{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T00:49:31Z","timestamp":1777682971379,"version":"3.51.4"},"reference-count":28,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JHS"],"published-print":{"date-parts":[[2024,1,10]]},"abstract":"<jats:p>An intrusion detection method using rough-fuzzy set and parallel quantum genetic algorithm (RFS-QGAID) is proposed in this paper. The RFS-QGAID is applied to solve the serious problems of determining the optimal antibodies subsets used to detect an anomaly. To obtain a simplified antibodies collection for high dimensional Log data sets, RFS is applied to delete the redundant antibody features and obtain the optimal antibodies features combination. Then, the optimal attitudes are entered into the QGA classifier for learning and training in the following stage. At last, the detected Log antigens are fed into RFS-QGAID, and we can classify the intrusion types. With RFS-QGAID, we give the simulations, the results on real Log data sets show that: the higher detection accuracy of RFS-QGAID is higher detection accuracy, but the false negative rate is lower for small samples sets, the adaptive performance is higher than other detection algorithms.<\/jats:p>","DOI":"10.3233\/jhs-222070","type":"journal-article","created":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T11:28:30Z","timestamp":1693567710000},"page":"69-81","source":"Crossref","is-referenced-by-count":6,"title":["Intrusion detection using rough-fuzzy set and parallel quantum genetic algorithm"],"prefix":"10.1177","volume":"30","author":[{"given":"Zhang","family":"Ling","sequence":"first","affiliation":[{"name":"Software Engineering College, Zhengzhou University of Light Industry, Henan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gui","family":"Qi","sequence":"additional","affiliation":[{"name":"Software Engineering College, Zhengzhou University of Light Industry, Henan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huang","family":"Min","sequence":"additional","affiliation":[{"name":"Software Engineering College, Zhengzhou University of Light Industry, Henan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JHS-222070_ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118745"},{"issue":"1","key":"10.3233\/JHS-222070_ref2","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.eswa.2016.09.041","article-title":"Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system","volume":"67","author":"Alyaseen","year":"2017","journal-title":"Expert Systems with Applications"},{"issue":"4","key":"10.3233\/JHS-222070_ref3","doi-asserted-by":"publisher","first-page":"1184","DOI":"10.1016\/j.jnca.2011.01.002","article-title":"Mutual information-based feature selection for intrusion detection systems","volume":"34","author":"Amiri","year":"2011","journal-title":"Journal of Network and Computer Applications"},{"key":"10.3233\/JHS-222070_ref4","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1016\/j.inffus.2022.09.026","article-title":"Fusion of statistical importance for feature selection in deep neural network-based intrusion detection system","volume":"90","author":"Ankit","year":"2023","journal-title":"Information Fusion"},{"issue":"3","key":"10.3233\/JHS-222070_ref5","first-page":"196","article-title":"Network intrusion detection design using feature selection of soft computing paradigms","volume":"4","author":"Chou","year":"2008","journal-title":"International Journal of computational Intelligence"},{"issue":"36","key":"10.3233\/JHS-222070_ref6","doi-asserted-by":"publisher","first-page":"3087","DOI":"10.1002\/int.22397","article-title":"Enhancing intrusion detection with feature selection and neural network","volume":"7","author":"Chunhui","year":"2021","journal-title":"International Journal OF Intelligent Systems"},{"issue":"8","key":"10.3233\/JHS-222070_ref7","doi-asserted-by":"publisher","first-page":"2505","DOI":"10.1109\/TSG.2017.2703842","article-title":"Real-time detection of false data injection attacks in smart grid: A deep learning-based intelligent mechanism","volume":"5","author":"He","year":"2017","journal-title":"IEEE Trans. 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