AI 中文总结
针对现代车载网络攻击面扩大问题，本文通过知识梳理统一车载攻击面分类法、评估FL拓扑并映射对抗威胁，审核60多篇文献识别常见陷阱，进而定义前瞻性研究议程及最低基准要求，推动车载FL-IDS走向实际安全部署。
AI 中文摘要
现代车载网络在内部电子控制单元（ECU）和外部车联网（V2X）通信方面面临不断扩大的攻击面。联邦学习（FL）已成为一种分散式范式，用于部署入侵检测系统（IDS）而不损害数据隐私。然而，车载FL-IDS文献存在方法零散和实验设置不现实的问题。本文提出了一种知识梳理（SoK），统一了车载攻击面的分类法，评估了FL拓扑结构，并映射了诸如中毒和推理攻击等对抗性威胁。通过审核60多篇出版物，我们识别出了常见的陷阱：人工独立同分布（IID）数据分割、依赖简单基准、对抗性评估薄弱以及忽略实时控制器局域网（CAN）约束。最后，我们定义了一个前瞻性的研究议程，并概述了将车载FL-IDS从乐观模拟过渡到安全的实际部署所需的最低基准要求。
英文摘要
Modern vehicular networks face an expanding attack surface across internal Electronic Control Units (ECUs) and external Vehicle-to-Everything (V2X) communication. Federated Learning (FL) has emerged as a decentralized paradigm to deploy Intrusion Detection Systems (IDS) without compromising data privacy. However, the vehicular FL-IDS literature suffers from fragmented methodologies and unrealistic experimental setups. This paper presents a Systematization of Knowledge (SoK) that unifies the taxonomy of vehicular attack surfaces, evaluates FL topologies, and maps adversarial threats such as poisoning and inference attacks. By auditing over 60 publications, we identify recurring pitfalls: artificial IID data splits, reliance on trivial benchmarks, weak adversarial evaluation, and omission of real-time CAN constraints. Finally, we define a forward-looking research agenda and outline minimum benchmarking requirements necessary to transition vehicular FL-IDS from optimistic simulations to secure, real-world deployment.
