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The Controller Area Network (CAN) bus serves as a standard protocol forfacilitating communication among various electronic control units (ECUs) withincontemporary vehicles. However, it has been demonstrated that the CAN bus issusceptible to remote attacks, which pose risks to the vehicle's safety andfunctionality. To tackle this concern, researchers have introduced intrusiondetection systems (IDSs) to identify and thwart such attacks. In this paper, wepresent an innovative approach to intruder detection within the CAN bus,leveraging Graph Convolutional Network (GCN) techniques as introduced by Zhang,Tong, Xu, and Maciejewski in 2019. By harnessing the capabilities of deeplearning, we aim to enhance attack detection accuracy while minimizing therequirement for manual feature engineering. Our experimental findingssubstantiate that the proposed GCN-based method surpasses existing IDSs interms of accuracy, precision, and recall. Additionally, our approachdemonstrates efficacy in detecting mixed attacks, which are more challenging toidentify than single attacks. Furthermore, it reduces the necessity forextensive feature engineering and is particularly well-suited for real-timedetection systems. To the best of our knowledge, this represents the pioneeringapplication of GCN to CAN data for intrusion detection. Our proposed approachholds significant potential in fortifying the security and safety of modernvehicles, safeguarding against attacks and preventing them from underminingvehicle functionality.

GCNIDS: Graph Convolutional Network-Based Intrusion Detection System for CAN Bus