## 6 Conclusions  
In this paper we proposed ICVTest, a novel black-box penetration testing framework, which is the first of its kind to focus on comprehensive vehicle-oriented security evaluation and draws upon extensive experience in automotive penetration testing. The penetration testing process in ICVTest is structured into six steps of specification, enabling even novice testers to be efficiently guided through the initiation phase. Additionally, leveraging extensive testing experience, ICVTest abstracts automotive cybersecurity test cases into 10 sets. Testers only need to conduct threat analysis on the target object and select appropriate test cases from predefined test case sets based on the results of threat analysis to evaluate vehicle security. Furthermore, we have implemented a comprehensive cybersecurity testing platform that seamlessly integrates testing tools and guide manuals to facilitate testers in conducting penetration testing efficiently. Ten test case sets in ICVTest are highly decoupled from the specific in-vehicle system. Despite variations in electronic and electrical architectures among vehicles, their software and hardware security assessments can be aligned with the 10 test case sets of ICVTest. By leveraging ICVTest, testers only need to reuse appropriate test cases based on the threat analysis results of the object under test. While ICVTest itself does not delve into specific testing methods, it offers a set of systematic guidance methods for evaluating vehicle security based on practical penetration testing experience. Hierarchical test case sets of ICVTest are highly versatile, providing guidance not only to testers with varying levels of experience but also for the security evaluation of diverse vehicles and on-board systems. We applied the ICVTest to evaluate a physical vehicle manufactured in 2021, which facilitated efficient identification of multiple cybersecurity vulnerabilities encompassing the in-vehicle CAN bus, sensors, infotainment systems, and remote vehicle controls. Our experimental results demonstrate that employing methodology provided by ICVTest is helpful to conduct the cybersecurity assessment of ICV, while it reduces the complexity associated with penetration testing and enhancing overall efficiency. In future, the ICVTest and the cybersecurity test platform still require further enhancements. (1) Although the framework divides vehicle penetration testing into 10 fields, continuous investigation and quality improvement is needed to determine the specific content that should be tested at each field in order to effectively address the emerging cybersecurity risks and to identify the unknown vulnerabilities. (2) The majority of tests within ICVTest are currently triggered by the real-time participation from testers, resulting in a low degree of automation. To enhance the platform's automated testing capabilities, it is necessary to develop the automated testing scripts for different test cases in the future. (3) Presently, we only mentioned AI security in sensor security testing, such as adversarial example-based traffic sign recognition robustness testing in camera security. However, as high-level autonomous driving becomes more prevalent in the future, there may be potential for separating AI security testing from sensor security testing. Conceptualization, J.W. and Z.L. ; Methodology, H.Z. and J.W. ; Software, H.Z. and Y.W. ; Validation, M.L. ; Formal analysis, J.S. ; Investigation, Y.W., M.L. and J.S. ; Writing--original draft, H.Z. ; Writing--review & editing, J.W. and Z.L. All authors have read and agreed to the published version of the manuscript. This work was supported by a grant of key technologies RD general program of Shenzhen, No. JSGG20201102170601003. Not applicable. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the Testing Confidentiality Agreement. This research is supported by key technologies RD general program of Shenzhen, No.