You can view the full text of this article for using the link below.
Title: Enhanced relevant feature selection model for intrusion detection systems
Authors: Ayman I. Madbouly; Tamer M. Barakat
Research and Consultancy Department, Deanship of Admission and Registration, King Abdulaziz University, Jeddah, Saudi Arabia; Building Physics and Environment Research Institute, Housing and Building National Research Center, Cairo, Egypt
Electrical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum, Egypt
Abstract: With the increased amount of network threats and intrusions, finding an efficient and reliable defence measure has a great focus as a research field. Intrusion detection systems (IDSs) have been widely deployed as effective defence measure for existing networks. IDSs detect anomalies based on features extracted from network traffic. Network traffic has many features to measure. The problem is that with the huge amount of network traffic we can measure many irrelevant features. These irrelevant features usually affect the performance of detection rate and consume the IDSs resources. In this paper, we proposed an enhanced model to increase attacks detection accuracy and to improve overall system performance. We measured the performance of the proposed model and verified its effectiveness and feasibility by comparing it with nine-different models and with a model that used the 41-features dataset. The results showed that, our enhanced model could efficiently achieves high detection rate, high performance rate, low false alarm rate, and fast and reliable detection process.
Keywords: intrusion detection systems; IDS; classification algorithms; supervised learning; feature selection; data mining; network security; attack detection.
Int. J. of Intelligent Engineering Informatics, 2016 Vol.4, No.1, pp.21 - 45
Submission date: 20 May 2015
Date of acceptance: 23 Jul 2015
Available online: 01 Feb 2016