Machine Learning For HTTP Botnet Detection Using Classifier Algorithms

• Main Authors: Mohd Dollah, Rudy Fadhlee, Abd Majid, Mohd Faizal, Arif, Fahmi, Mas'ud, Mohd Zaki, Lee, Kher Xin
• Format: Article
• Language: English
• Published: Penerbit Universiti,UTeM 2018
• Subjects: Q Science (General); QA Mathematics
• Online Access: http://eprints.utem.edu.my/id/eprint/21838/2/3591-9615-1-SM.pdf; http://eprints.utem.edu.my/id/eprint/21838/; http://journal.utem.edu.my/index.php/jtec/article/view/3591
• Institution: Universiti Teknikal Malaysia Melaka

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%.