DomainGAN: Generating Adversarial Examples to Attack Domain Generation Algorithm Classifiers

Domain Generation Algorithms (DGAs) are frequently used to generate large numbers of domains for use by botnets. These domains are often used as rendezvous points for the servers that malware has command and control over. There are many algorithms that are used to generate domains, but many of these algorithms are simplistic and are very easy to detect using classical machine learning techniques. In this paper, three different variants of generative adversarial networks (GANs) are used to improve domain generation by making the domains more difficult for machine learning algorithms to detect. The domains generated by traditional DGAs and the GAN based DGA are then compared by using state of the art machine learning based DGA classifiers.