
	Information Technology Journal
	Year: 2012 \| Volume: 11 \| Issue: 8 \| Page No.: 1048-1055 DOI: 10.3923/itj.2012.1048.1055

Fast-Flux Botnet Detection Based on Weighted SVM

Xiangzhan Yu, Bo Zhang, Le Kang and Juan Chen

Abstract: Botnet is one of the most active threats on the Internet today. Fast-flux technique is a popular way employed by botnet to evade detection. In this paper, we used data mining techniques to detect the fast-flux botnets. By analyzing the patterns of the Domain Name System (DNS) queries from the fast-flux botnets, we extract six features for constructing the weighted Support Vector Machine (SVM) in order to distinguish the normal network domain access from the fast-flux botnet domain access. The evaluation suggested that the approach is effective in detecting the fast-flux botnets.

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How to cite this article:

Xiangzhan Yu, Bo Zhang, Le Kang and Juan Chen, 2012. Fast-Flux Botnet Detection Based on Weighted SVM. Information Technology Journal, 11: 1048-1055.

DOI: 10.3923/itj.2012.1048.1055

URL: https://scialert.net/abstract/?doi=itj.2012.1048.1055

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