{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/online-cyber-attack-detection-in-smart-grid-a","title":"Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach","arxiv_id":"1809.05258","date":"2018-09-14","proceeding":null,"authors":["Mehmet Necip Kurt","Oyetunji Ogundijo","Chong Li","Xiaodong Wang"],"abstract":"Early detection of cyber-attacks is crucial for a safe and reliable operation\nof the smart grid. In the literature, outlier detection schemes making\nsample-by-sample decisions and online detection schemes requiring perfect\nattack models have been proposed. In this paper, we formulate the online\nattack/anomaly detection problem as a partially observable Markov decision\nprocess (POMDP) problem and propose a universal robust online detection\nalgorithm using the framework of model-free reinforcement learning (RL) for\nPOMDPs. Numerical studies illustrate the effectiveness of the proposed RL-based\nalgorithm in timely and accurate detection of cyber-attacks targeting the smart\ngrid.","url_abs":"http://arxiv.org/abs/1809.05258v1","url_pdf":"http://arxiv.org/pdf/1809.05258v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"online-cyber-attack-detection-in-smart-grid-a","repo_url":"https://github.com/mnecipkurt/tsg19","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"cyber-attack-detection","task_name":"Cyber Attack Detection"},{"task_slug":"outlier-detection","task_name":"Outlier Detection"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[{"method_slug":"acgpn","method_name":"ACGPN"},{"method_slug":"awd-lstm","method_name":"AWD-LSTM"},{"method_slug":"activation-regularization","method_name":"Activation Regularization"},{"method_slug":"dropconnect","method_name":"DropConnect"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"embedding-dropout","method_name":"Embedding Dropout"},{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"},{"method_slug":"temporal-activation-regularization","method_name":"Temporal Activation Regularization"},{"method_slug":"variational-dropout","method_name":"Variational Dropout"},{"method_slug":"weight-tying","method_name":"Weight Tying"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}