Performance Evaluation of Decision Tree Classifiers for Ranked Features of Intrusion Detection
نویسنده
چکیده
Decision Trees are considered to be one of the most popular approaches for representing classifier for various disciplines such as statistics, machine learning and data mining. Classification of Intrusion detection, according to their features into either intrusive or non intrusive class is a widely studied problem. Decision trees are useful to detect intrusion from connection records. In this paper, we evaluate the performance of various decision tree classifiers for classifying intrusion detection data. The aim of this paper is to investigate the performance of various decision tree classifiers for ranked intrusion detection data. Information Gain is used to provide ranking to intrusion detection data. Decision tree classifiers evaluated are C4.5, CART, Random Forest and REP Tree.
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