Journal of Computer Science

HYBRID FEATURE SELECTION ALGORITHM FOR INTRUSION DETECTION SYSTEM

Seyed Reza Hasani, Zulaiha Ali Othman and Seyed Mostafa Mousavi Kahaki

DOI : 10.3844/jcssp.2014.1015.1025

Journal of Computer Science

Volume 10, Issue 6

Pages 1015-1025

Abstract

Network security is a serious global concern. Usefulness Intrusion Detection Systems (IDS) are increasing incredibly in Information Security research using Soft computing techniques. In the previous researches having irrelevant and redundant features are recognized causes of increasing the processing speed of evaluating the known intrusive patterns. In addition, an efficient feature selection method eliminates dimension of data and reduce redundancy and ambiguity caused by none important attributes. Therefore, feature selection methods are well-known methods to overcome this problem. There are various approaches being utilized in intrusion detections, they are able to perform their method and relatively they are achieved with some improvements. This work is based on the enhancement of the highest Detection Rate (DR) algorithm which is Linear Genetic Programming (LGP) reducing the False Alarm Rate (FAR) incorporates with Bees Algorithm. Finally, Support Vector Machine (SVM) is one of the best candidate solutions to settle IDSs problems. In this study four sample dataset containing 4000 random records are excluded randomly from this dataset for training and testing purposes. Experimental results show that the LGP_BA method improves the accuracy and efficiency compared with the previous related research and the feature subcategory offered by LGP_BA gives a superior representation of data.

Copyright

© 2014 Seyed Reza Hasani, Zulaiha Ali Othman and Seyed Mostafa Mousavi Kahaki. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.