基于模型的隐身传感器攻击随机性监测
Model-based Randomness Monitor for Stealthy Sensor Attacks
摘要：
对现代自主网络物理系统（CPS）的恶意攻击可以利用有关系统动态和噪声特性的信息来隐藏，同时将系统劫持到不期望的状态。如果攻击试图隐藏在系统噪声配置文件中以保持未被检测到，则意图劫持系统的攻击者将改变传感器测量，这与系统模型的预期相矛盾。为了解决这个问题，在本文中，我们提出了一个框架来检测传感器攻击的影响下的CPS传感器测量的非随机性。具体来说，我们提出了一个运行时监视器，利用两个统计测试，Wilcoxon符号秩检验和串行独立性检验来检测测量数据中不一致的模式。对于建议的统计测试，我们提供了正式的保证和边界的攻击检测。我们验证了我们的方法，通过模拟和实验的无人地面车辆（UGV）的隐身攻击，并比较我们的框架与其他异常检测器。
英文摘要：
Malicious attacks on modern autonomous cyber-physical systems (CPSs) can leverage information about the system dynamics and noise characteristics to hide while hijacking the system toward undesired states. Given attacks attempting to hide within the system noise profile to remain undetected, an attacker with the intent to hijack a system will alter sensor measurements, contradicting with what is expected by the system's model. To deal with this problem, in this paper we present a framework to detect non-randomness in sensor measurements on CPSs under the effect of sensor attacks. Specifically, we propose a run-time monitor that leverages two statistical tests, the Wilcoxon Signed-Rank test and Serial Independence Runs test to detect inconsistent patterns in the measurement data. For the proposed statistical tests we provide formal guarantees and bounds for attack detection. We validate our approach through simulations and experiments on an unmanned ground vehicle (UGV) under stealthy attacks and compare our framework with other anomaly detectors.
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