{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T08:33:19Z","timestamp":1771921999678,"version":"3.50.1"},"reference-count":35,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,5,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In order to correct the deficiencies of intrusion detection technology, the entire computer and network security system are needed to be more perfect. This work proposes an improved k-means algorithm and an improved Apriori algorithm applied in data mining technology to detect network intrusion and security maintenance. The classical KDDCUP99 dataset has been utilized in this work for performing the experimentation with the improved algorithms. The algorithm\u2019s detection rate and false alarm rate are compared with the experimental data before the improvement. The outcomes of proposed algorithms are analyzed in terms of various simulation parameters like average time, false alarm rate, absolute error as well as accuracy value. The results show that the improved algorithm advances the detection efficiency and accuracy using the designed detection model. The improved and tested detection model is then applied to a new intrusion detection system. After intrusion detection experiments, the experimental results show that the proposed system improves detection accuracy and reduces the false alarm rate. A significant improvement of 90.57% can be seen in detecting new attack type intrusion detection using the proposed algorithm.<\/jats:p>","DOI":"10.1515\/jisys-2020-0146","type":"journal-article","created":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T16:30:20Z","timestamp":1620232220000},"page":"664-676","source":"Crossref","is-referenced-by-count":9,"title":["Application of data mining technology in detecting network intrusion and security maintenance"],"prefix":"10.1515","volume":"30","author":[{"given":"Yongkuan","family":"Zhu","sequence":"first","affiliation":[{"name":"Department of Information Engineering, Henan Polytechnic , Zhengzhou , Henan, 450046 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gurjot Singh","family":"Gaba","sequence":"additional","affiliation":[{"name":"School of Electronics and Electrical Engineering, Lovely Professional University , Phagwara 144411 , India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fahad M.","family":"Almansour","sequence":"additional","affiliation":[{"name":"Depertment of Computer Science, College of Sciences and Arts in Rass, Qassim University , Buraydah 51452 , Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roobaea","family":"Alroobaea","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computers and Information Technology, Taif University , Taif , KSA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mehedi","family":"Masud","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computers and Information Technology, Taif University , Taif , KSA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2021,5,5]]},"reference":[{"key":"2025120523322249930_j_jisys-2020-0146_ref_001","doi-asserted-by":"crossref","unstructured":"Yao H, Wang Q, Wang L, Zhang P, Li M, Liu Y. 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