Securing the CAN bus using deep learning for intrusion detection in vehicles

The Controller Area Network (CAN) bus protocol is the essential communication backbone in vehicles within the Intelligent Transportation System (ITS), enabling interaction between electronic control units (ECUs). However, CAN messages lack authentication and security, making the system vulnerable to attacks such as DoS, fuzzing, impersonation, and spoofing. This paper evaluates deep learning methods to detect intrusions in the CAN bus network. Using the Car Hacking, Survival Analysis, and OTIDS datasets, we train and test models to identify automotive cyber threats. We explore recurrent neural network (RNN) variants, including LSTM, GRU, and VGG-16, to analyze temporal and spatial features in the data. LSTMs and GRUs handle long-term dependencies in sequential data, making them suitable for analyzing CAN messages. Bi-LSTMs enhance this by processing sequences in both directions, learning from past and future contexts to improve anomaly detection. Our results show that LSTM achieves 99.89% accuracy in binary classification, while VGG-16 reaches 100% accuracy in multiclass classification. These findings demonstrate the potential of deep learning techniques in improving the security and resilience of ITS by effectively detecting and mitigating CAN bus network attacks.