{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:17:11Z","timestamp":1760710631714},"reference-count":0,"publisher":"IGI Global","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7]]},"abstract":"<jats:p>Aiming at the problem of DDoS attack detection in internet of things (IoT) environment, statistical and machine-learning algorithms are proposed to model and analyze the network traffic of DDoS attack. Docker-based virtualization platform is designed and configured to collect IoT network traffic data. Then the packet-level, flow-level, and second-level network traffic datasets are generated, and the importance of features in different traffic datasets are sorted. By SKlearn and TensorFlow machine-learning software framework, different machine learning algorithms are researched and compared. In packet-level DDoS attack detection, KNN algorithm achieves the best results; the accuracy is 92.8%. In flow-level DDoS attack detection, the voting algorithm achieves the best results; the accuracy is 99.8%. In second-level DDoS attack detection, the RNN algorithm behaves best results; the accuracy is 97.1%. The DDoS attack detection method combined with statistical analysis and machine-learning can effectively detect large-scale DDoS attacks on the internet of things simulation experimental environment.<\/jats:p>","DOI":"10.4018\/ijisp.2021070101","type":"journal-article","created":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T14:21:48Z","timestamp":1623075708000},"page":"1-18","source":"Crossref","is-referenced-by-count":12,"title":["DDoS Attack Simulation and Machine Learning-Based Detection Approach in Internet of Things Experimental Environment"],"prefix":"10.4018","volume":"15","author":[{"given":"Hongsong","family":"Chen","sequence":"first","affiliation":[{"name":"University of Science and Technology, Beijing, China"}]},{"given":"Caixia","family":"Meng","sequence":"additional","affiliation":[{"name":"Railway Police College, China"}]},{"given":"Jingjiu","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Science and Technology, Beijing, China"}]}],"member":"2432","container-title":["International Journal of Information Security and Privacy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=281038","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T08:04:02Z","timestamp":1651824242000},"score":1,"resource":{"primary":{"URL":"http:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJISP.2021070101"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2021,7]]},"references-count":0,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.4018\/ijisp.2021070101","relation":{},"ISSN":["1930-1650","1930-1669"],"issn-type":[{"value":"1930-1650","type":"print"},{"value":"1930-1669","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7]]}}}