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

ArXi:2604.23025v1 Announce Type: cross 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