A Fuzzy Based Divide and Conquer Algorithm for Feature Selection in KDD Intrusion Detection Dataset

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Provided by: Interscience Open Access Journals
Topic: Security
Format: PDF
In this paper the authors provide a fuzzy logic based divide and conquer algorithm for feature selection and reduction among large feature set of KDD intrusion detection data set, since a reduced feature set will help to evolve better mining rules. This algorithm introduces a fuzzy idea of dividing the normal record by attacks records or vice-versa, and then considers the feature sets for every attack type separately. Actually, this algorithm is applied on KDD CUP 99 dataset having 37 attack types and selecting important feature among 41 feature of KDD dataset.
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