论文标题
对工业控制系统的网络攻击探测器的中毒攻击
Poisoning Attacks on Cyber Attack Detectors for Industrial Control Systems
论文作者
论文摘要
最近，已经提出了基于神经网络（NN）的方法，包括自动编码器，用于检测针对工业控制系统（ICS）的网络攻击。使用系统操作期间收集的数据，通常会重新训练此类检测器，以应对受监视信号的自然演化（即概念漂移）。但是，通过利用这种机制，攻击者可以在训练时伪造损坏的传感器提供的信号，并毒化检测器的学习过程，以使网络攻击在测试时未被发现。通过这项研究，我们是第一个证明对ICS网络攻击在线探测器的中毒攻击的人。我们提出了两种截然不同的攻击算法，即基于插值和背部的中毒，并证明了它们对合成和现实世界中ICS数据的有效性。我们还讨论和分析了一些潜在的缓解策略。
Recently, neural network (NN)-based methods, including autoencoders, have been proposed for the detection of cyber attacks targeting industrial control systems (ICSs). Such detectors are often retrained, using data collected during system operation, to cope with the natural evolution (i.e., concept drift) of the monitored signals. However, by exploiting this mechanism, an attacker can fake the signals provided by corrupted sensors at training time and poison the learning process of the detector such that cyber attacks go undetected at test time. With this research, we are the first to demonstrate such poisoning attacks on ICS cyber attack online NN detectors. We propose two distinct attack algorithms, namely, interpolation- and back-gradient based poisoning, and demonstrate their effectiveness on both synthetic and real-world ICS data. We also discuss and analyze some potential mitigation strategies.
