{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T18:33:42Z","timestamp":1773772422836,"version":"3.50.1"},"reference-count":32,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,2,5]],"date-time":"2021-02-05T00:00:00Z","timestamp":1612483200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62061136006"],"award-info":[{"award-number":["62061136006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2462020YJRC001"],"award-info":[{"award-number":["2462020YJRC001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["110051360002"],"award-info":[{"award-number":["110051360002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Scientific Research Initiation Funds","award":["62061136006"],"award-info":[{"award-number":["62061136006"]}]},{"name":"Scientific Research Initiation Funds","award":["2462020YJRC001"],"award-info":[{"award-number":["2462020YJRC001"]}]},{"name":"Scientific Research Initiation Funds","award":["110051360002"],"award-info":[{"award-number":["110051360002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Security and Communication Networks"],"published-print":{"date-parts":[[2021,2,5]]},"abstract":"<jats:p>Anomaly detection algorithms (ADA) have been widely used as services in many maintenance monitoring platforms. However, there are numerous algorithms that could be applied to these fast changing stream data. Furthermore, in IoT stream data due to its dynamic nature, the phenomena of conception drift happened. Therefore, it is a challenging task to choose a suitable anomaly detection service (ADS) in real time. For accurate online anomalous data detection, this paper developed a service selection method to select and configure ADS at run-time. Initially, a time-series feature extractor (Tsfresh) and a genetic algorithm-based feature selection method are applied to swiftly extract dominant features which act as representation for the stream data patterns. Additionally, stream data and various efficient algorithms are collected as our historical data. A fast classification model based on XGBoost is trained to record stream data features to detect appropriate ADS dynamically at run-time. These methods help to choose suitable service and their respective configuration based on the patterns of stream data. The features used to describe and reflect time-series data\u2019s intrinsic characteristics are the main success factor in our framework. Consequently, experiments are conducted to evaluate the effectiveness of features closed by genetic algorithm. Experimentations on both artificial and real datasets demonstrate that the accuracy of our proposed method outperforms various advanced approaches and can choose appropriate service in different scenarios efficiently.<\/jats:p>","DOI":"10.1155\/2021\/6677027","type":"journal-article","created":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T02:09:20Z","timestamp":1612836560000},"page":"1-10","source":"Crossref","is-referenced-by-count":12,"title":["An Anomaly Detection Algorithm Selection Service for IoT Stream Data Based on Tsfresh Tool and Genetic Algorithm"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3720-0642","authenticated-orcid":true,"given":"Zhongguo","family":"Yang","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data, North China University of Technology Beijing, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1813-1415","authenticated-orcid":true,"given":"Irshad Ahmed","family":"Abbasi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Science and Arts at Belgarn, P.O. Box 60, Sabt Al-Alaya 61985, University of Bisha, Saudi Arabia"}]},{"given":"Elfatih Elmubarak","family":"Mustafa","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Science and Arts at Belgarn, P.O. Box 60, Sabt Al-Alaya 61985, University of Bisha, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2753-8615","authenticated-orcid":true,"given":"Sikandar","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, China University of Petroleum-Beijing, Beijing 102249, China"},{"name":"Beijing Key Laboratory of Petroleum Data Mining, China University of Petroleum-Beijing, Beijing 102249, China"}]},{"given":"Mingzhu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data, North China University of Technology Beijing, Beijing, China"}]}],"member":"311","reference":[{"key":"1","first-page":"743","article-title":"Comparative evaluation of anomaly detection techniques for sequence data","author":"V. 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