This paper focuses on the detection and classification of the cyberattack types in smart grid substation automation systems. The previous work in the literature focuses only on the detection of the attacks without providing any information regarding the attack’s type, which is a key in identifying the appropriate countermeasures. In this paper, a novel approach that uses a fine tree bagging ensemble learning technique is developed to detect and classify the cyberattack types from normal and power quality disturbances. Furthermore, the relevant features of different cyber-attack types such as message suppression, denial-of-service and data manipulation have been identified. The proposed approach is tested on a publicly available dataset and the results are compared to three other machine learning algorithms, namely decision tree, nearest neighbor, and support vector machine. The results have shown that the proposed approach is very effective in the detection and the classification of the attack types as well as it is insensitive to the selection of the training and the testing datasets unlike other existing approaches in the literature.

中文翻译：

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智能电网网络攻击类型分类：具有特征选择的基于精细树装袋的集成学习方法

本文重点研究智能电网变电站自动化系统中网络攻击类型的检测和分类。文献中以前的工作仅侧重于攻击的检测，而没有提供有关攻击类型的任何信息，而这是确定适当对策的关键。本文开发了一种使用精细树装袋集成学习技术的新方法来检测和分类正常和电能质量扰动中的网络攻击类型。此外，还确定了不同网络攻击类型的相关特征，例如消息抑制、拒绝服务和数据操纵。所提出的方法在公开可用的数据集上进行了测试，并将结果与​​其他三种机器学习算法（即决策树、最近邻和支持向量机）进行了比较。结果表明，所提出的方法在攻击类型的检测和分类方面非常有效，并且与文献中的其他现有方法不同，它对训练和测试数据集的选择不敏感。