AI 中文总结
研究针对基于无人机的联邦学习的链式攻击，该攻击结合网络层拒绝服务与凭证假冒。通过实验量化不同数据分布下攻击影响，发现单因素认证有漏洞，短期中断会致训练不稳定，还讨论了对无人机部署影响及未来防御方向。
AI 中文摘要
边缘智能（EI）已成为无人机群等关键任务无人平台的变革性模型，通过在网络边缘进行协作模型训练实现。然而，联邦学习（FL）部署的安全性取决于网络可用性和强大的客户端认证机制。本文研究了一种针对基于无人机的FL系统的链式攻击，它将网络层拒绝服务与基于凭证的假冒相结合。我们证明攻击者可以：（1）使用802.11去认证攻击使合法无人机离线，（2）随后使用提取的凭证假冒断开连接的无人机。通过系统的文献综述和在树莓派和Jetson两个不同测试平台上使用Flower框架的实证验证，我们量化了在独立同分布（IID）和非独立同分布（Non-IID）数据分布下可用性中断的影响，并确认单因素认证允许断开连接后的假冒。我们的发现表明，即使是短期无线中断也会导致严重的训练不稳定，特别是在非IID条件下，而认证差距使攻击者能够无缝替换断开连接的节点。我们讨论了对关键任务无人机部署的复合影响，并概述了未来针对可用性和认证漏洞的防御方向。
英文摘要
Edge Intelligence (EI) has emerged as a transformative model for mission-critical unmanned platforms, such as drone swarms, by enabling collaborative model training at the network periphery. However, the security of FL deployments depends on both network availability and robust client authentication mechanisms. This paper investigates a chained attack against drone-based FL systems that combines network-layer denial-of-service with credential-based impersonation. We demonstrate that an adversary can: (1) force legitimate drones offline using 802.11 deauthentication attacks, and (2) subsequently impersonate the disconnected drone using extracted credentials. Through a systematic literature review and empirical validation using the Flower framework on two distinct testbeds of Raspberry Pi and Jetsons, we quantify the impact of availability disruptions under Independent and Identically Distributed (IID) and Non-Independently and Identically Distributed (Non-IID) data distributions, and confirm that single-factor authentication permits post-disconnect impersonation. Our findings reveal that even short-term wireless interruptions cascade into substantial training instability, particularly under non-IID conditions, while the authentication gap enables adversaries to seamlessly replace disconnected nodes. We discuss the compounded implications for mission-critical drone deployments and outline directions for future defenses addressing both availability and authentication vulnerabilities.
