{"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/anomaly-detection-for-industrial-control","title":"Anomaly Detection for Industrial Control Systems Using Sequence-to-Sequence Neural Networks","arxiv_id":"1911.04831","date":"2019-11-12","proceeding":null,"authors":["Jonguk Kim","Jeong-Han Yun","Hyoung Chun Kim"],"abstract":"This study proposes an anomaly detection method for operational data of industrial control systems (ICSs). Sequence-to-sequence neural networks were applied to train and predict ICS operational data and interpret their time-series characteristic. The proposed method requires only a normal dataset to understand ICS's normal state and detect outliers. This method was evaluated with SWaT (secure water treatment) dataset, and 29 out of 36 attacks were detected. The reported method also detects the attack points, and 25 out of 53 points were detected. This study provides a detailed analysis of false positives and false negatives of the experimental results.","url_abs":"https://arxiv.org/abs/1911.04831v1","url_pdf":"https://arxiv.org/pdf/1911.04831v1.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":"anomaly-detection-for-industrial-control","repo_url":"https://github.com/jukworks/swat-seq2seq","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}