GAN-based Approach to Crafting Adversarial Malware Examples against a Heterogeneous Ensemble Classifier

Abstract

The rapid advances in machine learning and deep learning algorithms have led to their adoption to tackle different security problems such as spam, intrusion, and malware detection. Malware is a type of software developed with a malicious intent to damage, exploit, or disable devices, systems, or networks. Malware authors typically operate through black-box sitting when they have a partial knowledge about the targeted detection system. It has been shown that supervised machine learning models are vulnerable to well-crafted adversarial examples. The application domain of malware classification introduces additional constraints in the adversarial sample crafting process compared to the computer vision domain: (1) the input is binary and (2) retaining the visual appearance of the malware application and its intended functionality. In this paper, we have developed a heterogeneous ensemble classifier that combines supervised and unsupervised models to hinder black-box attacks designed by two variants of generative adversarial network (GAN). We experimentally validate its soundness on a corpus of malware and legitimate files.