Attention-aware deep reinforcement learning for detecting false data injection attacks in smart grids

Abstract

Cyber-attacks have long undermined the security and stable operation of the smart grid. The increasing share of intermittent energy aggravates the uncertainty of the grid environment and makes it easier for attacks to be stealthy. In recent research, attackers can construct highly stealthy false data injection attacks (FDIAs) with as little-known information as possible. Faced with a volatile grid environment and severe cyber threats, we propose a deep reinforcement learning FDIAs detection method. It focuses on state attention to address the problems of state feature extraction in existing reinforcement learning detection methods. The proposed approach adds an attention mechanism to the model-free deep reinforcement learning detection algorithm to extract state features. Selective attention helps us focus on the important parts instead of distracting from irrelevant details, making the states more representative and distinguishable, thus improving the reinforcement learning framework to detect attacks faster and more accurately. The proposed method has experimented on IEEE-14, and IEEE-118 bus systems provided by the Institute of Electrical and Electronics Engineers (IEEE) and demonstrates the method’s satisfaction in terms of detection accuracy and efficiency compared with similar methods.© 2017 Elsevier Inc. All rights reserved.