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Detecting Stealthy False Data Injection Attacks in the Smart Grid using Ensemble-based Machine Learning
Computers & Security ( IF 5.6 ) Pub Date : 2020-10-01 , DOI: 10.1016/j.cose.2020.101994
Mohammad Ashrafuzzaman , Saikat Das , Yacine Chakhchoukh , Sajjan Shiva , Frederick T. Sheldon

Abstract Stealthy false data injection attacks target state estimation in energy management systems in smart power grids to adversely affect operations of the power transmission systems. This paper presents a data-driven machine learning based scheme to detect stealthy false data injection attacks on state estimation. The scheme employs ensemble learning, where multiple classifiers are used and decisions by individual classifiers are further classified. Two ensembles are used in this scheme, one uses supervised classifiers while the other uses unsupervised classifiers. The scheme is validated using simulated data on the standard IEEE 14-bus system. Experimental results show that the performance of both supervised individual and ensemble models are comparable. However, for unsupervised models, the ensembles performed better than the individual classifiers.

中文翻译:

使用基于集成的机器学习检测智能电网中的隐秘虚假数据注入攻击

摘要 隐身虚假数据注入攻击智能电网能量管理系统中的目标状态估计,对输电系统的运行产生不利影响。本文提出了一种基于数据驱动机器学习的方案,以检测对状态估计的隐蔽虚假数据注入攻击。该方案采用集成学习,其中使用多个分类器,并对单个分类器的决策进行进一步分类。该方案中使用了两个集成,一个使用监督分类器,而另一个使用无监督分类器。该方案使用标准 IEEE 14 总线系统上的模拟数据进行验证。实验结果表明,监督个体模型和集成模型的性能相当。然而,对于无监督模型,
更新日期:2020-10-01
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