通过主动探测提升传感器攻击下的控制鲁棒性
Active Bayesian Inference for Robust Control under Sensor False Data Injection Attacks
- 将感知系统建模为二分图，结合异常检测构建贝叶斯网络推断受损传感器
- 利用系统非线性设计主动探测策略，显著提升攻击识别能力
- 仅关闭受损传感器，保持状态估计可靠性，适合工业控制系统应用
我们提出一种框架，弥合网络物理系统中传感器攻击检测与恢复之间的差距。该框架将现代复杂感知流水线建模为二分图，结合异常检测告警构成贝叶斯网络，用于推断受损传感器。通过利用系统非线性设计主动探测策略，最大化不同攻击假设间的可区分性；同时选择性禁用受损传感器以维持可靠的状态估计。我们提出基于阈值的探测策略，并通过简化部分可观测马尔可夫决策过程（POMDP）形式化验证其有效性。在倒立摆系统上进行单传感器和多传感器攻击实验表明，该方法显著优于异常鲁棒和预测基基准方法，尤其在长期攻击下表现更优。
原文摘要 · Abstract (English)
We present a framework for bridging the gap between sensor attack detection and recovery in cyber-physical systems. The proposed framework models modern-day, complex perception pipelines as bipartite graphs, which combined with anomaly detector alerts defines a Bayesian network for inferring compromised sensors. An active probing strategy exploits system nonlinearities to maximize distinguishability between attack hypotheses, while compromised sensors are selectively disabled to maintain reliable state estimation. We propose a threshold-based probing strategy and show its effectiveness via a simplified partially observable Markov decision process (POMDP) formulation. Experiments on an inverted pendulum under single and multi-sensor attacks show that our method significantly outperforms outlier-robust and prediction-based baselines, especially under prolonged attacks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。