综述基于学习的车载网络入侵检测技术，助力智能汽车安全防护。
A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network
- 分类梳理机器学习、深度学习与联邦学习在车载入侵检测中的应用
- 指出现有方法对已知、未知及混合攻击的检测能力差异
- 适合关注车联网安全与防御机制的研究者阅读
联网与自动驾驶汽车（CAVs）提升了出行便利性，但面临通过不安全控制器局域网（CAN）总线发起的网络安全威胁。网络攻击可能导致关键系统失控，后果严重，亟需强有力的防护方案。车载入侵检测系统（IDS）通过实时识别恶意行为提供有效解决方案。本综述全面回顾了基于学习的车载IDS最新研究，涵盖机器学习（ML）、深度学习（DL）与联邦学习（FL）方法。根据已有研究，我们系统分析了各类方法对已知攻击、未知攻击及混合攻击的检测能力，并揭示其局限性。同时，综述评估指标使用情况，强调需综合多维度标准以满足安全关键系统需求。此外，还剖析了基于联邦学习的IDS现状及其挑战。本工作有助于识别有效安全策略，弥补当前不足，推动未来研究向更鲁棒、自适应的防护机制发展，保障CAVs的安全与可靠性。
原文摘要 · Abstract (English)
Connected and Autonomous Vehicles (CAVs) enhance mobility but face cybersecurity threats, particularly through the insecure Controller Area Network (CAN) bus. Cyberattacks can have devastating consequences in connected vehicles, including the loss of control over critical systems, necessitating robust security solutions. In-vehicle Intrusion Detection Systems (IDSs) offer a promising approach by detecting malicious activities in real time. This survey provides a comprehensive review of state-of-the-art research on learning-based in-vehicle IDSs, focusing on Machine Learning (ML), Deep Learning (DL), and Federated Learning (FL) approaches. Based on the reviewed studies, we critically examine existing IDS approaches, categorising them by the types of attacks they detect - known, unknown, and combined known-unknown attacks - while identifying their limitations. We also review the evaluation metrics used in research, emphasising the need to consider multiple criteria to meet the requirements of safety-critical systems. Additionally, we analyse FL-based IDSs and highlight their limitations. By doing so, this survey helps identify effective security measures, address existing limitations, and guide future research toward more resilient and adaptive protection mechanisms, ensuring the safety and reliability of CAVs.
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