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Journal of Applied Mathematics
Volume 2014, Article ID 986428, 9 pages
Research Article

Botnet Detection Using Support Vector Machines with Artificial Fish Swarm Algorithm

1Department of Management Information Systems, National Chung Hsing University, Taichung 40227, Taiwan
2Department of Information Management, Overseas Chinese University, Taichung 40721, Taiwan

Received 21 January 2014; Accepted 4 March 2014; Published 29 April 2014

Academic Editor: Young-Sik Jeong

Copyright © 2014 Kuan-Cheng Lin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.