In this paper, a novel artificial intelligence-based cyber-attack detection model for smart grids is developed to stop data integrity cyber-attacks (DIAs) on the received load data by supervisory control and data acquisition (SCADA). In the proposed model, first the load data is forecasted using a regression model and after processing stage, the processed data is clustered using the unsupervised learning method. In this work, in order to achieve the best performance, three load forecasting methods (i.e. extra tree regression (ETR), long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM)) are utilized as regression models and their performance is compared. For clustering and outlying detection, the covariance elliptic envelope (EE) is employed as an unsupervised learning method. To examine the proposed model, the hourly load data of the power company of the city of Johor in Malaysia is employed and Two common DIAs, which are DIAs targeting economic loss and DIAs targeting blackouts, are used to evaluate the accuracy of detection methods in several scenarios. The simulation results show that the proposed EE-BiLSTM method can perform more robust and accurate compared to the other two methods.

翻译：本文提出了一种新型的基于人工智能的智能电网网络攻击检测模型，用于阻止对由监控与数据采集（SCADA）系统接收的负荷数据实施的数据完整性攻击（DIAs）。在该模型中，首先利用回归模型对负荷数据进行预测，经过处理阶段后，采用无监督学习方法对处理后的数据进行聚类。为获得最佳性能，本研究使用了三种负荷预测方法（即极端树回归（ETR）、长短期记忆（LSTM）和双向长短期记忆（BiLSTM））作为回归模型，并对其性能进行了比较。在聚类和异常检测方面，采用协方差椭圆包络（EE）作为无监督学习方法。为验证所提出的模型，采用了马来西亚柔佛州电力公司的每小时负荷数据，并针对两种常见的DIAs——以经济损失为目标的DIAs和以停电为目标的DIAs——在多种场景下评估了检测方法的准确性。仿真结果表明，与其他两种方法相比，所提出的EE-BiLSTM方法具有更强的鲁棒性和更高的准确性。