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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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2015/574589 |
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| author | P. Amudha S. Karthik S. Sivakumari |
| author_facet | P. Amudha S. Karthik S. Sivakumari |
| author_sort | P. Amudha |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6243d966632d4b0887380e6bceea0666 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-6243d966632d4b0887380e6bceea06662025-02-03T05:59:03ZengWileyThe Scientific World Journal2356-61401537-744X2015-01-01201510.1155/2015/574589574589A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant FeaturesP. Amudha0S. Karthik1S. Sivakumari2Department of CSE, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore 641 108, IndiaDepartment of CSE, SNS College of Technology, Coimbatore 641 035, IndiaDepartment of CSE, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore 641 108, IndiaIntrusion 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.http://dx.doi.org/10.1155/2015/574589 |
| spellingShingle | P. Amudha S. Karthik S. Sivakumari A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features The Scientific World Journal |
| title | A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features |
| title_full | A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features |
| title_fullStr | A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features |
| title_full_unstemmed | A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features |
| title_short | A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features |
| title_sort | hybrid swarm intelligence algorithm for intrusion detection using significant features |
| url | http://dx.doi.org/10.1155/2015/574589 |
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