Robust Intrusion Detection System in CAN Bus through Multi-Scale Feature Fusion

Over the past few decades, as vehicles have become increasingly intelligent, the applications of in-vehicle electronic systems have expanded significantly. However, with the growing complexity of vehicle networks, there is an ever-increasing concern for their network security. In particular, the Controller Area Network (CAN) bus has become a critical medium for communication between various Electronic Control Units (ECUs) within a vehicle. Since the design of the CAN bus lacks sufficient security measures, it is vulnerable to various network intrusions. To address this security challenge, researchers have been searching for ways to enhance the network security of the CAN bus to ensure that vehicle systems are not compromised by unauthorized access or network attacks. This paper introduces a robust intrusion detection system (IDS) for the CAN bus in vehicles, employing a novel Multi-Scale Feature Fusion technique. Leveraging the distinct capabilities of Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Transformer neural network architectures, the proposed methodology adeptly captures and prioritizes both shallow and deep features of CAN bus data. Besides, it enhances detection accuracy and robustness against various cyber-attacks. Evaluation on the Carhacking dataset demonstrates superior performance, achieving a precision, recall, and F1-score of 100%. Verified by an ablation study, this approach promises a substantial advancement in safeguarding in-vehicle networks.