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.

通过使用人工智能技术，本研究提出了一种新颖的智能电网基于模型的网络攻击检测方法，以阻止监控控制和数据采集系统（SCADA）上接收的负载数据的数据完整性网络攻击（DIAs），并使用回归模型进行负载数据的预测，以及通过无监督学习方法对处理后的数据进行聚类，以实现最佳性能。实验结果表明，EE-BiLSTM 方法比其他两种方法更稳健和准确。