Connected and autonomous vehicles (CAVs) are vulnerable to security gaps that can result in serious consequences, including cyber-physical and privacy risks. For example, an attacker can reconstruct a vehicle’s location trajectory by knowing the speed and steering wheel position of the vehicle. Such inferences not only lead to safety issues but also significantly threaten privacy. This paper assesses the privacy impacts of cyber threats on vehicular networks. We augment the Privacy Risk Assessment Methodology (PRAM), proposed by the National Institute of Standards and Technology, with cyber threats, with cyber threats, which are, in practice, mapped to PRAM impact metrics. We demonstrate the practical application of the enhanced PRAM methodology through a use case that highlights attacks leading to privacy risks in CAVs. The consideration of cyber attacks for privacy risk assessment addresses a major gap in current practices, which is to integrate privacy risk into cyber risk management.

In the wake of a cybersecurity incident, it is crucial to promptly discover how the threat actors breached security in order to assess the impact of the incident and to develop and deploy countermeasures that can protect against further attacks. To this end, defenders can launch a cyber-forensic investigation, which discovers the techniques that the threat actors used in the incident. A fundamental challenge in such an investigation is prioritizing the investigation of particular techniques since the investigation of each technique requires time and effort, but forensic analysts cannot know which ones were actually used before investigating them. To ensure prompt discovery, it is imperative to provide decision support that can help forensic analysts with this prioritization. A recent study demonstrated that data-driven decision support, based on a dataset of prior incidents, can provide state-of-the-art prioritization. However, this data-driven approach, called DISCLOSE, is based on a heuristic that utilizes only a subset of the available information and does not approximate optimal decisions. To improve upon this heuristic, we introduce a principled approach for data-driven decision support for cyber-forensic investigations. We formulate the decision-support problem using a Markov decision process, whose states represent the states of a forensic investigation. To solve the decision problem, we propose a Monte Carlo tree search based method, which relies on a k-NN regression over prior incidents to estimate state-transition probabilities. We evaluate our proposed approach on multiple versions of the MITRE ATT&CK dataset, which is a knowledge base of adversarial techniques and tactics based on real-world cyber incidents, and demonstrate that our approach outperforms DISCLOSE in terms of techniques discovered per effort spent.

Assessing the risk posed by Advanced Cyber Threats (APTs) is challenging without understanding the methods and tactics adversaries use to attack an organisation. The MITRE ATT&CK provides information on the motivation, capabilities, interests and tactics, techniques and procedures (TTPs) used by threat actors. In this paper, we leverage these characteristics of threat actors to support informed cyber risk characterisation and assessment. In particular, we utilise the MITRE repository of known adversarial TTPs along with attack graphs to determine the attack probability as well as the likelihood of success of an attack. We further identify attack paths with the highest likelihood of success considering the techniques and procedures of a threat actor. The assessment is supported by a case study of a health care organisation to identify the level of risk against two adversary groups– Lazarus and menuPass.

Investments on cybersecurity are essential for organizations to protect operational activities, develop trust relationships with clients, and maintain financial stability. A cybersecurity breach can lead to financial losses as well as to damage the reputation of an organization. Protecting an organization from cyber attacks demands considerable investments; however, it is known that organisations unequally divide their budget between cybersecurity and other technological needs. Organizations must consider cybersecurity measures, including but not limited to security controls, in their cybersecurity investment plans. Nevertheless, designing an effective cybersecurity investment plan to optimally distribute the cybersecurity budget is a primary concern. This paper presents GTM, a methodology depicted as a tool dedicated to providing optimal cybersecurity defense strategies and investment plans. GTM utilizes attack graphs to predict all possible cyber attacks, game theory to simulate the cyber attacks and 0-1 Knapsack to optimally allocate the budget. The output of GTM is an optimal cybersecurity strategy that includes security controls to protect the organisation against potential cyber attacks and enhance its cyber defenses. Furthermore, GTM’s effectiveness is evaluated against three use cases and compared against different attacker types under various scenarios.

We introduce a game-theoretic model to investigate the strategic interaction between a cyber insurance policyholder whose premium depends on her self-reported security level and an insurer with the power to audit the security level upon receiving an indemnity claim. Audits can reveal fraudulent (or simply careless) policyholders not following reported security procedures, in which case the insurer can refuse to indemnify the policyholder. However, the insurer has to bear an audit cost even when the policyholders have followed the prescribed security procedures. As audits can be expensive, a key problem insurers face is to devise an auditing strategy to deter policyholders from misrepresenting their security levels to gain a premium discount. This decision-making problem was motivated by conducting interviews with underwriters and reviewing regulatory filings in the U.S.; we discovered that premiums are determined by security posture, yet this is often self-reported and insurers are concerned by whether security procedures are practised as reported by the policyholders. To address this problem, we model this interaction as a Bayesian game of incomplete information and devise optimal auditing strategies for the insurers considering the possibility that the policyholder may misrepresent her security level. To the best of our knowledge, this work is the first theoretical consideration of post-incident claims management in cyber security. Our model captures the trade-off between the incentive to exaggerate security posture during the application process and the possibility of punishment for non-compliance with reported security policies. Simulations demonstrate that common sense techniques are not as efficient at providing effective cyber insurance audit decisions as the ones computed using game theory.