Vehicle CAN bus intrusion detection model based on Bayesian network

Abstract

With the rapid development of in-vehicle network technology, vehicle safety and protection are facing more and more challenges. The vehicle CAN bus is the main network for vehicle internal communication. However, due to its lack of necessary security mechanisms, the vehicle CAN bus is vulnerable to intrusion attacks. Therefore, developing an effective intrusion detection model is crucial to secure vehicle networks. This study proposes a vehicle CAN bus intrusion detection model based on Bayesian network. This model utilizes the probabilistic reasoning of Bayesian networks and the update characteristics of conditional probability, combined with the characteristic attributes of the vehicle CAN bus, to achieve accurate detection of potential intrusion behaviors. By learning historical data, the conditional probability of the Bayesian network can be updated to achieve real-time detection and prediction of intrusion behavior. In order to verify the effectiveness of the model, we used a real vehicle CAN bus data set for experiments. Experimental results show that the intrusion detection model based on Bayesian network has achieved good results in identifying and predicting intrusion behavior of the vehicle CAN bus. Compared with traditional intrusion detection methods, this model can provide higher accuracy and lower false alarm rate, effectively protecting the security of in-vehicle networks.