Email this article A Supervised Filter Based Feature Selection Algorithm For Intrusion Detection-FMIFS
Abstract
We propose a shared data based algorithm that analytically chooses the optimal feature for classification. This common information based component choice calculation can manage straightly and nonlinearly subordinate data features. Its reasonability is surveyed in the cases of framework intrusion distinguishing proof. An Intrusion Detection System (IDS), named Least Square Support Vector Machine based IDS (LSSVM-IDS), is manufactured using the segments picked by our proposed include choice calculation. The execution of LSSVM-IDS is surveyed using three interruption location appraisal datasets, to be particular KDD Cup 99, NSL-KDD and Kyoto 2006+ dataset.
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