A hybrid deep learning model for discrimination of physical disturbance and cyber-attack detection in smart grid

A smart grid (SG) consists of an interconnection of an electrical grid, communication, and information networks. The rapid developments of SG technologies have resulted in complex cyber-physical systems. Due to these complexities, the attack surfaces of SGs broaden, and their vulnerabilities to cyber-physical threats increase. SG security systems focus on the protection of significant units and sub-systems of communication and power networks from malicious threats and external attacks. False data injection attack (FDIA) is known as the most severe threat to SG systems. In this paper, a method of optimizing convolutional neural networks - long short-term memory (CNN-LSTM) with particle swarm optimization (PSO) to detect FDIA in the SG system is proposed. This model uses phasor measurement unit (PMU) measurements to detect an abnormal measurement value and determine the type of this anomaly. The complex hyperparameter space of the CNNLSTM is optimized by the PSO. A detailed numerical comparison is made using the state-of-the-art deep learning (DL) architectures like LSTM, PSO-LSTM, and CNN-LSTM models to verify the accuracy and effectiveness of the proposed model. The results show that the model outperforms other DL models. In addition, the model has a high accuracy rate that provides decision support for the stable and safe operation of SG systems. In this respect, the proposed detection model is a candidate for building a more robust and powerful detection and protection mechanism.