In-vehicle network intrusion detection using deep convolutional neural network

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 the data traffic of the CAN bus, to achieve high detection performance while reducing the unnecessary complexity in the architecture of the Inception-ResNet model. We performed an experimental study using the datasets we built with a real vehicle to evaluate our detection system. The experimental results demonstrate that the proposed IDS has significantly low false negative rates and error rates when compared to the conventional machine-learning algorithms.