Vehicle CAN bus intrusion detection model based on Bayesian network

Kangyao Dong

doi:10.1117/12.3032074

6 June 2024 Vehicle CAN bus intrusion detection model based on Bayesian network

Kangyao Dong

Proceedings Volume 13175, International Conference on Computer Network Security and Software Engineering (CNSSE 2024); 1317503 (2024) https://doi.org/10.1117/12.3032074
Event: 4th International Conference on Computer Network Security and Software Engineering (CNSSE 2024), 2024, Sanya, China

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kangyao Dong "Vehicle CAN bus intrusion detection model based on Bayesian network", Proc. SPIE 13175, International Conference on Computer Network Security and Software Engineering (CNSSE 2024), 1317503 (6 June 2024); https://doi.org/10.1117/12.3032074

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KEYWORDS

Computer intrusion detection

Education and training

Data modeling

Neural networks

Machine learning

Network security

Performance modeling

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