Improving bee algorithm based feature selection in intrusion detection system using membrane computing

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Despite the great benefits accruable from the debut of computer and the internet, efforts are constantly being put up by fraudulent and mischievous individuals to compromise the integrity, confidentiality or availability of electronic information systems. In Cyber-security parlance, this is termed 'intrusion'. Hence, this has necessitated the introduction of Intrusion Detection Systems (IDS) to help detect and curb different types of attack. However, based on the high volume of data traffic involved in a network system, effects of redundant and irrelevant data should be minimized if a qualitative intrusion detection mechanism is genuinely desirous. Several attempts, especially feature subset selection approach using Bee Algorithm (BA), Linear Genetic Programming (LGP), Support Vector Decision Function Ranking (SVDF), Rough, Rough-DPSO, and Mutivariate Regression Splines (MARS) have been advanced in the past to measure the dependability and quality of a typical IDS. The observed problem among these approaches has to do with their general performance. This has therefore motivated this research work. We hereby propose a new but robust algorithm called membrane algorithm to improve the Bee Algorithm based feature subset selection technique. This Membrane computing paradigm is a class of parallel computing devices. Data used were taken from KDD-Cup 99 Dataset which is the acceptable standard benchmark for intrusion detection. When the final results were compared to those of the existing approaches, using the three standard IDS measurements-Attack Detection, False Alarm and Classification Accuracy Rates, it was discovered that Bee Algorithm-Membrane Computing (BA-MC) approach is a better technique. This is because our approach produced very high attack detection rate of 89.11%, classification accuracy of 95.60% and also generated a reasonable decrease in false alarm rate of 0.004. Receiver Operating Characteristic (ROC) curve was used for results interpretation.

Original languageEnglish
Pages (from-to)523-529
Number of pages7
JournalJournal of Networks
Volume9
Issue number3
DOIs
Publication statusPublished - 2014

Fingerprint

Intrusion detection
Feature extraction
Membranes
Set theory
Curbs
Genetic programming
Parallel processing systems
Splines
Information systems
Availability
Internet

Keywords

  • Bee algorithm
  • Cybersecurity
  • Feature selection
  • Intrusion detection system
  • Membrane computing

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

@article{4a1b84babc4f471791980c49c4bcce6b,
title = "Improving bee algorithm based feature selection in intrusion detection system using membrane computing",
abstract = "Despite the great benefits accruable from the debut of computer and the internet, efforts are constantly being put up by fraudulent and mischievous individuals to compromise the integrity, confidentiality or availability of electronic information systems. In Cyber-security parlance, this is termed 'intrusion'. Hence, this has necessitated the introduction of Intrusion Detection Systems (IDS) to help detect and curb different types of attack. However, based on the high volume of data traffic involved in a network system, effects of redundant and irrelevant data should be minimized if a qualitative intrusion detection mechanism is genuinely desirous. Several attempts, especially feature subset selection approach using Bee Algorithm (BA), Linear Genetic Programming (LGP), Support Vector Decision Function Ranking (SVDF), Rough, Rough-DPSO, and Mutivariate Regression Splines (MARS) have been advanced in the past to measure the dependability and quality of a typical IDS. The observed problem among these approaches has to do with their general performance. This has therefore motivated this research work. We hereby propose a new but robust algorithm called membrane algorithm to improve the Bee Algorithm based feature subset selection technique. This Membrane computing paradigm is a class of parallel computing devices. Data used were taken from KDD-Cup 99 Dataset which is the acceptable standard benchmark for intrusion detection. When the final results were compared to those of the existing approaches, using the three standard IDS measurements-Attack Detection, False Alarm and Classification Accuracy Rates, it was discovered that Bee Algorithm-Membrane Computing (BA-MC) approach is a better technique. This is because our approach produced very high attack detection rate of 89.11{\%}, classification accuracy of 95.60{\%} and also generated a reasonable decrease in false alarm rate of 0.004. Receiver Operating Characteristic (ROC) curve was used for results interpretation.",
keywords = "Bee algorithm, Cybersecurity, Feature selection, Intrusion detection system, Membrane computing",
author = "Rufai, {Kazeem I.} and Muniyandi, {Ravie Chandren} and {Ali Othman}, Zulaiha",
year = "2014",
doi = "10.4304/jnw.9.3.523-529",
language = "English",
volume = "9",
pages = "523--529",
journal = "Journal of Networks",
issn = "1796-2056",
publisher = "Academy Publisher",
number = "3",

}

TY - JOUR

T1 - Improving bee algorithm based feature selection in intrusion detection system using membrane computing

AU - Rufai, Kazeem I.

AU - Muniyandi, Ravie Chandren

AU - Ali Othman, Zulaiha

PY - 2014

Y1 - 2014

N2 - Despite the great benefits accruable from the debut of computer and the internet, efforts are constantly being put up by fraudulent and mischievous individuals to compromise the integrity, confidentiality or availability of electronic information systems. In Cyber-security parlance, this is termed 'intrusion'. Hence, this has necessitated the introduction of Intrusion Detection Systems (IDS) to help detect and curb different types of attack. However, based on the high volume of data traffic involved in a network system, effects of redundant and irrelevant data should be minimized if a qualitative intrusion detection mechanism is genuinely desirous. Several attempts, especially feature subset selection approach using Bee Algorithm (BA), Linear Genetic Programming (LGP), Support Vector Decision Function Ranking (SVDF), Rough, Rough-DPSO, and Mutivariate Regression Splines (MARS) have been advanced in the past to measure the dependability and quality of a typical IDS. The observed problem among these approaches has to do with their general performance. This has therefore motivated this research work. We hereby propose a new but robust algorithm called membrane algorithm to improve the Bee Algorithm based feature subset selection technique. This Membrane computing paradigm is a class of parallel computing devices. Data used were taken from KDD-Cup 99 Dataset which is the acceptable standard benchmark for intrusion detection. When the final results were compared to those of the existing approaches, using the three standard IDS measurements-Attack Detection, False Alarm and Classification Accuracy Rates, it was discovered that Bee Algorithm-Membrane Computing (BA-MC) approach is a better technique. This is because our approach produced very high attack detection rate of 89.11%, classification accuracy of 95.60% and also generated a reasonable decrease in false alarm rate of 0.004. Receiver Operating Characteristic (ROC) curve was used for results interpretation.

AB - Despite the great benefits accruable from the debut of computer and the internet, efforts are constantly being put up by fraudulent and mischievous individuals to compromise the integrity, confidentiality or availability of electronic information systems. In Cyber-security parlance, this is termed 'intrusion'. Hence, this has necessitated the introduction of Intrusion Detection Systems (IDS) to help detect and curb different types of attack. However, based on the high volume of data traffic involved in a network system, effects of redundant and irrelevant data should be minimized if a qualitative intrusion detection mechanism is genuinely desirous. Several attempts, especially feature subset selection approach using Bee Algorithm (BA), Linear Genetic Programming (LGP), Support Vector Decision Function Ranking (SVDF), Rough, Rough-DPSO, and Mutivariate Regression Splines (MARS) have been advanced in the past to measure the dependability and quality of a typical IDS. The observed problem among these approaches has to do with their general performance. This has therefore motivated this research work. We hereby propose a new but robust algorithm called membrane algorithm to improve the Bee Algorithm based feature subset selection technique. This Membrane computing paradigm is a class of parallel computing devices. Data used were taken from KDD-Cup 99 Dataset which is the acceptable standard benchmark for intrusion detection. When the final results were compared to those of the existing approaches, using the three standard IDS measurements-Attack Detection, False Alarm and Classification Accuracy Rates, it was discovered that Bee Algorithm-Membrane Computing (BA-MC) approach is a better technique. This is because our approach produced very high attack detection rate of 89.11%, classification accuracy of 95.60% and also generated a reasonable decrease in false alarm rate of 0.004. Receiver Operating Characteristic (ROC) curve was used for results interpretation.

KW - Bee algorithm

KW - Cybersecurity

KW - Feature selection

KW - Intrusion detection system

KW - Membrane computing

UR - http://www.scopus.com/inward/record.url?scp=84897798132&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84897798132&partnerID=8YFLogxK

U2 - 10.4304/jnw.9.3.523-529

DO - 10.4304/jnw.9.3.523-529

M3 - Article

AN - SCOPUS:84897798132

VL - 9

SP - 523

EP - 529

JO - Journal of Networks

JF - Journal of Networks

SN - 1796-2056

IS - 3

ER -