Machine Learning For HTTP Botnet Detection Using Classifier Algorithms

Mohd Dollah, Rudy Fadhlee and Abd Majid, Mohd Faizal and Arif, Fahmi and Mas'ud, Mohd Zaki and Lee, Kher Xin (2018) Machine Learning For HTTP Botnet Detection Using Classifier Algorithms. Journal Of Telecommunication, Electronic And Computer Engineering (JTEC) , 10. pp. 27-30. ISSN 2180-1843

[img] Text
3591-9615-1-SM.pdf - Published Version

Download (347kB)

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%.

Item Type: Article
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
Divisions: Faculty of Information and Communication Technology
Depositing User: Mohd. Nazir Taib
Date Deposited: 27 May 2019 02:52
Last Modified: 26 Jun 2021 23:59
URI: http://eprints.utem.edu.my/id/eprint/21838
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item