Feature Selection Based on Hybrid Technique in Intrusion Detection KDDCup’s99 dataset
Keywords:
interruption, standard classification techniques, demonstrated, comprehensive manualAbstract
Interruption location has turn into a basic segment of system organization because of the immeasurable number of assaults relentlessly debilitate our PCs. Customary interruption recognition frameworks are restricted and do not give a complete answer for the issue. They hunt down potential noxious exercises on system traffics; they once in a while succeed to discover genuine security assaults and oddities. Nonetheless, much of the time, they neglect to identify noxious practices (false negative) or they fire alerts when nothing incorrectly in the system (false positive). Moreover, they require comprehensive manual preparing and human master obstruction. Applying Data Mining (DM) strategies on system movement information is a promising arrangement that helps grow better interruption identification frameworks. Experimental results on the KDDCup’99 data set have demonstrated that our rare class predictive models are much more efficient in the detection of intrusive behavior than standard classification techniques.
References
. approach toward Intrusion Detection System using datamining techniques by G.V. Nadiammai, M. Hemalatha in EgyptianInformatics Journal (2014) 15, 37-50
. Data Mining in Education for Students Academic Performance: ASystematic Review by Er.Anurag Jindal, Er. Williamjeet Singh in ISSN 2277-3061
. Mining With Noise Knowledge: Error-Aware Data Mining byXindong Wu and Xingquan Zhu in IEEE TRANSACTIONS ONSYSTEMS, MAN, AND CYBERNETICS—PART A: SYSTEMSAND HUMANS,VOL. 38, NO. 4, JULY 2008
. Combined Mining: Discovering Informative Knowledge in ComplexData by Longbing Cao, Senior Member, IEEE, Huaifeng Zhang,Member, IEEE, Yanchang Zhao, Effective Member, IEEE, Dan Luo, andChengqi Zhang, Senior Member, IEEE in IEEETRANSACTIONS ON SYSTEMS, MAN, ANDCYBERNETICS—PART B: CYBERNETICS, VOL.41, NO. 3, JUNE 2011
. Intrusion Detection System Using Data Mining Technique: SupportVector Machine by Yogita B. Bhavsar, Kalyani C.Waghmare in International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 3,March 2013)
. ]Huy Anh Nguyen and Deokjai Choi“Application of Data Mining to Network Intrusion Detection: Classifer Selection Model” ChonnamNational University, Computer Science Department. Gwangju. Korea.
. Wenke Lee, Salvatore J.Stolfo, Philip K.Chan “ Real Time Data Mining-Based Intrusion Detection” Computer science Department, FloridaInstitute of Technology, Melbourne
. paul Dokas, Levent Ertoz,Vipin kumar, Aleksandar Lazarevic,Jaideep Srivastava, Pang-Nig Tan “ Data Mining for Network Intrusion Detection” University of Minnesota, Minneapolis. USA
. Theodoros Lappas and Konstantinos Pelechrinis “Data Mining for (Network) Intrusion Detection system” Department of Computer Scienceand Engineering. Riverside.
. Subaira A.S. Anitha P. “An Efficient Classification Mechanism for Network Intrusion Detection System Based on Data Mining Techniques:
. A survey” in International Journal of Computer Science and Business Informatics. Coimbatore, October 2013 India.
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