Botnet Detection Using Support Vector Machines with Artificial Fish Swarm Algorithm
Because of the advances in Internet technology, the applications of the Internet of Things have become a crucial topic. The number of mobile devices used globally substantially increases daily; therefore, information security concerns are increasingly vital. The botnet virus is a major threat to bot...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/986428 |
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| _version_ | 1850166474483695616 |
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| author | Kuan-Cheng Lin Sih-Yang Chen Jason C. Hung |
| author_facet | Kuan-Cheng Lin Sih-Yang Chen Jason C. Hung |
| author_sort | Kuan-Cheng Lin |
| collection | DOAJ |
| description | Because of the advances in Internet technology, the applications of the Internet of Things have become a crucial topic. The number of mobile devices used globally substantially increases daily; therefore, information security concerns are increasingly vital. The botnet virus is a major threat to both personal computers and mobile devices; therefore, a method of botnet feature characterization is proposed in this study. The proposed method is a classified model in which an artificial fish swarm algorithm and a support vector machine are combined. A LAN environment with several computers which has infected by the botnet virus was simulated for testing this model; the packet data of network flow was also collected. The proposed method was used to identify the critical features that determine the pattern of botnet. The experimental results indicated that the method can be used for identifying the essential botnet features and that the performance of the proposed method was superior to that of genetic algorithms. |
| format | Article |
| id | doaj-art-da605972cef34fe39de72c8d763576b7 |
| institution | OA Journals |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-da605972cef34fe39de72c8d763576b72025-08-20T02:21:25ZengWileyJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/986428986428Botnet Detection Using Support Vector Machines with Artificial Fish Swarm AlgorithmKuan-Cheng Lin0Sih-Yang Chen1Jason C. Hung2Department of Management Information Systems, National Chung Hsing University, Taichung 40227, TaiwanDepartment of Management Information Systems, National Chung Hsing University, Taichung 40227, TaiwanDepartment of Information Management, Overseas Chinese University, Taichung 40721, TaiwanBecause of the advances in Internet technology, the applications of the Internet of Things have become a crucial topic. The number of mobile devices used globally substantially increases daily; therefore, information security concerns are increasingly vital. The botnet virus is a major threat to both personal computers and mobile devices; therefore, a method of botnet feature characterization is proposed in this study. The proposed method is a classified model in which an artificial fish swarm algorithm and a support vector machine are combined. A LAN environment with several computers which has infected by the botnet virus was simulated for testing this model; the packet data of network flow was also collected. The proposed method was used to identify the critical features that determine the pattern of botnet. The experimental results indicated that the method can be used for identifying the essential botnet features and that the performance of the proposed method was superior to that of genetic algorithms.http://dx.doi.org/10.1155/2014/986428 |
| spellingShingle | Kuan-Cheng Lin Sih-Yang Chen Jason C. Hung Botnet Detection Using Support Vector Machines with Artificial Fish Swarm Algorithm Journal of Applied Mathematics |
| title | Botnet Detection Using Support Vector Machines with Artificial Fish Swarm Algorithm |
| title_full | Botnet Detection Using Support Vector Machines with Artificial Fish Swarm Algorithm |
| title_fullStr | Botnet Detection Using Support Vector Machines with Artificial Fish Swarm Algorithm |
| title_full_unstemmed | Botnet Detection Using Support Vector Machines with Artificial Fish Swarm Algorithm |
| title_short | Botnet Detection Using Support Vector Machines with Artificial Fish Swarm Algorithm |
| title_sort | botnet detection using support vector machines with artificial fish swarm algorithm |
| url | http://dx.doi.org/10.1155/2014/986428 |
| work_keys_str_mv | AT kuanchenglin botnetdetectionusingsupportvectormachineswithartificialfishswarmalgorithm AT sihyangchen botnetdetectionusingsupportvectormachineswithartificialfishswarmalgorithm AT jasonchung botnetdetectionusingsupportvectormachineswithartificialfishswarmalgorithm |