False Data Injection (FDI) attacks are a common form of Cyber-attack targetting smart grids. Detection of stealthy FDI attacks is impossible by the current bad data detection systems. Machine learning is one of the alternative methods proposed to detect FDI attacks. This paper analyzes three various supervised learning techniques, each to be used with three different feature selection (FS) techniques. These methods are tested on the IEEE 14-bus, 57-bus, and 118-bus systems for evaluation of versatility. Accuracy of the classification is used as the main evaluation method for each detection technique. Simulation study clarify the supervised learning combined with heuristic FS methods result in an improved performance of the classification algorithms for FDI attack detection.
翻译:假数据输入攻击是网络攻击瞄准智能网格的一种常见形式。目前不良的数据探测系统无法探测隐性外国直接投资攻击。机器学习是发现外国直接投资攻击的替代方法之一。本文分析了三种不同的监督学习技术,每种技术都使用三种不同的特征选择(FS)技术。这些方法通过IEE 14-Bus、57-Bus和118-Bus系统进行测试,以评价多功能性。分类的准确性被作为每种探测技术的主要评价方法。模拟研究澄清了监督学习与超标准FS方法相结合的结果是改进了外国直接投资攻击探测分类算法的性能。