车联网联邦学习中的干净标签投毒攻防博弈研究
Clean-label Poisoning Attack and Defense Game for Federated Learning in Vehicular Networks
AI 导读
一项 arXiv 研究针对车联网联邦学习的干净标签投毒攻击提出攻防博弈分析。攻击者在特征空间中把良性样本扰动到恶意特征极端以破坏类别可分性，同时保留标签；防御侧提出特征一致性重标注方法来检测并重标可疑样本。博弈论建模刻画了均衡策略，结果显示攻击随被控节点数扩大而增强，而防御可在不降低干净准确率的前提下压低攻击成功率，并将均衡推向不利于攻击的区域。
正文 · 原文
Abstract:This paper studies interactions between clean-label poisoning attack and defense in vehicular networks using federated learning (FL). Basic Safety Message (BSM) classification for anomaly detection is modeled as a distributed learning problem over a vehicular network with dynamic topology. As a critical threat to FL, a feature-space clean-label attack is introduced, wherein benign samples are perturbed toward malicious feature extremes, degrading class separability while preserving labels. To counter this, a feature-consistency relabeling defense is proposed to detect and relabel suspicious samples. A game-theoretic framework is developed to capture attack-defense interactions and characterize equilibrium strategies based on attack effectiveness and cost parameters. Results show that the attack scales effectively with compromised nodes, while the proposed defense keeps attack success low without degrading clean accuracy and shifts equilibrium regions toward attack-averse regimes.
| Subjects: | Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Signal Processing (eess.SP) |
| Cite as: | arXiv:2610.32765 [cs.NI] |
| (or arXiv:2610.32765v1 [cs.NI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32765 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Tugba Erpek [view email]
[v1]
Fri, 23 Oct 2026 15:06:14 UTC (84 KB)
来源：arXiv cs.AI · arxiv.org