An Adversarial Machine Learning Approach to Evaluate the Robustness of a Security Solution

Cyber-Security industry has always been a "cat and a mouse" game - whenever a new technology was developed it was shortly followed by the appearance of several techniques used by malware creators to avoid detection. It is no surprise that the developing of adversarial machine learning algorithms has provided a tool that can be used to avoid machine learning based detection mechanisms available in security products. This paper presents how the same algorithms can also be used to strengthen a security solution by identifying its weak points / features. We will also provide a method that can be used to fight Generative Adversarial Networks (GANs) with GANs, that is effective when a malware writer is using these methods to avoid detection.