Deep learning-based classification model for botnet attack detection

Abstract

Botnets are vectors through which hackers can seize control of multiple systems and conduct malicious activities. Researchers have proposed multiple solutions to detect and identify botnets in real time. However, these proposed solutions have difficulties in keeping pace with the rapid evolution of botnets. This paper proposes a model for detecting botnets using deep learning to identify zero-day botnet attacks in real time. The proposed model is trained and evaluated on a CTU-13 dataset with multiple neural network designs and hidden layers. Results demonstrate that the deep-learning artificial neural network model can accurately and efficiently identify botnets.