Improving Detection Rate of the Network Intrusion Detection System Based on Wrapper Feature Selection Approach

  • Rana F. Najeeb Department of Computer Science, College of Science, AL-Nahrain University, Baghdad, Iraq.
  • Ban N. Dhannoon Department of Computer Science, College of Science, AL-Nahrain University, Baghdad, Iraq.
Keywords: Feature Selection, Firefly Algorithm, Intrusion Detection System, Naive Bayesian Classifier

Abstract

Regarding the security of computer systems, the intrusion detection systems (IDSs) are essential components for the detection of attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in real time. A major drawback of the IDS is their inability to provide adequate sensitivity and accuracy, coupled with their failure in processing enormous data. The issue of classification time is greatly reduced with the IDS through feature selection. In this paper, a new feature selection algorithm based on Firefly Algorithm (FA) is proposed. In addition, the nae bayesian classifier is used to discriminate attack behaviour from normal behaviour in the network traffic. The FA selects the discriminating features from NSL-KDD dataset. The performance of the IDS in the detection of attacks was enhanced by the proposed model and compare with other models.

Published
2018-03-04
How to Cite
NajeebR. F., & DhannoonB. N. (2018). Improving Detection Rate of the Network Intrusion Detection System Based on Wrapper Feature Selection Approach. Iraqi Journal of Science, 59(1B), 426-433. Retrieved from http://scbaghdad.edu.iq/eijs/index.php/eijs/article/view/197
Section
Computer Science