Research Article Open Access

HYBRID FEATURE SELECTION ALGORITHM FOR INTRUSION DETECTION SYSTEM

Seyed Reza Hasani1, Zulaiha Ali Othman1 and Seyed Mostafa Mousavi Kahaki1
  • 1 University Kebangsaan Malaysia, Malaysia

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.

Journal of Computer Science
Volume 10 No. 6, 2014, 1015-1025

DOI: https://doi.org/10.3844/jcssp.2014.1015.1025

Submitted On: 20 November 2013 Published On: 31 January 2014

How to Cite: Hasani, S. R., Othman, Z. A. & Kahaki, S. M. M. (2014). HYBRID FEATURE SELECTION ALGORITHM FOR INTRUSION DETECTION SYSTEM. Journal of Computer Science, 10(6), 1015-1025. https://doi.org/10.3844/jcssp.2014.1015.1025

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Keywords

  • IDS
  • Linear Genetic Programming
  • Feature Selection
  • Bees Algorithm
  • Anomaly Detection