Botnet traffic discriminatory analysis using particle swarm optimization

Authors:
Yan Zhang;Shuguang Huang;Yongyi Wang;Min Zhang
Affiliations:
Electronic Engineering Institute, Hefei, China;Electronic Engineering Institute, Hefei, China;Electronic Engineering Institute, Hefei, China;Electronic Engineering Institute, Hefei, China
Venue:
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Year:
2010

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Abstract

Particle Swarm Optimization are inherently distributed algorithms where the solution for a problem emerges from the interactions between many simple individual agents called particles. This article proposes the use of the Particle Swarm Optimization as a new tool for botnet traffic discriminatory analysis. Through this novel approach, we classify the C&C session, which functions as the unique characteristic of the bots, from the complicated background traffic data so as to identify the compromised computers. Experimental results show that the proposed approach perform a high accuracy in the identification of the C&C session.