Machine Learning for HTTP Botnet Detection Using Classifier Algorithms

Authors

  • Rudy Fadhlee Mohd Dollah Information Security, Digital Forensic, and Computer Networking (INSFORNET), Faculty of Information Technology and Communication, Universiti Teknikal Malaysia Melaka, Ayer Keroh, Melaka, Malaysia
  • Faizal M. A. Information Security, Digital Forensic, and Computer Networking (INSFORNET), Faculty of Information Technology and Communication, Universiti Teknikal Malaysia Melaka, Ayer Keroh, Melaka, Malaysia
  • Fahmi Arif Department of Industrial Engineering, Institut Teknologi Nasional (Itenas) Bandung. Indonesia
  • Mohd Zaki Mas’ud Information Security, Digital Forensic, and Computer Networking (INSFORNET), Faculty of Information Technology and Communication, Universiti Teknikal Malaysia Melaka, Ayer Keroh, Melaka, Malaysia
  • Lee Kher Xin Information Security, Digital Forensic, and Computer Networking (INSFORNET), Faculty of Information Technology and Communication, Universiti Teknikal Malaysia Melaka, Ayer Keroh, Melaka, Malaysia

Keywords:

Botnet Detection, Classification, Classifier, HTTP Botnet, Machine Learning, Malware,

Abstract

Recently, HTTP based Botnet threat has become a serious problem for computer security experts as bots can infect victim’s computer quick and stealthily. By using HTTP protocol, Bots are able to hide their communication flow within normal HTTP communications. In addition, since HTTP protocol is widely used by internet application, it is not easy to block this service as a precautionary approach. Thus, it is needed for expert finding ways to detect the HTTP Botnet in network traffic effectively. In this paper, we propose to implement machine learning classifiers, to detect HTTP Botnets. Network traffic dataset used in this research is extracted based on TCP packet feature. We also able to find the best machine learning classifier in our experiment. The proposed method is able to classify HTTP Botnet in network traffic using the best classifier in the experiment with an average accuracy of 92.93%.

Downloads

Published

2018-02-12

How to Cite

Mohd Dollah, R. F., M. A., F., Arif, F., Mas’ud, M. Z., & Xin, L. K. (2018). Machine Learning for HTTP Botnet Detection Using Classifier Algorithms. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-7), 27–30. Retrieved from https://jtec.utem.edu.my/jtec/article/view/3591

Issue

Vol. 10 No. 1-7: Innovative and Sustainable Technologies for Societal Wellbeing

Section

Articles

License

TRANSFER OF COPYRIGHT AGREEMENT

The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):

  1. All proprietary right other than copyright, such as patent rights
  2. The right to make further copies of all or part of the published article for my use in classroom teaching
  3. The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
  4. The right to make copies of the published work for internal distribution within the institution that employs me

I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.