Multimodal and Explainable Android Adware Classification Techniques

Abstract

With the widespread availability of adware masquerading as useful apps, there is an increasing need for robust security measures to identify adware. The identification of adware as a malware is a challenging task, as it often appears benign despite its malicious intent in the background. In this study, we propose an approach to classify adware on Android devices using data from multiple modalities. The focus is particularly on the classification of Airpush and Dowgin adware. Our proposed method uses both tabular and grayscale image data, and a feedforward neural network architecture to build a multimodal deep learning model that achieves a 95% prediction accuracy. Additionally, we incorporate Explainable AI (XAI) to enhance the interpretability of the results. The efficiency of our proposed approach is showcased through its ability to classify adware instances in an explainable manner, highlighting its significance not only in adware classification but also in fortifying against the evolving challenges posed by adware.