Parametric observation rule-based intrusion detection strategy for in-vehicle network

Due to the increasingly frequent interaction between in-vehicle network information and the outside world, a large number of network intrusions have already occurred in vehicles leading to loss of control of the vehicle and jeopardizing public safety. To protect the controllability of the vehicle and avoid the intrusion of the in-vehicle network, this paper proposes an intrusion detection method based on parameter observation rules and constructs an intrusion detection model based on parameter observation rules, and the observation rules are designed from message-level rules and signal-level rules. This paper builds a HIL (Hardware-in-the-Loop) testbed, and its experimental results show that the method effectively resists the message replay attack, message injection attack, and signal tampering attack of the in-vehicle CAN network, and the identification rate of attack detection reaches 97.53%.