Hybrid feature selection algorithm for intrusion detection system

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

Research output: Contribution to journalArticle

7 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1015-1025
Number of pages11
JournalJournal of Computer Science
Volume10
Issue number6
DOIs
Publication statusPublished - 2014

Fingerprint

Genetic programming
Intrusion detection
Feature extraction
Soft computing
Network security
Security of data
Support vector machines
Redundancy
Testing
Processing

Keywords

  • Anomaly detection
  • Bees algorithm
  • Feature selection
  • Ids
  • Linear genetic programming

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Artificial Intelligence

Cite this

Hybrid feature selection algorithm for intrusion detection system. / Hasani, Seyed Reza; Ali Othman, Zulaiha; Kahaki, Seyed Mostafa Mousavi.

In: Journal of Computer Science, Vol. 10, No. 6, 2014, p. 1015-1025.

Research output: Contribution to journalArticle

Hasani, Seyed Reza ; Ali Othman, Zulaiha ; Kahaki, Seyed Mostafa Mousavi. / Hybrid feature selection algorithm for intrusion detection system. In: Journal of Computer Science. 2014 ; Vol. 10, No. 6. pp. 1015-1025.
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