Paper Info
Title
Intrusion detection system based on a modified binary grey wolf optimisation
Abstract
One critical issue within network security refers to intrusion detection. The nature of intrusion attempts appears to be nonlinear, wherein the network traffic performance is unpredictable, and the problematic space features are numerous. These make intrusion detection systems (IDSs) a challenge within the research arena. Hence, selecting the essential aspects for intrusion detection is crucial in information security and with that, this study identified the related features in building a computationally efficient and effective intrusion system. Accordingly, a modified feature selection (FS) algorithm called modified binary grey wolf optimisation (MBGWO) is proposed in this study. The proposed algorithm is based on binary grey wolf optimisation to boost the performance of IDS. The new FS algorithm selected an optimal number of features. In order to evaluate the proposed algorithm, the benchmark of NSL-KDD network intrusion, which was modified from 99-data set KDD cup to assess issues linked with IDS, had been applied in this study. Additionally, the support vector machine was employed to classify the data set effectively. The proposed FS and classification algorithms enhanced the performance of the IDS in detecting attacks. The simulation outcomes portrayed that the proposed algorithm enhanced the accuracy of intrusion detection up to 99.22% and reduction in the number of features from 41 to 14.
Year
DOI
Venue
2020
10.1007/s00521-019-04103-1
Neural Computing and Applications
Keywords
DocType
Volume
Intrusion detection system, Anomaly-based detection, Modified binary grey wolf optimisation, Grey wolf optimisation
Journal
32
Issue
ISSN
Citations 
10
0941-0643
3
PageRank 
References 
Authors
0.36
10
5
Name
Order
Citations
PageRank
Qusay M. Alzubi130.36
Mohammed Anbar2169.05
Zakaria N. M. Alqattan381.18
Mohammed Azmi Al-Betar462043.69
Rosni Abdullah515624.82