Unsupervised Stacked Autoencoders for Anomaly Detection on Smart Cyber-physical Grids

Smart Cyber Physical Grids are the new wave of power system technology that integrates networks of sensors with power stations for more efficient power generation and distribution. While utilizing communication networks is accompanied with tremendous advantages, it also increases the vulnerability of power systems to cyber attacks. Many methods for security and attack detection have been proposed in literature; however, most papers do not consider the imbalance of data in real power systems. In this paper, we propose a deep learning based method, referred to as Ensemble Stacked AutoEncoder (ESAE), aimed at tackling the problem of data imbalance. This method achieves superior performance on imbalanced data by developing a deep representation learning model to construct new balanced representations. The detection accuracy and model performance is improved by utilizing an ensemble architecture based on Stacked Autoencoders and Random Forest classifiers to detect attacks from the new representations. The proposed method is tested on all degrees of data imbalance using test cases of IEEE 14-bus, 30-bus, and 57-bus systems. Comparisons are made to several classifiers to demonstrate the effectiveness of the proposed algorithm.