Domain Adaptation-Based Deep Learning Framework for Android Malware Detection Across Diverse Distributions

Abstract

This study addresses the challenge of Android malware detection, a critical issue due to the pervasive threats affecting mobile devices. As Android malware evolves, conventional detection methods struggle with novel or polymorphic malware that bypasses traditional defenses. This research leverages machine learning (ML) and deep learning (DL) techniques to overcome these limitations by adopting domain adaptation strategies that enhance model generalization across different distributions. The approach involves dividing a dataset into distinct distributions and applying domain adaptation techniques to ensure robustness and accuracy despite distribution shifts. Preliminary results demonstrate that domain adaptation significantly improves detection accuracy in target domains not represented in the training data. This paper showcases a domain adaptation-based method for Android malware detection, illustrating its potential to enhance security measures in dynamic environments. The findings suggest that integrating advanced ML and DL strategies with domain adaptation can substantially improve the efficacy of malware detection systems.