{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T22:10:09Z","timestamp":1654121409665},"reference-count":12,"publisher":"IGI Global","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2011,1,1]]},"abstract":"<p>In this paper, the authors propose a new feature selection procedure for intrusion detection, which is based on filter method used in machine learning. They focus on Correlation Feature Selection (CFS) and transform the problem of feature selection by means of CFS measure into a mixed 0-1 linear programming problem with a number of constraints and variables that is linear in the number of full set features. The mixed 0-1 linear programming problem can then be solved by using branch-and-bound algorithm. This feature selection algorithm was compared experimentally with the best-first-CFS and the genetic-algorithm-CFS methods regarding the feature selection capabilities. Classification accuracies obtained after the feature selection by means of the C4.5 and the BayesNet over the KDD CUP\u201999 dataset were also tested. Experiments show that the authors\u2019 method outperforms the best-first-CFS and the genetic-algorithm-CFS methods by removing much more redundant features while keeping the classification accuracies or getting better performances.<\/p>","DOI":"10.4018\/jmcmc.2011010102","type":"journal-article","created":{"date-parts":[[2011,10,19]],"date-time":"2011-10-19T16:40:05Z","timestamp":1319042405000},"page":"21-34","source":"Crossref","is-referenced-by-count":4,"title":["Improving Effectiveness of Intrusion Detection by Correlation Feature Selection"],"prefix":"10.4018","volume":"3","author":[{"given":"Hai Thanh","family":"Nguyen","sequence":"first","affiliation":[{"name":"Gj\u00f8vik University College, Norway"}]},{"given":"Katrin","family":"Franke","sequence":"additional","affiliation":[{"name":"Gj\u00f8vik University College, Norway"}]},{"given":"Slobodan","family":"Petrovic","sequence":"additional","affiliation":[{"name":"Gj\u00f8vik University College, Norway"}]}],"member":"2432","reference":[{"key":"jmcmc.2011010102-0","doi-asserted-by":"publisher","DOI":"10.1016\/S0377-2217(99)00106-X"},{"key":"jmcmc.2011010102-1","doi-asserted-by":"publisher","DOI":"10.1016\/S0377-2217(00)00097-7"},{"key":"jmcmc.2011010102-2","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, Y., Cheng, X.-Q., & Guo, L. (2006). Survey and Taxonomy of Feature SelectionAlgorithms in Intrusion Detection System. In Proceedings of Inscrypt 2006 (LNCS 4318, pp. 153-167).","DOI":"10.1007\/11937807_13"},{"key":"jmcmc.2011010102-3","author":"T. H.Cormen","year":"2001","journal-title":"Introduction to Algorithms"},{"key":"jmcmc.2011010102-4","doi-asserted-by":"crossref","unstructured":"Crescenzo, G. D., Ghosh, A., & Talpade, R. (2005). Towards a theory of intrusion detection. In Proceedings of the 10th European Symposium on Research in Computer Security (ESORICS\u201905) (pp. 267-286). New York: Springer.","DOI":"10.1007\/11555827_16"},{"key":"jmcmc.2011010102-5","author":"R. O.Duda","year":"2001","journal-title":"Pattern Classification"},{"key":"jmcmc.2011010102-6","author":"E. E.Ghiselli","year":"1964","journal-title":"Theory of Psychological Measurement"},{"key":"jmcmc.2011010102-7","doi-asserted-by":"crossref","unstructured":"Gu, G., Fogla, P., Dagon, D., Lee, W., & Skoric, B. (2006). Towards an information-theoretic framework for analyzing intrusion detection systems. In Proceedings of the 11th European Symposium on Research in Computer Security (ESORICS\u201906) (pp. 527-546). New York: Springer.","DOI":"10.1007\/11863908_32"},{"key":"jmcmc.2011010102-8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-35488-8"},{"key":"jmcmc.2011010102-9","unstructured":"Hall, M. (1999). Correlation Based Feature Selection for Machine Learning. Unpublished doctoral dissertation, University of Waikato, Department of Computer Science, Hamilton, New Zealand."},{"key":"jmcmc.2011010102-10","author":"H.Liu","year":"2008","journal-title":"Computational Methods of Feature Selection"},{"key":"jmcmc.2011010102-11","author":"J. R.Quinlan","year":"1993","journal-title":"C4.5: Programs for Machine Learning"}],"container-title":["International Journal of Mobile Computing and Multimedia Communications"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=51659","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T21:39:12Z","timestamp":1654119552000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/jmcmc.2011010102"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2011,1,1]]},"references-count":12,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2011,1]]}},"URL":"https:\/\/doi.org\/10.4018\/jmcmc.2011010102","relation":{},"ISSN":["1937-9412","1937-9404"],"issn-type":[{"value":"1937-9412","type":"print"},{"value":"1937-9404","type":"electronic"}],"subject":[],"published":{"date-parts":[[2011,1,1]]}}}