Smart Grid Cyber Attacks Detection Using Supervised Learning and Heuristic Feature Selection
Smart Grid Cyber Attacks Detection Using Supervised Learning and Heuristic Feature Selection
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使用监督学习和启发式特征选择检测智能电网网络攻击
DOI：
10.1109/sege.2019.8859946
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
A. Dehghantanha
中科院分区：
文献类型：
--
作者：
Jacob Sakhnini;H. Karimipour;A. Dehghantanha
虚假数据注入（FDI）攻击是针对智能电网的一种常见网络攻击形式。目前的不良数据检测系统不可能检测到隐形的FDI攻击。机器学习是检测FDI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，每种技术都将与三种不同的特征选择（FS）技术一起使用。这些方法在IEEE 14总线、57总线和118总线系统上进行了测试，以评估通用性。分类的准确性是评价各种检测技术的主要方法。仿真研究表明，将监督学习与启发式FS方法相结合，提高了FDI攻击检测分类算法的性能。
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.