Feature Selection Using Particle Swarm Optimization in Intrusion Detection
The prevention of intrusion in networks is decisive and an intrusion detection system is extremely desirable with potent intrusion detection mechanism. Excessive work is done on intrusion detection systems but still these are not powerful due to high number of false alarms. One of the leading causes...
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| Format: | Article |
| Language: | English |
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Wiley
2015-10-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/806954 |
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| _version_ | 1832547214059110400 |
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| author | Iftikhar Ahmad |
| author_facet | Iftikhar Ahmad |
| author_sort | Iftikhar Ahmad |
| collection | DOAJ |
| description | The prevention of intrusion in networks is decisive and an intrusion detection system is extremely desirable with potent intrusion detection mechanism. Excessive work is done on intrusion detection systems but still these are not powerful due to high number of false alarms. One of the leading causes of false alarms is due to the usage of a raw dataset that contains redundancy. To resolve this issue, feature selection is necessary which can improve intrusion detection performance. Latterly, principal component analysis (PCA) has been used for feature reduction and subset selection in which features are primarily projected into a principal space and then features are elected based on their eigenvalues, but the features with the highest eigenvalues may not have the guaranty to provide optimal sensitivity for the classifier. To avoid this problem, an optimization method is required. Evolutionary optimization approach like genetic algorithm (GA) has been used to search the most discriminative subset of transformed features. The particle swarm optimization (PSO) is another optimization approach based on the behavioral study of animals/birds. Therefore, in this paper a feature subset selection based on PSO is proposed which provides better performance as compared to GA. |
| format | Article |
| id | doaj-art-4303ba4dc6ab4456b22e574aead0a794 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2015-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-4303ba4dc6ab4456b22e574aead0a7942025-02-03T06:45:33ZengWileyInternational Journal of Distributed Sensor Networks1550-14772015-10-011110.1155/2015/806954806954Feature Selection Using Particle Swarm Optimization in Intrusion DetectionIftikhar AhmadThe prevention of intrusion in networks is decisive and an intrusion detection system is extremely desirable with potent intrusion detection mechanism. Excessive work is done on intrusion detection systems but still these are not powerful due to high number of false alarms. One of the leading causes of false alarms is due to the usage of a raw dataset that contains redundancy. To resolve this issue, feature selection is necessary which can improve intrusion detection performance. Latterly, principal component analysis (PCA) has been used for feature reduction and subset selection in which features are primarily projected into a principal space and then features are elected based on their eigenvalues, but the features with the highest eigenvalues may not have the guaranty to provide optimal sensitivity for the classifier. To avoid this problem, an optimization method is required. Evolutionary optimization approach like genetic algorithm (GA) has been used to search the most discriminative subset of transformed features. The particle swarm optimization (PSO) is another optimization approach based on the behavioral study of animals/birds. Therefore, in this paper a feature subset selection based on PSO is proposed which provides better performance as compared to GA.https://doi.org/10.1155/2015/806954 |
| spellingShingle | Iftikhar Ahmad Feature Selection Using Particle Swarm Optimization in Intrusion Detection International Journal of Distributed Sensor Networks |
| title | Feature Selection Using Particle Swarm Optimization in Intrusion Detection |
| title_full | Feature Selection Using Particle Swarm Optimization in Intrusion Detection |
| title_fullStr | Feature Selection Using Particle Swarm Optimization in Intrusion Detection |
| title_full_unstemmed | Feature Selection Using Particle Swarm Optimization in Intrusion Detection |
| title_short | Feature Selection Using Particle Swarm Optimization in Intrusion Detection |
| title_sort | feature selection using particle swarm optimization in intrusion detection |
| url | https://doi.org/10.1155/2015/806954 |
| work_keys_str_mv | AT iftikharahmad featureselectionusingparticleswarmoptimizationinintrusiondetection |