Improved Performance of Intrusion Detection Systems using Feature Reduction and J48 Decision Tree Classification Algorithm

  • Oluwakemi C. Abikoye
  • Abdullateef Gbenga Balogun Department of Computer Science, University of Ilorin, Ilorin
  • A.R. Olarewaju Department of Computer Science, University of Ilorin, Ilorin
  • Amos O. Bajeh Department of Computer Science, University of Ilorin, Ilorin
Keywords: IDS, Feature reduction, data mining, classification, decision tree, gain ratio

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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Published
2020-05-03
How to Cite
Abikoye, O. C., Balogun, A. G., Olarewaju, A., & Bajeh, A. O. (2020). Improved Performance of Intrusion Detection Systems using Feature Reduction and J48 Decision Tree Classification Algorithm. Ilorin Journal of Computer Science and Information Technology, 1(1), 71 - 88. Retrieved from https://iljcsit.com.ng/index.php/ILJCSIT/article/view/13