Adversarial Patterns: Building Robust Android Malware Classifiers

Deep learning-based classifiers have substantially improved recognition of malware samples. However, these classifiers can be vulnerable to adversarial input perturbations. Any vulnerability in malware classifiers poses significant threats to the platforms they defend. Therefore, to create stronger defense models against malware, we must understand the patterns in input perturbations caused by an adversary. This survey paper presents a comprehensive study on adversarial machine learning for android malware classifiers. We first present an extensive background in building a machine learning classifier for android malware, covering both image-based and text-based feature extraction approaches. Then, we examine the pattern and advancements in the state-of-the-art research in evasion attacks and defenses. Finally, we present guidelines for designing robust malware classifiers and enlist research directions for the future.