A deep architecture for in-vehicle intrusion detection using controller area network-graph relied feature images

Active integration of digital innovations into automotive systems anticipates flawless security and safety in automation, and it is a major concern in intelligent transport systems. Securing the in-vehicle Controller Area Network (CAN) bus communication remains a major challenge due to inherent protocol vulnerabilities. In this study, we propose a novel deep learning-based intrusion detection approach that captures structural and contextual patterns within CAN traffic. Specifically, we introduce the concept of CANGraph-feature images, where CAN message interactions combining payload and arbitration ID information are represented as graph structures and subsequently transformed into images. This transformation enables the use of Convolutional Neural Networks (CNNs), leveraging their powerful spatial feature extraction capabilities to detect subtle and complex anomalies. Our optimized CNN architecture automatically learns discriminative features from the CANGraph images, effectively identifying abnormal behaviors. Extensive experiments on real-world vehicular datasets demonstrate that the proposed method robustly detects a wide range of attack types, including replay, fuzzing, DoS, and spoofing. The presented deep architecture shows promising potential to enhance the security of in-vehicle networks by achieving strong performance while maintaining low computational overhead and latency.