Fighting Fire with Fire: Continuous Attack for Adversarial Android Malware Detection

The pervasive adoption of Android as the leading operating system, due to its open-source nature, has simultaneously rendered it a prime target for malicious software attacks. In response, various learning-based Android malware detectors (AMDs) have been developed, achieving notable success in malware identification. However, these detectors are increasingly compromised by adversarial examples (AEs), which are subtly modified inputs designed to evade detection while maintaining malicious functionality. Recently, advanced adversarial example generation tools have been introduced that can reduce the efficacy of popular detectors to 1%. In this background, to address the critical need for more resilient AMDs, we propose a novel defense mechanism, Harnessing Attack Generativity for Defense Enhancement, i.e., HagDe. HagDe involves applying iterative perturbations in the direction of gradient ascent to all samples, aiming to exploit the high sensitivity of AEs to perturbations. This method enables the detection of adversarial samples by observing the disproportionate increase in the loss function following minor perturbations, distinguishing them from regular samples. To evaluate HagDe, we conduct an extensive evaluation on 15,000 samples and 15 different attack patterns. Results show that HagDe can achieve a defense effectiveness of 88.5% on AdvDroidZero and 90.7% on BagAmmo, representing an increase of 32.45% and 11.28%, respectively, compared to the latest defense method KD_BU and LID.