Botnet Detection Using a Feed-Forward Backpropagation Artificial Neural Network

• Main Author: Ahmed, Abdulghani Ali
• Format: Conference or Workshop Item
• Language: English
• Published: Springer 2019
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://umpir.ump.edu.my/id/eprint/22501/1/Botnet%20Detection%20Using%20a%20Feed-Forward1.pdf; http://umpir.ump.edu.my/id/eprint/22501/; https://doi.org/10.1007/978-3-030-03302-6_3
• Institution: Universiti Malaysia Pahang Al-Sultan Abdullah

Botnet represent a critical threat to computer networks because their behavior allows hackers to take control of many computers simultaneously. Botnets take over the device of their victim and performs malicious activities on its system. Although many solutions have been developed to address the detection of Botnet in real time, these solutions are still prone to several problems that may critically affect the efficiency and capability of identifying and preventing Botnet attacks. The current work proposes a technique to detect Botnet attacks using a feed-forward backpropagation artificial neural network. The proposed technique aims to detect Botnet zero-day attack in real time. This technique applies a backpropagation algorithm to the CTU-13 dataset to train and evaluate the Botnet detection classifier. It is implemented and tested in various neural network designs with different hidden layers. Results demonstrate that the proposed technique is promising in terms of accuracy and efficiency of Botnet detection.