SDN-GAN: Generative Adversarial Deep NNs for Synthesizing Cyber Attacks on Software Defined Networks

The recent evolution in programmable networks such as SDN opens the possibility to control networks using software controllers. However, such networks are vulnerable to attacks that occur in traditional networks. Several techniques are proposed to handle the security vulnerabilities in SDNs. However, it is challenging to create attack signatures, scenarios, or even intrusion detection rules that are applicable to SDN dynamic environments. Generative Adversarial Deep Neural Networks automates the generation of realistic data in a semi supervised manner. This paper describes an approach that generates synthetic attacks that can target SDNs. It can be used to train SDNs to detect different attack variations. It is based on the most recent OpenFlow models/algorithms and it utilizes similarity with known attack patterns to identify attacks. Such synthesized variations of attack signatures are shown to attack SDNs using adversarial approaches.