Modern vehicles are more complex and interconnected than ever before, which also means that attack surfaces for vehicles have increased significantly. Malicious cyberattacks will not only exploit personal privacy and property, but also affect the functional safety of electrical/electronic (E/E) safety-critical systems by controlling the driving functionality, which is life-threatening. Therefore, it is necessary to conduct cybersecurity testing on vehicles to reveal and address relevant security threats and vulnerabilities. Cybersecurity standards and regulations issued in recent years, such as ISO/SAE 21434 and UNECE WP.29 regulations (R155 and R156), also emphasize the indispensability of cybersecurity verification and validation in the development lifecycle but lack specific technical details. Thus, this paper conducts a systematic and comprehensive review of the research and practice in the field of automotive cybersecurity testing, which can provide reference and advice for automotive security researchers and testers. We classify and discuss the security testing methods and testbeds in automotive engineering. Furthermore, we identify gaps and limitations in existing research and point out future challenges.

Modern vehicles are no longer merely mechanical systems but are monitored and controlled by various electronic systems. Safety-critical systems of connected vehicles become vulnerable to cyberattacks because of increasing interconnection. At present, the security risk analysis of connected vehicles is mainly based on qualitative methods, while these methods are usually subjective and lack consideration for functional safety. In order to solve this problem, we propose in this paper a security risk analysis framework for connected vehicles based on formal methods. Firstly, we introduce the electronic and electrical architecture of the connected vehicle and analyze the attack surfaces of the in-vehicle safety-critical systems from three levels of sensors, in-vehicle networks, and controllers. Secondly, we propose a method to model the target of evaluation (i.e., in-vehicle safety-critical system) as a Markov decision process and use probabilistic computation tree logic to formally describe its security properties. Then, a probabilistic model checker PRISM is used to analyze the security risk of target systems quantitatively according to security properties. Finally, we apply the proposed approach to analyze and compare the security risks of the collision warning system under a distributed and centralized electrical and electronic architecture. In addition, from a practical point of view, we propose a Markov model generation method based on a SysML activity diagram, which can simplify our modeling process. The evaluation results show that we can have a quantitative understanding of the security risks at the system level in the early stage of system design.