Poster Abstract: Are Android Malware Detection Models Adversarially Robust?

The popularity of android mobile phones has increased manifolds in the last few years, which has attracted many malware developers. Researchers have proposed several new-age malware detection models using machine and deep learning algorithms to strengthen the current detection engines. However, we found that these models are adversarially vulnerable, which will jeopardize their adoption in the security ecosystem. We proposed a framework where we first stepped into the attacker's shoes to design a correlation-based evasion attack and tested it against four different malware detection models. The attack exploited vulnerabilities and drastically reduced the performance of all four detection models. Later we proposed adversarial retraining as the defense strategy to counter the attacks and improve the adversarial robustness of android malware detection models.