Models on the Move: Towards Feasible Embedded AI for Intrusion Detection on Vehicular CAN Bus

Controller Area Network (CAN) protocol is widely used in vehicles as an efficient standard enabling communication among Electronic Control Units (ECUs). However, the CAN bus is vulnerable to malicious attacks because of a lack of defense features. To achieve efficient and effective intrusion detection system (IDS) design for hardware and embedded system security in vehicles, we have specifically tackled the challenge that existing IDS techniques rarely consider attacks with smallbatch. We propose a model with hardware implementation to function in the vehicular CAN bus, namely MULSAM which employing multi-dimensional long short-term memory with the self-attention mechanism. The self-attention mechanism can enhance the characteristics of CAN bus-oriented attack behavior and the multi-dimensional long short-term memory can effectively extract the in-depth features of time series data. The MULSAM model has been compared with other baselines on five attacks generated by extracting benign CAN data from the actual vehicle. Our experimental results demonstrate that MULSAM has the best training stability and detection accuracy (98.98%) to identify small-batch injection attacks. Furthermore, to speed up the inference of MULSAM as an embedded unit in vehicles, hardware accelerator has been implemented on FPGA to achieve a better energy efficiency than other embedded platform. Even with a certain degree of quantification, the acceleration model for MULSAM still presents a high detection accuracy of 98.81% and a low latency of 1.88 ms, leading to a new cyber-physical system security solution towards feasible embedded AI for intrusion detection on vehicular CAN bus.