Open AccessStatistical Based Feature Selection and Ensemble Model for Network Intrusion Detection using Data Mining Technique
Author(s) -
G. Mageswary*,
M. Karthikeyan
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c4049.098319
Subject(s) - c4.5 algorithm , intrusion detection system , computer science , feature selection , data mining , feature (linguistics) , tree (set theory) , network security , decision tree , selection (genetic algorithm) , cart , anomaly based intrusion detection system , artificial intelligence , machine learning , support vector machine , engineering , mathematics , computer network , mathematical analysis , linguistics , philosophy , naive bayes classifier , mechanical engineering
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.