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Cyber-physical systems are becoming more intelligent with the adoption of heterogeneous sensor networks and machine learning capabilities that deal with an increasing amount of input data. While this complexity aims to solve problems in various domains, it adds new challenges for the system assurance. One issue is the rise in the number of abnormal behaviors that affect system performance due to possible sensor faults and attacks. The combination of safety risks, which are usually caused by random sensor faults and security risks that can happen during any random system state, makes the full coverage testing of the cyber-physical system challenging. Existing techniques are inadequate to deal with complex safety and security co-risks against cyber-physical systems. In this paper, we propose AST-SafeSec, an analysis methodology for both safety and security aspects that utilizes reinforcement learning to identify the most likely adversarial paths at various normal or failure states of a cyber-physical system that can influence system behavior through its sensor data. The methodology is evaluated using an autonomous vehicle scenario by incorporating a security attack into the stochastic sensor elements of a vehicle. Evaluation results show that the methodology analyzes the interaction of malicious attacks with random faults and identifies the incident caused by the interactions and the most likely path that leads to the incident. [ABSTRACT FROM AUTHOR]

Latest technological trends lead toward systems connected to public networks even in critical domains. Bringing together safety and security work is becoming imperative, as a connected safety-critical system is not safe if it is not secure. The main objective of this study is to investigate the current status of safety and security co-analysis in system engineering by conducting a systematic literature review. The steps of the review are the following: the research questions identification; agreement upon a search string; applying the search string to chosen databases; a selection criterion formulation for the relevant publications filtering; selected papers categorization and analysis. We focused on the early system development stages and identified 33 relevant publications categorized as follows: combined safety and security approaches that consider the mutual influence of safety and security; safety-informed security approaches that consider influence of safety on security; and security-informed safety approaches that consider influence of security on safety. The results showed that a number of identified approaches are driven by needs in fast developing application areas, e.g., automotive, while works focusing on combined analysis are mostly application area independent. Overall, the study shows that safety and security co-analysis is still a developing domain. [ABSTRACT FROM AUTHOR]