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Feature Subset Selection Using Genetic Algorithm for Intrusion Detection System

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KDD 99 intrusion detection datasets, which are based on DARPA 98, is a labeled dataset studied in the field of intrusion detection. The respected KDD dataset contains numerous features, in which some of them are irrelevant or less effective to detect the attacks. This study is set to improve the classification of intrusions by means of selecting significant features. Binary Genetic Algorithm (BGA) is proposed for feature selection in order to decrease the number of unrelated features. The selected features are then become the input for the classification task using a standard Multi-layer Perceptron (MLP) classifier. The results achieved show very high classification accuracy and low false positive rate with the lowest CPU time.
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Document Type: Research Article

Publication date: January 1, 2014

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  • ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
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