VOLTRON: Detecting Unknown Malware Using Graph-Based Zero-Shot Learning
This addresses the security challenge for millions of Android users by improving detection of emerging malware, though it is incremental as it builds on existing graph-based and zero-shot learning methods.
The paper tackles the problem of detecting unknown Android malware families without labeled data by introducing a zero-shot learning framework that combines Variational Graph Auto-Encoders and Siamese Neural Networks, achieving 96.24% accuracy and 95.20% recall for unknown malware families.
The persistent threat of Android malware presents a serious challenge to the security of millions of users globally. While many machine learning-based methods have been developed to detect these threats, their reliance on large labeled datasets limits their effectiveness against emerging, previously unseen malware families, for which labeled data is scarce or nonexistent. To address this challenge, we introduce a novel zero-shot learning framework that combines Variational Graph Auto-Encoders (VGAE) with Siamese Neural Networks (SNN) to identify malware without needing prior examples of specific malware families. Our approach leverages graph-based representations of Android applications, enabling the model to detect subtle structural differences between benign and malicious software, even in the absence of labeled data for new threats. Experimental results show that our method outperforms the state-of-the-art MaMaDroid, especially in zero-day malware detection. Our model achieves 96.24% accuracy and 95.20% recall for unknown malware families, highlighting its robustness against evolving Android threats.