Intelligent attackers can suitably tamper sensor/actuator data at various Smart grid surfaces causing intentional power oscillations, which if left undetected, can lead to voltage disruptions. We develop a novel combination of formal methods and machine learning tools that learns power system dynamics with the objective of generating unsafe yet stealthy false data based attack sequences. We enable the grid with anomaly detectors in a generalized manner so that it is difficult for an attacker to remain undetected. Our methodology, when applied on an IEEE 14 bus power grid model, uncovers stealthy attack vectors even in presence of such detectors.

翻译：智能攻击者可在智能电网的多个界面上篡改传感器/执行器数据，引发有意的功率振荡。此类攻击若未被检测到，将导致电压崩溃。我们提出了一种融合形式化方法与机器学习工具的创新方案，通过学习电力系统动态特性，生成隐蔽且不安全的虚假数据攻击序列。同时，我们以泛化方式为电网部署异常检测器，使攻击者难以保持隐蔽。将该方法应用于IEEE 14节点电网模型时，即使存在此类检测器，仍能发现隐蔽攻击向量。