{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T12:02:40Z","timestamp":1779796960258,"version":"3.53.1"},"reference-count":0,"publisher":"Agora University of Oradea","issue":"3","license":[{"start":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T00:00:00Z","timestamp":1779753600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["INT J COMPUT COMMUN, Int. J. Comput. Commun. Control"],"abstract":"<jats:p>Network Intrusion Detection Systems (NIDS) deal with class imbalance in network traffic data, where minority attack classes are underestimated. FCM-Cosine, a modified Fuzzy C-Means clustering algorithm, replaces Euclidean distance in the objective function with Cosine distance to better capture directional similarity in high-dimensional feature spaces. The cluster-then-classify framework decomposes the global intrusion detection problem into localized classification sub-problems to detect minority attack classes. Five classifiers have been examined on the CICIoT2023 dataset at two scales (16,100 and 465,000 samples). FCM-Cosine had an average F1-Macro of 69.36%, while Decision Tree had 86.79%, resulting in a 37.97% improvement over direct training. The framework is ten times faster than SMOTE (19.18s vs. 189.73s average training time) and scales nearly linearly with dataset size. Results demonstrate that FCM-Cosine offers competitive classification performance with computational efficiency for large-scale NIDS deployments.<\/jats:p>","DOI":"10.15837\/ijccc.2026.3.7305","type":"journal-article","created":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T11:13:21Z","timestamp":1779794001000},"source":"Crossref","is-referenced-by-count":0,"title":["Cosine Distance-Based Fuzzy C-Means Clustering for Local Classification in Imbalanced Network Intrusion Detection"],"prefix":"10.15837","volume":"21","author":[{"family":"Ngoc-Bich Giap Thi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Van-Nhan Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Anh-Thu Pham","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Trong-Minh Hoang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"6528","published-online":{"date-parts":[[2026,5,26]]},"container-title":["INTERNATIONAL JOURNAL OF COMPUTERS  COMMUNICATIONS &amp; CONTROL"],"original-title":[],"deposited":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T11:13:22Z","timestamp":1779794002000},"score":1,"resource":{"primary":{"URL":"https:\/\/univagora.ro\/jour\/index.php\/ijccc\/article\/view\/7305"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,26]]},"references-count":0,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,5,26]]}},"URL":"https:\/\/doi.org\/10.15837\/ijccc.2026.3.7305","relation":{},"ISSN":["1841-9844","1841-9836"],"issn-type":[{"value":"1841-9844","type":"electronic"},{"value":"1841-9836","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5,26]]}}}