Fusion of Statistic, Data Mining and Genetic Algorithm for feature selection in Intrusion Detection
Journal Title: International Journal of Advanced Research in Computer Engineering & Technology(IJARCET) - Year 2013, Vol 2, Issue 5
Abstract
The security of information and data is a critical issue in a computer networked environment. In our society computer networks are used to store proprietary information and to provide services for organizations and society. So in order to secure this valuable information from unknown attacks (intrusions) need of intrusion detection system arises. There are many intrusion detection approaches focused on the issues of feature reduction as some of the features are irrelevant or redundant which results in lengthy detection process and degrading the performance of an IDS. So in order to design lightweight IDS we investigate the performance of three feature selection approaches CFS, Information Gain and Gain Ratio. In this paper we propose a fusion model by making use of the three standard algorithms and finally applying genetic algorithm that identify important reduced input features. We apply Naive Bayes classifier on the dataset for evaluating the performance of the proposed method over the standard ones. The reduced attributes shows that proposed algorithm give better performance that is efficient and effective for detecting intrusions
Authors and Affiliations
MeghaAggarwa , Amrita
Keywords
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