基于扩展卡尔曼滤波的虚假数据攻击检测方法

虚假数据注入攻击以破坏电力系统SCADA的数据完整性和可用性为目标,其检测方法对智能电网的安全与稳定运行具有重要意义。基于扩展卡尔曼滤波提出了一种虚假数据注入攻击检测方法。该方法利用检测数据递归得到系统的实时运行状态,从而达到有效检测识别电力系统中虚假数据注入情况的目的。另外,该方法能同时评估系统过去运行状态和预测未来系统状态的变化情况。同时,该方法能有效识别系统精确模型未知情况下的虚假数据注入攻击。以IEEE-14节点和30节点模型为研究对象,其结果表明所提方法不仅能弥补传统状态估计方法无法检测虚假数据注入攻击的缺陷,而且具有储存量小、易于实现等优点。

Detection Method Against False Data Injection Attack Based on Extended Kalman Filter

False data injection attacks can destroy the data integrity and availability in SCADA systems, so the detection method for data security and stable operation of smart grid is of great significance. The extended Kalman filter method uses the detected data to recursively obtain the real-time state of the system, so as to achieve the purpose of effectively detecting and checking whether or not there are false data injections in the power system. In addition, this method can not only evaluate the past operating states, but also predict the minute changes of future system. Furthermore, this method can effectively identify the false data injection attack under unknown conditions of the system. IEEE-14 node and 30-node models are used as the benchmarks for detecting false data injection attacks. The results show that the proposed method not only is superior to traditional state estimation methods that fail to detect false data injection attack, but also exhibits many advantages, such as small storage capacity and easy realization.