An Investigation into the Effectiveness of Machine Learning Techniques for Intrusion Detection

  • E. G. Dada Department of Computer Engineering, Faculty of Engineering, University of Maiduguri, P.M.B 1069, Maiduguri, Borno State. Nigeria
  • J. S. Bassi Department of Computer Engineering, Faculty of Engineering, University of Maiduguri, P.M.B 1069, Maiduguri, Borno State. Nigeria
  • O. O. Adekunle Department of Computer Science, Faculty of Science, National Open University of Nigeria (NOUN), 91, Cadastral Zone, Nnamdi Azikiwe Expressway, Jabi, Abuja, Nigeria.

Abstract

Attacks on computer systems are becoming progressively frequent. Many machine learning techniques have been developed in the bid to increase the effectiveness of intrusion detection systems (IDS). However, the sophistication of intrusion attacks on computer networks and the large size of dataset pose a serious challenge as they drastically reduce the effectiveness of the IDS. We do not propose any new algorithm in this paper. However, experiments were conducted to investigate the performance of six (6) machine learning techniques found in literature and how they can effectively detect intrusion activities on a network. This work examines how effective each algorithm under investigation handles intrusion events. In our experiment, the NSL-KDDTrain+ dataset was partitioned into training subgroups subject to the type of network protocol. Subsequent to this, extraneous and unneeded attributes are removed from each training subgroup. The effectiveness of the algorithms was evaluated. The experimental results show that the Logistic Model Tree Induction method is more effective in terms of (classification accuracy: 99.40%, F-measure: 0.991, false positive rate: 0.32%, precision: 98.90% and Receiver Operating Characteristics: 98.6%) compared to the other five machine learning techniques we investigated.

Published
Dec 1, 2017
How to Cite
DADA, E. G.; BASSI, J. S.; ADEKUNLE, O. O.. An Investigation into the Effectiveness of Machine Learning Techniques for Intrusion Detection. Arid Zone Journal of Engineering, Technology and Environment, [S.l.], v. 13, n. 6, p. 764-778, dec. 2017. ISSN 2545-5818. Available at: <http://azojete.com.ng/index.php/azojete/article/view/288>. Date accessed: 22 apr. 2018.
Section
Articles