Vehicles today comprise intelligent systems like connected autonomous driving and advanced driving assistance systems (ADAS) to enhance the driving experience, which is enabled through increased connectivity to infrastructure and fusion of information from different sensing modes. However, the rising connectivity coupled with the legacy network architecture within vehicles can be exploited for launching active and passive attacks on critical vehicle systems and directly affecting the safety of passengers. Machine learning-based intrusion detection models have been shown to successfully detect multiple targeted attack vectors in recent literature, whose deployments are enabled through quantised neural networks targeting low-power platforms. Multiple models are often required to simultaneously detect multiple attack vectors, increasing the area, (resource) cost, and energy consumption. In this paper, we present a case for utilising custom-quantised MLP's (CQMLP) as a multi-class classification model, capable of detecting multiple attacks from the benign flow of controller area network (CAN) messages. The specific quantisation and neural architecture are determined through a joint design space exploration, resulting in our choice of the 2-bit precision and the n-layer MLP. Our 2-bit version is trained using Brevitas and optimised as a dataflow hardware model through the FINN toolflow from AMD/Xilinx, targeting an XCZU7EV device. We show that the 2-bit CQMLP model, when integrated as the IDS, can detect malicious attack messages (DoS, fuzzing, and spoofing attack) with a very high accuracy of 99.9%, on par with the state-of-the-art methods in the literature. Furthermore, the dataflow model can perform line rate detection at a latency of 0.11 ms from message reception while consuming 0.23 mJ/inference, making it ideally suited for integration with an ECU in critical CAN networks.

翻译：现代车辆集成了智能系统（如网联自动驾驶与高级驾驶辅助系统），通过增强与基础设施的互联互通及多模态传感信息融合来提升驾驶体验。然而，日益增长的互联性结合车辆内部的传统网络架构，可能被利用来对关键车辆系统发起主动/被动攻击，直接威胁乘客安全。近期研究表明，基于机器学习的入侵检测模型能够有效检测多种定向攻击向量，此类模型的部署依赖于针对低功耗平台设计的量化神经网络。为同时检测多种攻击向量，常需部署多个模型，这增加了面积、资源成本及能耗。本文论证了采用自定义量化MLP（CQMLP）作为多分类模型的可行性，该模型能从控制器局域网（CAN）消息的正常流中检测多种攻击。通过联合设计空间探索确定具体的量化策略与神经网络架构，最终选择2比特量化的n层MLP。该2比特模型基于Brevitas训练，并通过AMD/Xilinx的FINN工具流优化为数据流硬件模型，目标器件为XCZU7EV。实验表明，集成入侵检测系统（IDS）的2比特CQMLP模型能以99.9%的超高准确率检测恶意攻击消息（包括DoS、模糊测试及欺骗攻击），性能与当前文献中的先进方法持平。此外，该数据流模型可在消息接收后0.11毫秒内实现线速检测，单次推理能耗仅为0.23毫焦，使其成为关键CAN网络中ECU集成的理想方案。