This work focuses on the use of deep learning for vulnerability analysis of cyber-physical systems (CPS). Specifically, we consider a control architecture widely used in CPS (e.g., robotics), where the low-level control is based on e.g., the extended Kalman filter (EKF) and an anomaly detector. To facilitate analyzing the impact potential sensing attacks could have, our objective is to develop learning-enabled attack generators capable of designing stealthy attacks that maximally degrade system operation. We show how such problem can be cast within a learning-based grey-box framework where parts of the runtime information are known to the attacker, and introduce two models based on feed-forward neural networks (FNN); both models are trained offline, using a cost function that combines the attack effects on the estimation error and the residual signal used for anomaly detection, so that the trained models are capable of recursively generating such effective sensor attacks in real-time. The effectiveness of the proposed methods is illustrated on several case studies.
翻译:这项工作的重点是利用深层学习对网络物理系统进行脆弱性分析。 具体地说,我们考虑在CPS(例如机器人)广泛使用的一种控制结构,即低级控制以扩展的卡尔曼过滤器(EKF)和异常探测器等为基础。为了便于分析潜在感知攻击可能产生的影响,我们的目标是开发能够设计隐形攻击并最大限度地降低系统操作的以学习为动力的攻击发电机。我们展示了如何在基于学习的灰箱框架内丢弃这类问题,使攻击者知道运行时间的部分信息,并引入了两种基于饲料前神经网络的模型(FNN);两种模型都是在离线上培训的,使用成本功能,将攻击对估计误差的影响和用于异常探测的残余信号结合起来,以便经过培训的模型能够实时连续生成这种有效的传感器攻击。在几个案例研究中说明了拟议方法的有效性。