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The implementation of electronics in modern vehicles has resulted in an increase in attacks targeting in-vehicle networks; thus, attack detection models have caught the attention of the automotive industry and its researchers. Vehicle network security is an urgent and significant problem because the malfunctioning of vehicles can directly affect human and road safety. The controller area network (CAN), which is used as a de facto standard for in-vehicle networks, does not have sufficient security features, such as message encryption and sender authentication, to protect the network from cyber-attacks. In this paper, we propose an intrusion detection system (IDS) based on a deep convolutional neural network (DCNN) to protect the CAN bus of the vehicle. The DCNN learns the network traffic patterns and detects malicious traffic without hand-designed features. We designed the DCNN model, which was optimized for …