用二值化神经网络实现车载网络低延迟入侵检测
Towards Ultra-Low Latency: Binarized Neural Network Architectures for In-Vehicle Network Intrusion Detection
- 采用二值化神经网络，仅用报文载荷、消息ID和频率做检测
- 在CAN总线场景下实现异常与多类流量分类，适合实时部署
- 专为微控制器和网关ECU设计，满足车载安全实时性要求
控制器局域网（CAN）协议是车辆内部通信的核心，支持电子控制单元（ECUs）间高速数据交换。然而，其固有设计缺乏强有力的安全部件，使车辆易受网络攻击。尽管已有研究探索机器学习与深度学习提升网络安全，但实际应用仍存疑。本文提出一种基于二值化神经网络（BNNs）的轻量级入侵检测方法，利用报文载荷、消息ID及报文频率进行有效检测。同时，开发混合二值编码技术，整合非二值特征如消息ID与频率。所提方法——专为车载入侵检测优化的BNN框架结合混合二值量化技术处理非载荷属性——在异常检测与多类网络流量分类中均表现良好。系统适用于微控制器与网关ECU部署，符合CAN总线安全应用的实时性需求。
原文摘要 · Abstract (English)
The Control Area Network (CAN) protocol is essential for in-vehicle communication, facilitating high-speed data exchange among Electronic Control Units (ECUs). However, its inherent design lacks robust security features, rendering vehicles susceptible to cyberattacks. While recent research has investigated machine learning and deep learning techniques to enhance network security, their practical applicability remains uncertain. This paper presents a lightweight intrusion detection technique based on Binarized Neural Networks (BNNs), which utilizes payload data, message IDs, and CAN message frequencies for effective intrusion detection. Additionally, we develop hybrid binary encoding techniques to integrate non-binary features, such as message IDs and frequencies. The proposed method, namely the BNN framework specifically optimized for in-vehicle intrusion detection combined with hybrid binary quantization techniques for non-payload attributes, demonstrates efficacy in both anomaly detection and multi-class network traffic classification. The system is well-suited for deployment on micro-controllers and Gateway ECUs, aligning with the real-time requirements of CAN bus safety applications.
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