In this paper, we explore low-power custom quantised Multi-Layer Perceptrons (MLPs) as an Intrusion Detection System (IDS) for automotive controller area network (CAN). We utilise the FINN framework from AMD/Xilinx to quantise, train and generate hardware IP of our MLP to detect denial of service (DoS) and fuzzying attacks on CAN network, using ZCU104 (XCZU7EV) FPGA as our target ECU architecture with integrated IDS capabilities. Our approach achieves significant improvements in latency (0.12 ms per-message processing latency) and inference energy consumption (0.25 mJ per inference) while achieving similar classification performance as state-of-the-art approaches in the literature.

翻译：本文探讨了将低功耗定制量化多层感知机（MLP）作为汽车控制器局域网（CAN）入侵检测系统（IDS）的应用。我们利用AMD/Xilinx的FINN框架对MLP进行量化、训练及硬件IP生成，以检测CAN网络上的拒绝服务（DoS）和模糊攻击，并选用集成IDS功能的ZCU104（XCZU7EV）FPGA作为目标ECU架构。该方法在实现与现有文献先进方法相当的分类性能的同时，显著降低了延迟（每条消息处理延迟0.12毫秒）和推理能耗（每次推理0.25毫焦耳）。