PhD forum abstract: Designing adversarial robust and explainable malware detection system for android based smartphones

Abstract

Android smartphones and malware have grown exponentially in the last decade. Literature suggests that the current malware detection systems cannot cope with the present security challenges. Thus researchers are developing next-generation malware detection systems/models using the machine and deep learning. However, the proposed systems/models have poor explainability and are vulnerable against adversarial attacks, which will jeopardize their adoption in the future security ecosystem. Thus, we aim to construct adversarial robust malware detection models by first acting as an adversary to find vulnerabilities in models and then proposing preventive countermeasures. We also aim to improve models' explain-ability to win security community confidence before real-world implementation.