Botnet detection using ensemble classifiers of network flow

Zahraa M. Algelal (University Of Kufa)
Eman Abdulaziz Ghani Aldhaher (University Of Kufa)
Dalia N. Abdul-Wadood (University Of Bagdad)
Radhwan Hussein Abdulzhraa Al-Sagheer (university of kufa)

International Journal of Electrical and Computer Engineering
Vol 10, No 3: June 2020

Recently, Botnets have become a common tool for implementing and transferring various malicious codes over the Internet. These codes can be used to execute many malicious activities including DDOS attack, send spam, click fraud, and steal data. Therefore, it is necessary to use Modern technologies to reduce this phenomenon and avoid them in advance in order to differentiate the Botnets traffic from normal network traffic. In this work, ensemble classifier algorithms to identify such damaging botnet traffic. We experimented with different ensemble algorithms to compare and analyze their ability to classify the botnet traffic from the normal traffic by selecting distinguishing features of the network traffic. Botnet Detection offers a reliable and cheap style for ensuring transferring integrity and warning the risks before its occurrence.