A novel federated deep learning for intrusion detection in smart grid cyber-physical systems

Abstract

The fusion of sophisticated computational, communicative, and physical elements in Smart Grid Cyber-Physical Systems (SGCPS) has greatly improved the efficiency and reliability of power grids. However, this complexity introduces enhanced cybersecurity risks, evidenced by significant cyberattacks on the Ukrainian power grid during 2015 and 2016. Despite progress in Artificial Intelligence (AI)-driven security solutions for SGCPS, practical deployment of these technologies is often limited due to a lack of high-quality attack data and owners’ hesitance to distribute sensitive details. This paper introduces an innovative strategy to fortify SGCPS against diverse network threats via a comprehensive intrusion detection system. We present a deep learning model leveraging a temporal convolutional network with multi-feature integration, aimed at robust threat identification. We also propose a federated learning framework enabling various SGCPS to jointly develop an extensive intrusion detection model, ensuring data privacy. Moreover, we incorporate a gradient compression technique utilizing the Long Short Term Memory-$\beta$-Total Correlation Variational Autoencoder (LSTM-$\beta$-TCVAE) model to enhance and secure model parameters throughout the training phase. Thorough experimental validations confirm the efficacy of our method in recognizing multiple cyber threat types to SGCPS and its advantages over current methods.