Graph‐based Bayesian network conditional normalizing flows for multiple time series anomaly detection

International Journal of Intelligent SystemsEarly View RESEARCH ARTICLE Graph-based Bayesian network conditional normalizing flows for multiple time series anomaly detection Xin Xie, Xin Xie orcid.org/0000-0002-4483-8599 School of Information Engineering, East China Jiaotong University, Nanchang, ChinaSearch for more papers by this authorWeiye Ning, Corresponding Author Weiye Ning niniye1998@163.com orcid.org/0000-0003-0845-9791 School of Information Engineering, East China Jiaotong University, Nanchang, China Correspondence Weiye Ning, School of Information Engineering, East China Jiaotong University, Nanchang, 330013 Jiangxi, China. Email: niniye1998@163.comSearch for more papers by this authorYuhui Huang, Yuhui Huang orcid.org/0000-0002-8575-7285 School of Information Engineering, East China Jiaotong University, Nanchang, ChinaSearch for more papers by this authorZhao Li, Zhao Li orcid.org/0000-0003-0025-9517 School of Information Engineering, East China Jiaotong University, Nanchang, ChinaSearch for more papers by this authorSi Yu, Si Yu orcid.org/0000-0002-5561-7206 School of Information Engineering, East China Jiaotong University, Nanchang, ChinaSearch for more papers by this authorHao Yang, Hao Yang orcid.org/0000-0002-4095-6811 State Grid Jiangxi Electric Power Co Ltd, Electric Power Research Institute, Nanchang, ChinaSearch for more papers by this author Xin Xie, Xin Xie orcid.org/0000-0002-4483-8599 School of Information Engineering, East China Jiaotong University, Nanchang, ChinaSearch for more papers by this authorWeiye Ning, Corresponding Author Weiye Ning niniye1998@163.com orcid.org/0000-0003-0845-9791 School of Information Engineering, East China Jiaotong University, Nanchang, China Correspondence Weiye Ning, School of Information Engineering, East China Jiaotong University, Nanchang, 330013 Jiangxi, China. Email: niniye1998@163.comSearch for more papers by this authorYuhui Huang, Yuhui Huang orcid.org/0000-0002-8575-7285 School of Information Engineering, East China Jiaotong University, Nanchang, ChinaSearch for more papers by this authorZhao Li, Zhao Li orcid.org/0000-0003-0025-9517 School of Information Engineering, East China Jiaotong University, Nanchang, ChinaSearch for more papers by this authorSi Yu, Si Yu orcid.org/0000-0002-5561-7206 School of Information Engineering, East China Jiaotong University, Nanchang, ChinaSearch for more papers by this authorHao Yang, Hao Yang orcid.org/0000-0002-4095-6811 State Grid Jiangxi Electric Power Co Ltd, Electric Power Research Institute, Nanchang, ChinaSearch for more papers by this author First published: 29 August 2022 https://doi.org/10.1002/int.23027Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Various devices and sensors of cyber-physical systems interact with each other in time and space, and the generated multiple time series have implicit correlations and highly nonlinear relationships. Determining how to model the multiple time series and capture dependencies through extracting features is the key to anomaly detection. In this paper, we propose a graph-based Bayesian network conditional normalizing flows model for multiple time series anomaly detection, Bayesian network conditional normalizing flows (BNCNF). It applies a Bayesian network to model the causal relationships of multiple time series and introduces a spectral temporal dependency encoder to obtain the representations of interdependency between multiple time series. The representations are introduced as conditional information into the normalizing flows for density estimation, and the data corresponding to low density is judged as anomalies. The experiment are conducted on SWaT, WADI, and SMD datasets, the F1 score reaches 0.95, 0.92, and 0.97 on the three datasets, respectively. The results show that BNCNF has better performance in anomaly detection compared with the current mainstream methods. CONFLICT OF INTEREST The authors declare no conflict of interest. Open Research DATA AVAILABILITY STATEMENT Research data are not shared. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation