The effectiveness of cost sensitive machine learning algorithms in classifying Zeus flows
by Ahmad Azab
International Journal of Information and Computer Security (IJICS), Vol. 17, No. 3/4, 2022

Abstract: Zeus botnet is regarded as one of the primary sources of financial losses for both individuals and organisations. Therefore, the identification of its command and control (C&C) network traffic has become an important research field. Although the literature provided machine learning and other solutions for C&C identification, they suffer from various drawbacks. In this paper, we provide a framework that bridges the gap in terms of the machine learning solution, by building a classifier to detect the untrained version of Zeus botnet C&C traffic. The framework showed efficacy in detecting a new version of Zeus botnet, by building the classifier on an older version, compared to the machine learning approach used in the current research.

Online publication date: Fri, 22-Apr-2022

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