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However, there is still a high false alarm rate and missing intrusions due to class imbalance in the multi-class dataset. This imbalanced distribution of classes results in low detection accuracy for the minority classes. This paper proposes a Synthetic Multi-minority Oversampling (SMMO) framework by integrating with a collaborative feature selection (CoFS) approach in network intrusion detection systems. Our framework aims to increase the detection accuracy of the extreme minority classes (i.e., user-to-root and remote-to-local attacks) by improving the dataset\u2019s class distribution and selecting relevant features. In our framework, SMMO generates synthetic data and iteratively over-samples multi-minority classes. And the collaboration of correlation-based feature selection with an evolutionary algorithm selects essential features. We evaluate our framework with a random forest, J48, BayesNet, and AdaBoostM1. In a multi-class NSL-KDD dataset, the experimental results show that the proposed framework significantly improves the detection accuracy of the extreme minority classes compared with other approaches.<\/jats:p>","DOI":"10.1007\/s44196-022-00171-9","type":"journal-article","created":{"date-parts":[[2023,2,11]],"date-time":"2023-02-11T14:34:22Z","timestamp":1676126062000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["SMMO-CoFS: Synthetic Multi-minority Oversampling with Collaborative Feature Selection for Network Intrusion Detection System"],"prefix":"10.1007","volume":"16","author":[{"given":"Yeshalem Gezahegn","family":"Damtew","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongmei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,11]]},"reference":[{"key":"171_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2016\/3672758","volume":"2016","author":"R Abd Rahman","year":"2016","unstructured":"Abd Rahman, R., Ramli, R., Jamari, Z., et al.: Evolutionary algorithm with roulette-tournament selection for solving aquaculture diet formulation. 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