Kazi, Mohamed ORCID: https://orcid.org/0000-0001-5105-3581, Woodhead, Steve and Gan, Diane ORCID: https://orcid.org/0000-0002-0920-7572 (2020) Comparing and analysing binary classification algorithms when used to detect the Zeus malware. In: 2019 Sixth HCT Information Technology Trends (ITT). Ras Al Khaimah, United Arab Emirates, 20-21 Nov. 2019. Information Technology Trends (ITT) . Institute of Electrical and Electronics Engineers (IEEE), Piscataway, New Jersey, pp. 6-11. ISBN 978-1728150611; 978-1728150628 (doi:10.1109/ITT48889.2019.9075115)

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Abstract

The Zeus banking malware is one of the most prolific banking malware variants ever to be discovered. This paper examines and analyses the Support Vector Machine (SVM), Decision Tree and Random Forest machine learning algorithms when used in conjunction with a manual feature selection process to detect Zeus network traffic. Selecting the features manually provides the researcher with more control over which features that can and should be selected. The manual feature selection process will also allow the researcher to analyze the impact of the various features and then select the features that provide the best accuracy results during the classification and detection of Zeus. The algorithms in scope for this research are the Decision Tree algorithm, Random Forest algorithm and the SVM algorithm.

Item Type:	Conference Proceedings
Title of Proceedings:	2019 Sixth HCT Information Technology Trends (ITT). Ras Al Khaimah, United Arab Emirates, 20-21 Nov. 2019
Uncontrolled Keywords:	Zeus banking malware; detecting banking malware using binary classification algorithms; decision tree algorithm; random forest algorithm; SVM algorithm; manual feature selection
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Engineering (ENG)
Last Modified:	10 Oct 2022 11:51
URI:	http://gala.gre.ac.uk/id/eprint/37743

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