A Novel Model-Based Reinforcement Learning for Online Anomaly Detection in Smart Power Grid

• Main Authors: Du, W. (Author), Fu, B. (Author), Wang, C. (Author), Wang, L. (Author), Wang, X. (Author), Zhu, Y. (Author)
• Format: Article
• Language: English
• Published: Hindawi Limited 2023
• Subjects: Anomaly detection; Computer crime; Crime; Cyber-attacks; Decision procedure; Detection methods; E-learning; Learning systems; Markov decision; Model-based reinforcement learning; Network security; Online anomaly detection; On-line diagnosing; Outlier Detection; Reinforcement learning; Reinforcement learnings; Smart grid; Smart power grids
• Online Access: View Fulltext in Publisher
• ISBN: 20507038 (ISSN)
• ISSN: 20507038 (ISSN)
• DOI: 10.1155/2023/6166738

Smart grids must detect cyber-attacks early to ensure their safety and reliability. There have been many outlier detection methods presented in the studies, varying from those requiring instance-by-instance decisions t the online diagnosing methods that require the use of accurate models of an attack. This study proposes a novel intelligent online anomaly or attack detection method based on the partially observable Markov decision procedure (POMDP). The proposed model may be categorized as a general detection method according to the reinforcement learning (RL) architecture for POMDP which can help the learning process based on the award concept. The performance of the proposed model is verified using the IEEE test system. Based on numerical results, the suggested RL-based algorithm shows to be very effective in detecting cyber-attacks against the smart grid quickly and accurately. © 2023 Ling Wang et al.