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A machine learning approach with appropriate normalization and prepossessing increases NIDS performance. This paper presents an empirical study on various normalization methods implemented on a benchmark network traffic dataset, KDD Cup\u201999, that has been used to evaluate the NIDS model. The present study shows decimal normalization has a better prediction performance than non-normalized traffic data categorized into \u2018normal\u2019 or \u2018intrusive\u2019 classes.<\/jats:p>","DOI":"10.3233\/jifs-211191","type":"journal-article","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T14:09:32Z","timestamp":1638886172000},"page":"1749-1766","source":"Crossref","is-referenced-by-count":16,"title":["A comparative simulation of normalization methods for machine learning-based intrusion detection systems using KDD Cup\u201999 dataset"],"prefix":"10.1177","volume":"42","author":[{"given":"Satish","family":"Kumar","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Kakryal, Katra, Jammu and Kashmir, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sunanda","family":"Gupta","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Shri Mata 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