DeepImageDroid: A Hybrid Framework Leveraging Visual Transformers and Convolutional Neural Networks for Robust Android Malware Detection

Abstract

As the leading mobile operating system, Android powers critical infrastructure and personal devices across sectors such as finance and healthcare and a wide range of user scenarios. However, its open-source nature presents considerable security challenges. Exploiting vulnerabilities in native and custom permissions, malicious API calls, intents, signatures, and manifests, threat actors can gain access to sensitive data and device control. The continuously evolving landscape of Android malware necessitates robust and generalized detection methods. While traditional machine learning (ML) models have been employed to address this issue, they have limitations. Focusing on single datasets can impede both generalization and effective detection. Further, the dynamic nature of malware renders many traditional methods inadequate for providing comprehensive and real-time protection. This paper addresses this critical need by proposing DeepImageDroid, an advanced and efficient deep learning (DL) framework for Android malware detection. DeepImageDroid harnesses the combined power of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), utilizing three diverse Android malware datasets. This hybrid approach significantly improves detection accuracy and model generalization compared to existing solutions. By employing the weighted average ensemble method, DeepImageDroid achieved a remarkable 96% accuracy.

Keywords

• Android
• CNN
• deep learning
• ensemble
• hybrid
• malware