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Analysis of Intrusion Detection System Using Wrapper Approach through Weka, Genetic Algorithm and Neural Network


Mr. Uttam B. Jadhav , IET Alwar, Rajasthan; Prof.(Dr.) P.K. Dwivedi, IET Alwar, Rajasthan


Intrusion Detection, Neurotree, Wrapper, Perceptron, Kohonen.


the objective is to construct a lightweight Intrusion Detection System (IDS) aimed at detecting anomalies in networks. The crucial part of building lightweight IDS depends on preprocessing of network data, identifying important features and in the design of efficient learning algorithm that classify normal and anomalous patterns. The design of IDS is investigated from these three perspectives. The goals are (i) removing redundant instances that causes the learning algorithm to be unbiased (ii) identifying suitable subset of features by employing a wrapper based feature selection algorithm (iii) realizing proposed IDS with neurotree to achieve better detection accuracy. The lightweight IDS has been developed by using a wrapper based feature selection algorithm that maximizes the specificity and sensitivity of the IDS as well as by employing a neural ensemble decision tree iterative procedure to evolve optimal features. An extensive experimental evaluation of the proposed approach with a family of six decision tree classifiers namely Decision Stump, C4.5, Naive Bayes` Tree, Random Forest, Random Tree and Representative Tree model to perform the detection of anomalous network pattern has been introduced[1].

Other Details

Paper ID: IJSRDV2I1145
Published in: Volume : 2, Issue : 1
Publication Date: 01/03/2014
Page(s): 101-106

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