An Enhanced Sine Cosine Algorithm for Feature Selection in Network Intrusion Detection
For computer networks to remain secure, intrusion detection is essential. Analyzing network traffic data is part of this activity to spot possible cyber threats. However, the curse of dimensionality presents a challenge because there are so many dimensions in the data. To overcome this challenge, fe...
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Ferdowsi University of Mashhad
2024-12-01
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| Series: | Computer and Knowledge Engineering |
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| Online Access: | https://cke.um.ac.ir/article_45730_ddd55b2e670189f6bfc5dd00f0b13e67.pdf |
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| author | zahra asgari varzaneh soodeh hosseini |
| author_facet | zahra asgari varzaneh soodeh hosseini |
| author_sort | zahra asgari varzaneh |
| collection | DOAJ |
| description | For computer networks to remain secure, intrusion detection is essential. Analyzing network traffic data is part of this activity to spot possible cyber threats. However, the curse of dimensionality presents a challenge because there are so many dimensions in the data. To overcome this challenge, feature selection is essential to creating a successful intrusion detection system. It involves removing irrelevant and redundant features, which enhances the classification model's accuracy and lowers the dimensionality of the feature space. Metaheuristic algorithms are optimization techniques inspired by nature and are well-suited to choose features for network intrusion detection. They are effective in exploring large search spaces and have been widely used for this purpose. In this study, we improve the Sine Cosine Algorithm named ISCA for feature selection by introducing a controlling parameter to balance exploration and exploitation. Based on the NSL-KDD dataset, the results show that compared to other competing algorithms, the ISCA performs better than other metaheuristic algorithms in terms of both the number of features selected and the accuracy of classification. |
| format | Article |
| id | doaj-art-7fa40c534f994fafbc7deb336e14f9cd |
| institution | Kabale University |
| issn | 2538-5453 2717-4123 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Ferdowsi University of Mashhad |
| record_format | Article |
| series | Computer and Knowledge Engineering |
| spelling | doaj-art-7fa40c534f994fafbc7deb336e14f9cd2025-01-19T04:04:23ZengFerdowsi University of MashhadComputer and Knowledge Engineering2538-54532717-41232024-12-0172172610.22067/cke.2024.83460.109745730An Enhanced Sine Cosine Algorithm for Feature Selection in Network Intrusion Detectionzahra asgari varzaneh0soodeh hosseini1Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran,Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran.For computer networks to remain secure, intrusion detection is essential. Analyzing network traffic data is part of this activity to spot possible cyber threats. However, the curse of dimensionality presents a challenge because there are so many dimensions in the data. To overcome this challenge, feature selection is essential to creating a successful intrusion detection system. It involves removing irrelevant and redundant features, which enhances the classification model's accuracy and lowers the dimensionality of the feature space. Metaheuristic algorithms are optimization techniques inspired by nature and are well-suited to choose features for network intrusion detection. They are effective in exploring large search spaces and have been widely used for this purpose. In this study, we improve the Sine Cosine Algorithm named ISCA for feature selection by introducing a controlling parameter to balance exploration and exploitation. Based on the NSL-KDD dataset, the results show that compared to other competing algorithms, the ISCA performs better than other metaheuristic algorithms in terms of both the number of features selected and the accuracy of classification.https://cke.um.ac.ir/article_45730_ddd55b2e670189f6bfc5dd00f0b13e67.pdfsine cosine algorithmmeta-heuristic algorithmsfeature selectionnetwork intrusion detection |
| spellingShingle | zahra asgari varzaneh soodeh hosseini An Enhanced Sine Cosine Algorithm for Feature Selection in Network Intrusion Detection Computer and Knowledge Engineering sine cosine algorithm meta-heuristic algorithms feature selection network intrusion detection |
| title | An Enhanced Sine Cosine Algorithm for Feature Selection in Network Intrusion Detection |
| title_full | An Enhanced Sine Cosine Algorithm for Feature Selection in Network Intrusion Detection |
| title_fullStr | An Enhanced Sine Cosine Algorithm for Feature Selection in Network Intrusion Detection |
| title_full_unstemmed | An Enhanced Sine Cosine Algorithm for Feature Selection in Network Intrusion Detection |
| title_short | An Enhanced Sine Cosine Algorithm for Feature Selection in Network Intrusion Detection |
| title_sort | enhanced sine cosine algorithm for feature selection in network intrusion detection |
| topic | sine cosine algorithm meta-heuristic algorithms feature selection network intrusion detection |
| url | https://cke.um.ac.ir/article_45730_ddd55b2e670189f6bfc5dd00f0b13e67.pdf |
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