{"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/statistical-anomaly-detection-via-composite","title":"Statistical Anomaly Detection via Composite Hypothesis Testing for Markov Models","arxiv_id":"1702.08435","date":"2017-02-27","proceeding":null,"authors":["Jing Zhang","Ioannis Ch. Paschalidis"],"abstract":"Under Markovian assumptions, we leverage a Central Limit Theorem (CLT) for\nthe empirical measure in the test statistic of the composite hypothesis\nHoeffding test so as to establish weak convergence results for the test\nstatistic, and, thereby, derive a new estimator for the threshold needed by the\ntest. We first show the advantages of our estimator over an existing estimator\nby conducting extensive numerical experiments. We find that our estimator\ncontrols better for false alarms while maintaining satisfactory detection\nprobabilities. We then apply the Hoeffding test with our threshold estimator to\ndetecting anomalies in two distinct applications domains: one in communication\nnetworks and the other in transportation networks. The former application seeks\nto enhance cyber security and the latter aims at building smarter\ntransportation systems in cities.","url_abs":"http://arxiv.org/abs/1702.08435v3","url_pdf":"http://arxiv.org/pdf/1702.08435v3.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":"statistical-anomaly-detection-via-composite","repo_url":"https://github.com/hbhzwj/SADIT","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"statistical-anomaly-detection-via-composite","repo_url":"https://github.com/jingzbu/ROCHM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"hypothesis-testing","task_name":"Two-sample testing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}