The field of intelligent connected in modern vehicles continues to expand, and the functions of vehicles become more and more complex with the development of the times. This has also led to an increasing number of vehicle vulnerabilities and many safety issues. Therefore, it is particularly important to identify high-risk vehicle intelligent connected systems, because it can inform security personnel which systems are most vulnerable to attacks, allowing them to conduct more thorough inspections and tests. In this paper, we develop a new model for vehicle risk assessment by combining I-FAHP with FCA clustering: VSRQ model. We extract important indicators related to vehicle safety, use fuzzy cluster analys (FCA) combined with fuzzy analytic hierarchy process (FAHP) to mine the vulnerable components of the vehicle intelligent connected system, and conduct priority testing on vulnerable components to reduce risks and ensure vehicle safety. We evaluate the model on OpenPilot and experimentally demonstrate the effectiveness of the VSRQ model in identifying the safety of vehicle intelligent connected systems. The experiment fully complies with ISO 26262 and ISO/SAE 21434 standards, and our model has a higher accuracy rate than other models. These results provide a promising new research direction for predicting the security risks of vehicle intelligent connected systems and provide typical application tasks for VSRQ. The experimental results show that the accuracy rate is 94.36%, and the recall rate is 73.43%, which is at least 14.63% higher than all other known indicators.

翻译：现代车辆智能网联领域持续拓展，随着时代发展，车辆功能日益复杂，这也导致车辆漏洞数量不断增加，带来诸多安全问题。因此，识别高风险车辆智能网联系统尤为重要，因为这能使安全人员了解哪些系统最易受到攻击，从而开展更全面的检查与测试。本文结合I-FAHP与FCA聚类，提出了一种新的车辆风险评估模型——VSRQ模型。我们提取与车辆安全相关的重要指标，利用模糊聚类分析（FCA）结合模糊层次分析法（FAHP）挖掘车辆智能网联系统的脆弱组件，并对脆弱组件进行优先级测试，以降低风险并保障车辆安全。我们在OpenPilot上评估了该模型，实验证明了VSRQ模型在识别车辆智能网联系统安全性方面的有效性。实验完全符合ISO 26262和ISO/SAE 21434标准，且该模型的准确率高于其他模型。这些结果为预测车辆智能网联系统安全风险提供了有前景的新研究方向，并为VSRQ提供了典型应用任务。实验结果表明，该模型的准确率为94.36%，召回率为73.43%，比所有其他已知指标至少高出14.63%。