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arXiv · 2604.23025

Self-Supervised Learning for Android Malware Detection on a Time-Stamped Dataset

Annan Fu·Hao Pei·Maryam Tanha
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Abstract

Android malware detectors built with machine learning often suffer from temporal bias: models are trained and evaluated without respecting apps' actual release times, inflating accuracy and weakening real-world robustness. We address this by constructing a time-stamped dataset of benign and malicious Android apps and introducing a timestamp-verification procedure to ensure temporal accuracy. We then propose a detection framework that uses Bootstrap Your Own Latent (BYOL) for self-supervised pre-training to learn obfuscation-resilient representations, followed by supervised classification. Under time-aware evaluation, the method attains 98% accuracy and 89% F1. We further characterize malware behavior by analyzing true positives and false negatives using VirusTotal and the MITRE ATT&CK framework. To support reproducibility and further innovation, we release our dataset and source code.

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Annan Fu, Hao Pei, Maryam Tanha. 2026-04-24. Self-Supervised Learning for Android Malware Detection on a Time-Stamped Dataset. https://arxiv.org/abs/2604.23025

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