{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T02:29:29Z","timestamp":1773714569114,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T00:00:00Z","timestamp":1632787200000},"content-version":"vor","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":["61671190"],"award-info":[{"award-number":["61671190"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In order to adapt to the rapid development of network technology and network security detection in different scenarios, the generalization ability of the classifier needs to be further improved and has the ability to detect unknown attacks. However, the generalization ability of a single classifier is limited to dealing with class imbalance, and the previous ensemble methods inevitably increase the training cost. Therefore, in this paper, a novel network intrusion detection algorithm combined with group convolution is proposed to improve the generalization performance of the model. The basic classifier uses group convolution with symmetric structure instead of ordinary convolution neural network, which is trained by the cyclic cosine annealing learning rate. Through snapshot ensemble, the generalization ability of the integration model is improved without increasing the training cost. The effectiveness of this method is proved on NSL-KDD and UNSW-NB15 datasets compared to six other ensemble methods, the classification accuracy can achieve 85.82% and 80.38%, respectively.<\/jats:p>","DOI":"10.3390\/sym13101814","type":"journal-article","created":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T21:39:29Z","timestamp":1632865169000},"page":"1814","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Network Intrusion Detection Algorithm Combined with Group Convolution Network and Snapshot Ensemble"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9118-230X","authenticated-orcid":false,"given":"Aili","family":"Wang","sequence":"first","affiliation":[{"name":"Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, College of Measurement and Control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China"}]},{"given":"Wenya","family":"Wang","sequence":"additional","affiliation":[{"name":"Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, College of Measurement and Control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China"}]},{"given":"Huaming","family":"Zhou","sequence":"additional","affiliation":[{"name":"Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, College of Measurement and Control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China"}]},{"given":"Jian","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Wuxi Vocational College of Science and Technology, Wuxi 214028, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,28]]},"reference":[{"key":"ref_1","first-page":"69","article-title":"Intrusion Detection System: A Review","volume":"9","author":"Sharma","year":"2015","journal-title":"Int. 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