Performance of Various Feature Selection Techniques Under Loaded Networks

Provided by: International Journal of Computer Applications
Topic: Data Management
Format: PDF
Network traffic dataset has many features and all may not contribute in detection of threats. Rejecting irrelevant features may increase performance of IDS by reducing computational time. In this paper various available feature selection methods like correlation feature selection, chi squared attribute evaluation, consistency subset evaluation, filtered attribute evaluation, filtered subset evaluation, gain ratio attribute evaluation, information gain attribute evaluation, One RA attribute evaluation, symmetrical uncert attribute evaluation are tested on three classifiers naive bayes, J48 and PART by using weka data mining and machine learning tool on UCI KDD CUP 1999 network traffic dataset.

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