CAN Bus Intrusion Detection Based on Deep Learning With Data Augmentation for Connected Autonomous Vehicles

As the de-facto standard for in-vehicle networks, the Controller Area Network (CAN) is exposed to different types of cyber-attacks due to the lack of security mechanisms. Intrusion Detection Systems (IDS) can be deployed to identify the attacks by monitoring host and network activities. However, there is little abnormal historical data that can be used to train deep learning models, resulting in data imbalance and biased trained model. Hence, we propose a prediction-based IDS framework for detecting the attacks on a CAN bus, which consists of two deeplearning models of the data augmentation module and the prediction module. Firstly, the Generative Adversarial Networks (GAN) was utilized as the data augmentation module to automatically generate high-quality attack data and balance the training set. Two networks were introduced as the prediction module, and the first one is a convolutional neural networks (CNN) that predicts correlated data of all CAN IDs, and the second one is an LSTM that predicts messages individually using times series data for each CAN ID. Furthermore, an intrusion detection equipment for the CAN bus was designed and the real vehicle test was conducted. The experimental results show that the proposed method can detect CAN attacks, with an average F1-score of 99.74% and an accuracy of 99.78%. Compared with the reference work, the F1-score of attack detection is improved by 15.25%, and also the detection time is reduced by 29.11%.