Open AccessA FUZZY BASED DIVIDE AND CONQUER ALGORITHM FOR FEATURE SELECTION IN KDD INTRUSION DETECTION DATASET
Author(s) -
Anish Das,
S. Sathya
Publication year - 2013
Publication title -
international journal of computer science and informatics
Language(s) - English
Resource type - Journals
ISSN - 2231-5292
DOI - 10.47893/ijcsi.2013.1132
Subject(s) - feature selection , intrusion detection system , computer science , data mining , divide and conquer algorithms , feature (linguistics) , fuzzy logic , rough set , artificial intelligence , set (abstract data type) , pattern recognition (psychology) , machine learning , algorithm , linguistics , philosophy , programming language
This paper provides 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. The selected features are used in TANAGRA [11, 12] data mining tool to classify the dataset (i.e. KDD 99) for every attack vs. normal using various classification algorithms [5, 6]. The result for feature selection and classification shows a reduced set and maximized classification rate respectively.