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A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features.

Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm i...

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Published in:Scientific World Journal Vol. 2015; pp. 574589 - 574590
Main Authors: Amudha, P, Karthik, S, Sivakumari, S
Format: Journal Article
Published: Wiley-Blackwell 1/1/2015
Online Access:View this record in EBSCOhost
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      dt: 1/1/2015
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      pub: Wiley-Blackwell
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        atl: A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features.
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        au:
          Amudha, P
          Karthik, S
          Sivakumari, S
        affil: Department of CSE, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore 641 108, India.
      sug:
      ab: Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm is proposed to integrate Modified Artificial Bee Colony (MABC) with Enhanced Particle Swarm Optimization (EPSO) to predict the intrusion detection problem. The algorithms are combined together to find out better optimization results and the classification accuracies are obtained by 10-fold cross-validation method. The purpose of this paper is to select the most relevant features that can represent the pattern of the network traffic and test its effect on the success of the proposed hybrid classification algorithm. To investigate the performance of the proposed method, intrusion detection KDDCup'99 benchmark dataset from the UCI Machine Learning repository is used. The performance of the proposed method is compared with the other machine learning algorithms and found to be significantly different.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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