arXiv Machine Learning June 8, 2026 By Annan Fu, Hao Pei, Maryam Tanha

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

Read the original on arXiv Machine Learning →

arXiv:2604. 23025v2 Announce Type: replace-cross 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection

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MASCOT-Android: A Curated Dataset and Automated Collection Pipeline for Android Malware Source Code Specimens

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