用正常行为数据识别机器人集群中的异常个体，无需预先知道攻击方式。
Discovering Antagonists in Networks of Systems: Robot Deployment
- 基于环境上下文的正则化流模型预测机器人运动概率
- 对五类攻击策略检测准确率超80%，误报率低于5%
- 仅需仿真正常数据，适合实际部署中未知威胁场景
提出一种情境异常检测方法，用于检测执行覆盖任务的机器人集群中个体的物理运动异常。通过训练模拟正常行为的正则化流模型，预测当前环境下机器人运动的概率。应用时，利用预测概率判断机器人是否为异常个体。该方法在五种不同攻击策略下进行评估，仅使用可获取的正常行为仿真数据训练，无需预先了解异常特征。最佳检测准则对每类攻击均实现至少80%的识别率，且正常机器人误报率低于5%。硬件实验验证结果与仿真一致。相比现有方法，该模型在预测性能和检测鲁棒性上均有提升。
原文摘要 · Abstract (English)
A contextual anomaly detection method is proposed and applied to the physical motions of a robot swarm executing a coverage task. Using simulations of a swarm's normal behavior, a normalizing flow is trained to predict the likelihood of a robot motion within the current context of its environment. During application, the predicted likelihood of the observed motions is used by a detection criterion that categorizes a robot agent as normal or antagonistic. The proposed method is evaluated on five different strategies of antagonistic behavior. Importantly, only readily available simulated data of normal robot behavior is used for training such that the nature of the anomalies need not be known beforehand. The best detection criterion correctly categorizes at least 80% of each antagonistic type while maintaining a false positive rate of less than 5% for normal robot agents. Additionally, the method is validated in hardware experiments, yielding results similar to the simulated scenarios. Compared to the state-of-the-art approach, both the predictive performance of the normalizing flow and the robustness of the detection criterion are increased.
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