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.

本文提出了一种基于 I-FAHP 和 FCA clustering 相结合的车辆风险评估模型（VSRQ model），并提取与车辆安全相关的重要指标，利用模糊聚类分析（FCA）和模糊层次分析（FAHP）来挖掘车辆智能连接系统的易受攻击的组件，并对易受攻击的组件进行优先测试以降低风险并确保车辆安全。该模型在 OpenPilot 上得到了评估，实验证明了 VSRQ 模型在识别车辆智能连接系统安全方面的有效性。