A Hybrid Approach Toward Efficient and Accurate Intrusion Detection for In-Vehicle Networks

With recent advancements in the automotive world and the introduction of autonomous vehicles, automotive security has become a real and important issue. Modern vehicles have tens of Electronic Control Units (ECUs) connected to in-vehicle networks. As a de facto standard for in-vehicle network communication, the Controller Area Network (CAN) has become a target of cyber attacks. Anomaly-based Intrusion Detection System (IDS) is considered as an effective approach to secure CAN and detect malicious attacks. Currently, there are two primary approaches used for intrusion detection: rule-based and machine learning-based. Rule-based approach is efficient but limited in the detection accuracy while machine learning-based detection has comparably higher detection accuracy but higher computation cost at the same time. In this paper, we propose a novel hybrid IDS that combines the benefits of both rule-based and machine learning-based approaches. More specifically, we use machine learning methods to achieve a high detection rate while keeping the low computational requirement by offsetting the detection with a rule-based component. Our experiments with CAN traces collected from four different vehicle models demonstrate the effectiveness and efficiency of the proposed hybrid IDS.

Automated summary

With the advancements in the automotive world and the introduction of autonomous vehicles, automotive security has become a crucial concern. This paper proposes a novel hybrid IDS that combines the benefits of both rule-based and machine learning-based approaches, achieving high detection accuracy while maintaining low computation costs.