论文标题
基于模型的随机性监视器，用于隐身传感器攻击
Model-based Randomness Monitor for Stealthy Sensor Attacks
论文作者
论文摘要
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征的信息，同时将系统劫持到不希望的状态时。给定试图隐藏系统噪声概况以保持未被发现的攻击，目的是劫持系统的攻击者会改变传感器测量结果，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，以检测传感器攻击效果下CPSS中传感器测量中的非随机性。具体而言，我们提出了一个运行时监视器，该监视器利用两个统计测试，Wilcoxon签名级测试和串行独立性运行测试，以检测测量数据中不一致的模式。对于拟议的统计测试，我们提供了正式的保证和界限以供攻击检测。我们通过在隐秘攻击下对无人接地车辆（UGV）进行模拟和实验来验证我们的方法，并将我们的框架与其他异常探测器进行比较。
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.
