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Android Zero-Day Guard: Zero-Shot Malware Detection Using Deep Learning and Generative Models
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DOI：10.1109/TNSM.2026.3671305.png)
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Abstract
En 中文
This paper proposes an Android-oriented zero-day malware detection method named Android Zero-Day Guard. By integrating deep neural networks with zero-shot learning, this approach is capable of identifying emerging threats without prior exposure to malicious samples. The method converts APK files into images and extracts deep features, enabling effective capture of behavioral malware patterns. Experimental results demonstrate that the proposed method achieves a precision of 94.93%, a recall of 93.75%, and an F1-score of 94.28% across multiple malware families. Without relying on dynamic analysis, it exhibits strong detection capability and generalization performance, making it well-suited for the early identification of emerging threats. While the model performs strongly on benchmark datasets, continuous validation on the latest families is essential for deployment in a rapidly evolving threat landscape.
Keywords:
Malware
Feature extraction
Accuracy
Zero shot learning
Smart phones
Generative adversarial networks
Computational modeling
Data models
Convolutional neural networks
Application programming interfaces
Android zero-day malware
zero-shot learning
Wasserstein generative adversarial network
malware detection
Journal
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5.4
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618
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9.2K
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