汽车信息安全：面向总线网络的伪造攻击检测技术

Intelligent connected vehicles(ICVs) are facing a huge challenge of cyber security. For instance, automotive CAN transmits messages with the plain texts, which lacks of the identity recognition of transmitter electronic control units (ECUs) and encryption mechanism. Therefore, how to identify the transmitter of abnormal messages plays a significant role for the automotive cyber-security. Accordingly, an ECU identification recognition technique for masquerade attacks based on the signal features of CAN bus is proposed. Specifically, the core identity parameters based on voltages of CAN are extracted including the rising-falling edge time, plateau duration and mode of high voltages；then, the lightweight Softmax classifier is utilized to train the characteristic parameters offline and constructs the online learning model. The real-world experiments manifest that compared with the traditional method, the proposed method could improve the ECU identification accuracy by about 10%, which is also effective to detect the masquerade attacks. Besides, effects of the operation temperature on the extracted parameters are also evaluated which has indirectly validates the strong robustness of the proposed method. All in all, the proposed method has addressed the defects of CAN network and guaranteed the cyber-security of ICVs.