Automated Botnet Traffic Detection via Machine Learning

Automated Botnet Traffic Detection via Machine Learning
Title:
Automated Botnet Traffic Detection via Machine Learning
Other Titles:
2018 IEEE Region 10 Conference (TENCON)
Keywords:
Publication Date:
28 October 2018
Citation:
F. K. Wai, Z. Lilei, W. K. Wai, S. Le and V. L. L. Thing, "Automated Botnet Traffic Detection via Machine Learning," TENCON 2018 - 2018 IEEE Region 10 Conference, Jeju, Korea (South), 2018, pp. 0038-0043. doi: 10.1109/TENCON.2018.8650466
Abstract:
Connected machines become more vulnerable to malware infections which potentially cause them to be controlled as part of a botnet for cybercrime activities. Prompt detection of infected machines is required for protecting local networks and infrastructure as well as reducing the impact of botnets. In this paper, we propose the use of machine learning techniques involving multi-layer perceptrons and decision trees on network traffic analysis for the detection of botnet traffic. We enhance components of an existing detection framework with these techniques to automate its processes and improve performance at the same time. Our experiments indicate that the modifications successfully improved the overall performance of botnet traffic detection in both supervised and semi-supervised manners.
License type:
PublisherCopyrights
Funding Info:
Description:
(c) 2018 IEEE.
ISSN:
2159-3450
2159-3442
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