Journal of Applied Mathematics

  • J. Appl. Math.
  • Volume 2014, Special Issue (2014), Article ID 986428, 9 pages.

Botnet Detection Using Support Vector Machines with Artificial Fish Swarm Algorithm

Kuan-Cheng Lin, Sih-Yang Chen, and Jason C. Hung

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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.

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J. Appl. Math., Volume 2014, Special Issue (2014), Article ID 986428, 9 pages.

First available in Project Euclid: 1 October 2014

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Lin, Kuan-Cheng; Chen, Sih-Yang; Hung, Jason C. Botnet Detection Using Support Vector Machines with Artificial Fish Swarm Algorithm. J. Appl. Math. 2014, Special Issue (2014), Article ID 986428, 9 pages. doi:10.1155/2014/986428.

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