An enhancing framework for botnet detection using generative adversarial networks

Abstract

The botnet, as one of the most formidable threats to cyber security, is often used to launch large-scale attack sabotage. How to accurately identify the botnet, especially to improve the performance of the detection model, is a key technical issue. In this paper, we propose a framework based on generative adversarial networks to augment botnet detection models (Bot-GAN). Moreover, we explore the performance of the proposed framework based on flows. The experimental results show that Bot-GAN is suitable for augmenting the original detection model. Compared with the original detection model, the proposed approach improves the detection performance, and decreases the false positive rate, which provides an effective method for improving the detection performance. In addition, it also retains the primary characteristics of the original detection model, which does not care about the network payload information, and has the ability to detect novel botnets and others using encryption or proprietary protocols.