用可解释AI识别群体智能系统中的数据投毒攻击
Explainable AI Based Diagnosis of Poisoning Attacks in Evolutionary Swarms
- 基于进化智能建模群体协作，模拟投毒攻击影响
- 投毒超过10%时可检测到低效协作策略
- 提取攻击痕迹，适合安全监控与群体系统研究
群体系统（如多无人机网络）在监控、搜救等关键场景中依赖自主个体的去中心化决策，实现高效协同。然而，真实环境中团队级策略易受数据投毒攻击影响，导致协作失效或产生对抗行为。为此，本文提出一个基于可解释AI的分析框架，采用进化智能建模代理间交互，使最优协作联盟自动生成。通过系统性地对群体模型施加数据操纵攻击，验证了可解释AI在量化攻击影响、提取攻击特征方面的能力。结果表明，当模型被投毒超过10%时，能有效识别出非最优策略及效率低下的协作模式。
原文摘要 · Abstract (English)
Swarming systems, such as for example multi-drone networks, excel at cooperative tasks like monitoring, surveillance, or disaster assistance in critical environments, where autonomous agents make decentralized decisions in order to fulfill team-level objectives in a robust and efficient manner. Unfortunately, team-level coordinated strategies in the wild are vulnerable to data poisoning attacks, resulting in either inaccurate coordination or adversarial behavior among the agents. To address this challenge, we contribute a framework that investigates the effects of such data poisoning attacks, using explainable AI methods. We model the interaction among agents using evolutionary intelligence, where an optimal coalition strategically emerges to perform coordinated tasks. Then, through a rigorous evaluation, the swarm model is systematically poisoned using data manipulation attacks. We showcase the applicability of explainable AI methods to quantify the effects of poisoning on the team strategy and extract footprint characterizations that enable diagnosing. Our findings indicate that when the model is poisoned above 10%, non-optimal strategies resulting in inefficient cooperation can be identified.
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