arXiv cs.AI· Yalin E. Sagduyu, Tugba Erpek, Nathan Stephenson, Mengran Xue·· 12 小时前AI 评分26
车联网联邦学习中的干净标签投毒攻击与防御博弈
Clean-label Poisoning Attack and Defense Game for Federated Learning in Vehicular Networks
AI 导读
针对车联网联邦学习中的 Basic Safety Message（BSM）异常检测分类，研究提出一种特征空间干净标签投毒攻击：将良性样本向恶意特征极值扰动，在保持标签不变的同时降低类别可分性。
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