Biblio

High-end vehicles incorporate about one hundred computers; physical and virtualized ones; self-driving vehicles even more. This allows a plethora of attack combinations. This paper demonstrates how to assess exploitability risks of vehicular on-board networks via automatically generated and analyzed attack graphs. Our stochastic model and algorithm combine all possible attack vectors and consider attacker resources more efficiently than Bayesian networks. We designed and implemented an algorithm that assesses a compilation of real vehicle development documents within only two CPU minutes, using an average of about 100 MB RAM. Our proof of concept "Security Analyzer for Exploitability Risks" (SAlfER) is 200 to 5 000 times faster and 40 to 200 times more memory-efficient than an implementation with UnBBayes1. Our approach aids vehicle development by automatically re-checking the architecture for attack combinations that may have been enabled by mistake and which are not trivial to spot by the human developer. Our approach is intended for and relevant for industrial application. Our research is part of a collaboration with a globally operating automotive manufacturer and is aimed at supporting the security of autonomous, connected, electrified, and shared vehicles.

To provide a comprehensive security analysis of modern networked systems, we need to take into account the combined effects of existing vulnerabilities and zero-day vulnerabilities. In addition to them, it is important to incorporate new vulnerabilities emerging from threats such as BYOD, USB file sharing. Consequently, there may be new dependencies between system components that could also create new attack paths, but previous work did not take into account those new attack paths in their security analysis (i.e., not all attack paths are taken into account). Thus, countermeasures may not be effective, especially against attacks exploiting the new attack paths. In this paper, we propose a Unified Vulnerability Risk Analysis Module (UV-RAM) to address the aforementioned problems by taking into account the combined effects of those vulnerabilities and capturing the new attack paths. The three main functionalities of UV-RAM are: (i) to discover new dependencies and new attack paths, (ii) to incorporate new vulnerabilities introduced and zero-day vulnerabilities into security analysis, and (iii) to formulate mitigation strategies for hardening the networked system. Our experimental results demonstrate and validate the effectiveness of UV-RAM.

By enabling a direct comparison of different security solutions with respect to their relative effectiveness, a network security metric may provide quantifiable evidences to assist security practitioners in securing computer networks. However, research on security metrics has been hindered by difficulties in handling zero-day attacks exploiting unknown vulnerabilities. In fact, the security risk of unknown vulnerabilities has been considered as something unmeasurable due to the less predictable nature of software flaws. This causes a major difficulty to security metrics, because a more secure configuration would be of little value if it were equally susceptible to zero-day attacks. In this paper, we propose a novel security metric, k-zero day safety, to address this issue. Instead of attempting to rank unknown vulnerabilities, our metric counts how many such vulnerabilities would be required for compromising network assets; a larger count implies more security because the likelihood of having more unknown vulnerabilities available, applicable, and exploitable all at the same time will be significantly lower. We formally define the metric, analyze the complexity of computing the metric, devise heuristic algorithms for intractable cases, and finally demonstrate through case studies that applying the metric to existing network security practices may generate actionable knowledge.