice data. The second part aims to reveal the underlying mechanisms that determine the effect of privacy and security on purchase behaviour by means of a qualitative survey. The results suggest that security and privacy can strongly affect purchase behaviour, under the circumstances that privacyand security-related information is available and communicated in an understandable manner, allowing consumers to compare devices. Moreover, the results show that a description of security that focuses on gains is more effective in nudging consumers towards buying secure devices. Future efforts could build upon this study by comparing the effect of security and privacy to more device attributes, such as ease of use or cost reduction. The results can serve as a basis for interventions that nudge consumers towards buying more secure and privacy-friendly devices.

Contains fulltext : 245034.pdf (Publisher’s version ) (Open Access), Cyber operations are relatively a new phenomenon of the last two decades. During that period, they have increased in number, complexity, and agility, while their design and development have been processes well kept under secrecy. As a consequence, limited data(sets) regarding these incidents are available. Although various academic and practitioner public communities addressed some of the key points and dilemmas that surround cyber operations (such as attack, target identification and selection, and collateral damage), still methodologies and models are needed in order to plan, execute, and assess them in a responsibly and legally compliant way. Based on these facts, it is the aim of this article to propose a model that i)) estimates and classifies the effects of cyber operations, and ii) assesses proportionality in order to support targeting decisions in cyber operations. In order to do that, a multi-layered fuzzy model was designed and implemented by analysing real and virtual realistic cyber operations combined with interviews and focus groups with technical - military experts. The proposed model was evaluated on two cyber operations use cases in a focus group with four technical - military experts. Both the design and the results of the evaluation are revealed in this article.

Contains fulltext : 240048.pdf (Publisher’s version ) (Open Access), Risk analysis is an essential methodology for cybersecurity as it allows organizations to deal with cyber threats potentially affecting them, prioritize the defense of their assets, and decide what security controls should be implemented. Many risk analysis methods are present in cybersecurity models, compliance frameworks, and international standards. However, most of them employ risk matrices, which suffer shortcomings that may lead to suboptimal resource allocations. We propose a comprehensive framework for cybersecurity risk analysis, covering the presence of both intentional and nonintentional threats and the use of insurance as part of the security portfolio. A simplified case study illustrates the proposed framework, serving as template for more complex problems.

Contains fulltext : 240146.pdf (Publisher’s version ) (Open Access), Security professionals play a decisive role in security risk decision making, with important implications for security in organisations and society. Because of this subjective input in security understanding possible biases in this process is paramount. In this paper, well known biases as observed and described in prospect theory are studied in individual security risk decision making by security professionals. To this end, we distributed a questionnaire among security professionals including both original dilemmas from prospect theory and dilemmas adapted to the context of incident prevention. It was hypothesised that security professionals dealing with risks and decision making under risk on an almost daily basis would or should be less vulnerable to decision biases involving risks, in particular when framed in terms of incident prevention. The results show that security professionals are vulnerable to decision biases at the same scale as lay people, but some biases are weaker when decision problems are framed in terms of security as opposed to monetary gains and losses. Of the individual characteristics defining experience, only the general education level observably affects vulnerability for biases in security decision making in this study. A higher general education level leads to a significantly higher vulnerability to decision biases. By highlighting the vulnerability of security professionals to decision biases, this study contributes essential awareness and knowledge for improved decision making, for example by different representation of probabilities and uncertainty.

Risk assessments in the (cyber) security domain are often, if not always, based on subjective expert judgement. For the first time, to the best of our knowledge, the individual preferences of professionals from the security domain are studied. In on online survey they are asked to mention, rate and rank their preferences when assessing a security risk. The survey setup allows to differentiate between easily accessible or “on top of mind” attributes and guided or stimulated attributes. The security professionals are also challenged to both non-compensatory and compensatory decision making on the relevance of the attributes. The results of this explorative study indicate a clear difference and shift in the individual perceived relevance of attributes in these different settings. Another remarkable finding of this study is the predominant focus on impact attributes by the respondents and the less significant position of likelihood or probability. The majority of professionals seem to ignore likelihood in their security risk assessment. This might be due to so called probability neglect as introduced by other scholars. the security in organisations and society is depending on the assessment and judgement of these professionals, understanding their preferences and the influence of cognitive biases is paramount. This study contributes to this body of knowledge and might raise attention to this important topic in both the academic and professional security domain., Safety and Security Science

Water management infrastructures such as floodgates are critical and increasingly operated by Industrial Control Systems (ICS). These systems are becoming more connected to the internet, either directly or through the corporate networks. This makes them vulnerable to cyber-attacks. Abnormal behaviour in floodgates operated by ICS could be caused by both (intentional) attacks and (accidental) technical failures. When operators notice abnormal behaviour, they should be able to distinguish between those two causes to take appropriate measures, because for example replacing a sensor in case of intentional incorrect sensor measurements would be ineffective and would not block corresponding the attack vector. In the previous work, we developed the attack-failure distinguisher framework for constructing Bayesian Network (BN) models to enable operators to distinguish between those two causes, including the knowledge elicitation method to construct the directed acyclic graph and conditional probability tables of BN models. As a full case study of the attack-failure distinguisher framework, this paper presents a BN model constructed to distinguish between attacks and technical failures for the problem of incorrect sensor measurements in floodgates, addressing the problem of floodgate operators. We utilised experts who associate themselves with the safety and/or security community to construct the BN model and validate the qualitative part of constructed BN model. The constructed BN model is usable in water management infrastructures to distinguish between intentional attacks and accidental technical failures in case of incorrect sensor measurements. This could help to decide on appropriate response strategies and avoid further complications in case of incorrect sensor measurements., Safety and Security Science, Organisation & Governance

Given the significant privacy and security risks of Internet-of-Things (IoT) devices, it seems desirable to nudge consumers towards buying more secure devices and taking privacy into account in the purchase decision. In order to support this goal, this study examines the effect of security and privacy on IoT device purchase behaviour and assesses whether these effects are sensitive to framing, using a mixed methods approach. The first part of the study focuses on quantifying the effect of security and privacy compared to the effect of other device attributes such as price or functionality, by testing a causal model with choice models that have been developed from stated choice data. The second part aims to reveal the underlying mechanisms that determine the effect of privacy and security on purchase behaviour by means of a qualitative survey. The results suggest that security and privacy can strongly affect purchase behaviour, under the circumstances that privacy- and security-related information is available and communicated in an understandable manner, allowing consumers to compare devices. Moreover, the results show that a description of security that focuses on gains is more effective in nudging consumers towards buying secure devices. Future efforts could build upon this study by comparing the effect of security and privacy to more device attributes, such as ease of use or cost reduction. The results can serve as a basis for interventions that nudge consumers towards buying more secure and privacy-friendly devices., Organisation & Governance, Transport and Logistics

Item does not contain fulltext, This chapter provides an analysis of the cyber insurance landscape and begins with an overview of the cyber insurance ecosystem, presenting the cyber insurance life cycle and the main and secondary actors. It examines organisational decision-making involving cybersecurity and cyber insurance, both for companies as a whole and for Small and Medium Enterprises in particular. The chapter presents the cyber insurance ecosystem and discusses the different types of actors involved in the ecosystem and the relationships between them. Companies must make key investment decisions concerning cybersecurity measures on a regular basis, but there is a lack of research directly investigating how companies make these decisions, as identified by Weishaupl et al. There are many different processes influencing cybersecurity-related decisions inside a company. There are also many different factors, both internal and external, that can influence companies' cybersecurity decision-making and cyber insurance adoption. Many of impediments to cyber insurance adoption are problems that arise when companies try to buy cyber insurance.

Item does not contain fulltext, Domino effects are high-impact phenomena that have caused catastrophic damage to several chemical and process plants around the world through secondary incidents caused by primary ones. With the increasing trend of cyberattacks targeting critical infrastructures, there is a concern that such cyberattacks may trigger domino effects, by manipulating industrial control systems in such a way that the physical consequences are likely to escalate. In this study, we have demonstrated that via network segmentation of industrial control systems, the plant robustness against cyberattack-related domino effects can be improved. To this end, a risk-based decision-making methodology is developed based on Bayesian network and graph theory to investigate and evaluate the robustness of segmentation alternatives. The application of the methodology to an illustrative case study shows the efficacy of the approach as a viable cyber risk mitigation measure in chemical and process plants.

Contains fulltext : 240214.pdf (Publisher’s version ) (Open Access), Cyberattacks against healthcare institutions threaten patient care. The risk of being targeted by a damaging attack is increased when medical devices are used which rely on unmaintained legacy software that cannot be replaced and may have publicly known vulnerabilities. This review aims to provide insight into solutions presented in the literature that mitigate risks caused by legacy software on medical devices. We performed a scoping review by categorising and analysing the contributions of a selection of articles, taken from a literature set discovered through bidirectional citation searching. We found 18 solutions, each fitting at least one of the categories of intrusion detection and prevention, communication tunnelling or hardware protections. Approaches taken include proxying Bluetooth communication through smartphones, behaviour-specification based anomaly detection and authenticating signals based on physical characteristics. These solutions are applicable to various use-cases, ranging from securing pacemakers to medical sensor networks. Most of the solutions are based on intrusion detection and on tunnelling insecure wireless communications. These technologies have distinct application areas, and the decision which one is most appropriate will depend on the type of medical device.

Item does not contain fulltext, Victims are often conceptualized as single, human and static entities with certain risk factors that make them more vulnerable and attractive for offenders. This framework is challenged by emerging forms of high-tech cybercrime, such as ransomware, botnets and virtual theft, in which the offender targets a composite of human, technical and virtual entities. This study critically assesses the current theorization of the cyber victim and offers an alternative approach. Drawing on actor-network theory and three empirical case studies, it analyses the cyber victim as a hybrid actor-network consisting of different entities that, together with the offender, make the victimization possible. The proposed concepts of victim composition, delegation and translation enable a more profound understanding of the hybrid and complex process of becoming a high-tech cyber victim. Keywords: cybercrime, cyber victimization, actor-network theory, botnet, ransomware, virtual theft.

Item does not contain fulltext, The evaluation of the effectiveness of surveillance technology in intelligence agencies and oversight bodies is notably lacking. Assessments of surveillance technology concerning legal compliance, cost, and matters of privacy occupy a solid place, but effectiveness is rarely considered. Bureaucracy may explain this absence. Applying James Q. Wilson's observations on bureaucracy reveals that effectiveness is minimally treated because it is more difficult to evaluate than budget assessments and legal compliance, and because intelligence outcomes are unobservable and difficult to oversee. Effectiveness evaluation is thus fettered by bureaucracy. Considerations of bringing in effectiveness assessment must appreciate the realities of bureaucratic constraints to be successful.

Individuals are regularly made responsible for risks they wish to take: one can consent to processing of personal data, and decide what to buy based on risk information on product labels. However, both large-scale processing of personal data and aggregated product choices may carry collective risks for society. In such situations, governance arrangements implying individual responsibility are at odds with uncertain collective risks from new technologies. We, therefore, investigate the governance challenges of what we call risk personalization: a form of governance for dealing with uncertain collective risks that allocates responsibility for governing those risks to individuals. We situate risk personalization at the intersection of two trends: governance of uncertain risk, and emphasis on individual responsibility. We then analyze three cases selected based on diversity: social media, nanomaterials, and Uber. Cross-case comparison highlights issues of risk personalization pertaining to (i) the nature of the risk, (ii) governance arrangements in place, and (iii) mechanisms for allocating responsibility to individuals. We identify governance challenges in terms of (i) meaningful choice, (ii) effectiveness in mitigating risk, and (iii) collective decision making capacity. We conclude that the risk personalization lens stimulates reflection on the effectiveness and legitimacy of risk governance in light of individual agency., Organisation and Governance

The evaluation of the effectiveness of surveillance technology in intelligence agencies and oversight bodies is notably lacking. Assessments of surveillance technology concerning legal compliance, cost, and matters of privacy occupy a solid place, but effectiveness is rarely considered. Bureaucracy may explain this absence. Applying James Q. Wilson’s observations on bureaucracy reveals that effectiveness is minimally treated because it is more difficult to evaluate than budget assessments and legal compliance, and because intelligence outcomes are unobservable and difficult to oversee. Effectiveness evaluation is thus fettered by bureaucracy. Considerations of bringing in effectiveness assessment must appreciate the realities of bureaucratic constraints to be successful., Safety and Security Science, Organisation and Governance

Cyberattacks against healthcare institutions threaten patient care. The risk of being targeted by a damaging attack is increased when medical devices are used which rely on unmaintained legacy software that cannot be replaced and may have publicly known vulnerabilities. This review aims to provide insight into solutions presented in the literature that mitigate risks caused by legacy software on medical devices. We performed a scoping review by categorising and analysing the contributions of a selection of articles, taken from a literature set discovered through bidirectional citation searching. We found 18 solutions, each fitting at least one of the categories of intrusion detection and prevention, communication tunnelling or hardware protections. Approaches taken include proxying Bluetooth communication through smartphones, behaviour-specification based anomaly detection and authenticating signals based on physical characteristics. These solutions are applicable to various use-cases, ranging from securing pacemakers to medical sensor networks. Most of the solutions are based on intrusion detection and on tunnelling insecure wireless communications. These technologies have distinct application areas, and the decision which one is most appropriate will depend on the type of medical device., Organisation and Governance, Safety and Security Science

Cyber operations are relatively a new phenomenon of the last two decades. During that period, they have increased in number, complexity, and agility, while their design and development have been processes well kept under secrecy. As a consequence, limited data(sets) regarding these incidents are available. Although various academic and practitioner public communities addressed some of the key points and dilemmas that surround cyber operations (such as attack, target identification and selection, and collateral damage), still methodologies and models are needed in order to plan, execute, and assess them in a responsibly and legally compliant way. Based on these facts, it is the aim of this article to propose a model that i)) estimates and classifies the effects of cyber operations, and ii) assesses proportionality in order to support targeting decisions in cyber operations. In order to do that, a multi-layered fuzzy model was designed and implemented by analysing real and virtual realistic cyber operations combined with interviews and focus groups with technical – military experts. The proposed model was evaluated on two cyber operations use cases in a focus group with four technical – military experts. Both the design and the results of the evaluation are revealed in this article., Information and Communication Technology, Organisation and Governance

Item does not contain fulltext, This chapter provides an analysis of the cyber insurance landscape and begins with an overview of the cyber insurance ecosystem, presenting the cyber insurance life cycle and the main and secondary actors. It examines organisational decision-making involving cybersecurity and cyber insurance, both for companies as a whole and for Small and Medium Enterprises in particular. The chapter presents the cyber insurance ecosystem and discusses the different types of actors involved in the ecosystem and the relationships between them. Companies must make key investment decisions concerning cybersecurity measures on a regular basis, but there is a lack of research directly investigating how companies make these decisions, as identified by Weishaupl et al. There are many different processes influencing cybersecurity-related decisions inside a company. There are also many different factors, both internal and external, that can influence companies' cybersecurity decision-making and cyber insurance adoption. Many of impediments to cyber insurance adoption are problems that arise when companies try to buy cyber insurance.