{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T14:10:19Z","timestamp":1770819019802,"version":"3.50.1"},"reference-count":0,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2014,1,1]],"date-time":"2014-01-01T00:00:00Z","timestamp":1388534400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2014,4]]},"abstract":"<jats:p>This paper presents a new kNN-based evolving neuro-fuzzy inference system (kENFIS). The main function of kENFIS is to detect computer worms which possess a constant threat to Internet and have caused a significant damage to business recently. However, kENFIS can be applied to solve complex real-world problems that demand fuzzy rule-based systems able to adapt their parameters and ultimately evolve their rule base. kENFIS partitions the input space into clusters by using a new designed kNN-based evolving fuzzy clustering method (kEFCM) and organizes the rule base using Takagi-Sugeno method. The evolving operation is performed by incremental supervised learning. It integrates the simplicity of k-nearest neighbors (kNN) algorithm with the accuracy of least-square method (LSM) to building up the knowledge-base and learning with a few training examples. The performance of kENFIS has been evaluated and compared with some existing well-known algorithms. Also, its ability to detect worms on-line was tested. The evaluation results demonstrate that kENFIS can be effectively applied in worm detection as well as in other classification problems.<\/jats:p>","DOI":"10.3233\/ifs-130868","type":"journal-article","created":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T18:26:41Z","timestamp":1575311201000},"page":"1893-1908","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":12,"title":["kENFIS: kNN-based evolving neuro-fuzzy inference system for computer worms detection"],"prefix":"10.1177","volume":"26","author":[{"given":"A.","family":"Shubair","sequence":"first","affiliation":[{"name":"Department of Educational Technology, Sultan Qaboos University, Muscat, Oman"}]},{"given":"Sureswaran","family":"Ramadass","sequence":"additional","affiliation":[{"name":"NAV6 Center of Excellence, Universiti Sains Malaysia USM, Penang, Malaysia"}]},{"given":"Altyeb Altaher","family":"Altyeb","sequence":"additional","affiliation":[{"name":"NAV6 Center of Excellence, Universiti Sains Malaysia USM, Penang, Malaysia"}]}],"member":"179","published-online":{"date-parts":[[2014,1]]},"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/IFS-130868","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/IFS-130868","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T12:53:58Z","timestamp":1770814438000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/IFS-130868"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,1]]},"references-count":0,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2014,4]]}},"alternative-id":["10.3233\/IFS-130868"],"URL":"https:\/\/doi.org\/10.3233\/ifs-130868","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,1]]}}}