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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 1, 2020.
Abstract: Botnet employs advanced evasion techniques to avoid detection. One of the Botnet evasion techniques is by hiding their command and control communication over an encrypted channel like SSL and TLS. This paper provides a Botnet Analysis and Detection System (BADS) framework for detecting Botnet. The BADS framework has been used as a guideline to devise the methodology, and we divided this methodology into six phases: i. data collection, customization, and conversion, ii. feature extraction and feature selection, iii. Botnet prediction and classification, iv. Botnet detection, v. attack notification, and vi. testing and evaluation. We tend to use the machine learning algorithm for Botnet prediction and classification. We also found several challenges in implementing this work. This research aims to detect Botnet over an encrypted channel with high accuracy, fast detection time, and provides autonomous management to the network manager.
Zahian Ismail, Aman Jantan and Mohd. Najwadi Yusoff, “A Framework for Detecting Botnet Command and Control Communication over an Encrypted Channel” International Journal of Advanced Computer Science and Applications(IJACSA), 11(1), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110140
@article{Ismail2020,
title = {A Framework for Detecting Botnet Command and Control Communication over an Encrypted Channel},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110140},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110140},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {1},
author = {Zahian Ismail and Aman Jantan and Mohd. Najwadi Yusoff}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.