Statistical Based Feature Selection and Ensemble Model for Network Intrusion Detection using Data Mining Technique
Mageswary .G1, Karthikeyan .M2

G. Mageswary, Assistant Professor in the Department of Computer Science at Dharumapuram Gnanambigai Government Arts College for Women, Mayiladuthurai.
Dr. M. Karthikeyan, Assistant Professor in the Division of Computer & Information Science, Faculty of Science, Annamalai University.

Manuscript received on 15 August 2019. | Revised Manuscript received on 25 August 2019. | Manuscript published on 30 September 2019. | PP: 858-864 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4049098319/19©BEIESP | DOI: 10.35940/ijrte.C4049.098319
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Abstract: In today’s world, Information society, computer networks and their interconnected applications are the emerging technologies. Intrusion Detection System (IDS) is used to distinguish the attitude of the network. Now a days, due to frequent and heavy attacks an Network devices, the Intrusion Detection System has become growing and censorious component to secure Network devices. A huge amount of data is needed to build the perfect Intrusion Detection System. This proposed system focuses on feature selection and ensemble of tree based classification methods to build Intrusion Detection System. The implementation of feature selection is fulfilled by using the NSL-KDD dataset. Statistical based feature selection methods such as Pearson’s Correlation, Chi-square, Gain ratio and Symmetrical uncertainty are used to generate four modified datasets. By using that modified datasets the tree based Intrusion Detection models are built using J48, REP Tree and simple CART algorithms. To acquire better prediction of accuracy the algorithms J48, REP tree and simple CART are combined using ensemble method and built perfect tree based Intrusion Detection System.
Index Terms: CART, Ensemble Model, Feature selection, Intrusion Detection System, J48, REP Tree.

Scope of the Article:
Network Modelling and Simulation