An Anomaly Detection System Based on Generative Classifiers for Controller Area Network
This addresses security for safety-critical vehicle networks, but appears incremental as it builds on existing IDS approaches with a novel method.
The paper tackles the problem of detecting cyber-attacks on automotive Controller Area Network (CAN) systems by introducing a generative classifier-based Intrusion Detection System (IDS) that uses variational Bayes and a deep latent variable model. The result shows superior performance in detection accuracy and F1-score compared to state-of-the-art IDSs on a public dataset, even with limited training data.
As electronic systems become increasingly complex and prevalent in modern vehicles, securing onboard networks is crucial, particularly as many of these systems are safety-critical. Researchers have demonstrated that modern vehicles are susceptible to various types of attacks, enabling attackers to gain control and compromise safety-critical electronic systems. Consequently, several Intrusion Detection Systems (IDSs) have been proposed in the literature to detect such cyber-attacks on vehicles. This paper introduces a novel generative classifier-based Intrusion Detection System (IDS) designed for anomaly detection in automotive networks, specifically focusing on the Controller Area Network (CAN). Leveraging variational Bayes, our proposed IDS utilizes a deep latent variable model to construct a causal graph for conditional probabilities. An auto-encoder architecture is utilized to build the classifier to estimate conditional probabilities, which contribute to the final prediction probabilities through Bayesian inference. Comparative evaluations against state-of-the-art IDSs on a public Car-hacking dataset highlight our proposed classifier's superior performance in improving detection accuracy and F1-score. The proposed IDS demonstrates its efficacy by outperforming existing models with limited training data, providing enhanced security assurance for automotive systems.