Online Location-based Detection of False Data Injection Attacks in Smart Grid Using Deep Learning

Abstract

The smart grid is a multi-dimensional data-generating cyber-physical system. Distributed architectures and the heterogeneous nature of the Internet-of-Things (IoT) sensors make it more prone to various cyber-attacks. False data injection attacks (FDIAs) have recently emerged as significant threats to smart grid state estimation. As a result, real-time locational detection of stealthy FDIAs is critical for smart grid security and reliability. In this paper, we introduce a comparative analysis of various deep-learning approaches to test their effectiveness in the location-based detection of FDIA. Also, a deep learning approach is developed by constructing a multi-feature architecture based on a convolution neural network and long short-term memory network (MCNN-LSTM). Extensive testing on IEEE test cases has demonstrated that the proposed approach outperforms the existing deep learning approaches in locating FDIAs for small and large systems under different attack scenarios. We evaluate the performance of each model in terms of presence and location-based detection accuracy, model complexity, and prediction time. Extensive results in the IEEE 14 and IEEE 118-bus systems show that the suggested architecture has a locational detection accuracy of more than 94% and 95%, respectively. From the results, we can conclude the proposed approach is more robust, scalable, and faster in detecting the locations of compromised measurements than the other deep learning models.