Improved Performance of Intrusion Detection System using feature Reduction and J48 Decision Tree Classification
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Date
2016
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Department of Computer Science, University of Ilorin, Ilorin, Nigeria
Abstract
Due to the obvious importance of accuracy in the performance of intrusion detection
system, in addition to the algorithms used there is an increasing need for more activities
to be carried out, aiming for improved accuracy and reduced real time used in detection.
This paper investigates the use of filtered dataset on the performance of J48 Decision Tree
classifier in its classification of a connection as either normal or an attack. The reduced
dataset is based on using Gain Ratio attribute evaluation technique (entropy) for
performing feature selection (removal of redundant attributes) and feeding the filtered
dataset into a J48 Decision Tree algorithm for classification. A 10-fold cross validation
technique was used for the performance evaluation of the J48 Decision Tree classifier on
the KDD cup 1999 dataset and simulated in WEKA tool. The results showed J48 decision
tree algorithm performed better in terms of accuracy and false positive report on the
reduced dataset than the full dataset(Probing full dataset: 97.8%, Probing reduced
dataset: 99.5%, U2R full dataset: 75%, reduced dataset: 76.9%, R2L full dataset: 98.0%,
reduced dataset: 98.3%).
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Keywords
IDS, gain ratio, data mining, classification, decision tree,
Citation
Abikoye, O.C., Balogun, A.O., Olarewaju, A.K. & Bajeh, A.O .(2016) : Improved Performance of Intrusion Detection System using feature Reduction and J48 Decision Tree Classification. Ilorin Journal of Computer Science and Information Technology. 1(1); 71-88, Published by Department of Computer Science, Faculty of Communication and Information Sciences, University of Ilorin Ilorin, Nigeria