Machine Learning-Based Intrusion Detection for Securing In-Vehicle CAN Bus Communication

Modern cars are now much more connected than they were a few years ago, thanks to the rapid development of embedded technology. This had made them more vulnerable to attacks. The controller area network (CAN) bus, a widely used communication standard in automotive systems, plays a crucial role in the interconnection of onboard electronic components. However, the lack of inherent security mechanisms in the CAN bus makes it a prime target for malicious attacks, compromising the system such as the denial of service (DoS), fuzzy, spoofing, and replay attacks. In this paper, we propose a machine learning-based intrusion detection system for identifying attacks on in-vehicle CAN bus communication. We train and test long short-term memory (LSTM) and convolutional neural network (CNN) models on two public datasets (Car-Hacking and CAN-Intrusion) and our self-created dataset, named Bus-CAN-Attack, which was generated using the ICSim simulation tool. Using the selected hyper parameters, we achieve impressive detection accuracy with the fine-tuned models varying from 89% to 99% for the different datasets.