MULSAM: Multidimensional Attention With Hardware Acceleration for Efficient Intrusion Detection on Vehicular CAN Bus

Abstract

Controller area network (CAN) protocol is 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. In this article, a novel vehicle intrusion detection system (IDS) is developed. The challenge is that existing techniques of IDSs rarely consider attacks with small-batch, which are characterized by their small attack scale and concealed attack patterns, posing a significant threat to driving safety. To solve this problem, we developed an algorithm model that merges multidimensional long short-term memory (MD-LSTM) and self-attention mechanism (SAM), shortly named MULSAM. The MULSAM model was compared with other baseline models, including stacked long short-term memory (LSTM), MD-LSTM, etc. Experiments show that our approach has the best-detection accuracy (98.98%) and training stability. Further, to speed up the inference of MULSAM on edge, the hardware accelerator is implemented on FPGA devices using technologies, such as parallelization, modular, pipeline, and fixed-point quantization. Experiments show that our FPGA-based acceleration scheme has a better-energy efficiency than the CPU platform. Even with a certain degree of quantification, the acceleration model for MULSAM still displays a high-detection accuracy of 98.81% and a low latency of 1.88 ms.