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.

翻译：基于机器学习构建的安卓恶意软件检测器常受时间偏差影响：模型训练与评估未遵循应用实际发布时间，导致准确率虚高且削弱实际鲁棒性。为应对此问题，我们构建了包含良性应用与恶意应用的时间戳数据集，并提出时间戳验证流程确保时序准确性。继而提出基于Bootstrap Your Own Latent（BYOL）自监督预训练的检测框架，学习抗混淆攻击的特征表示，随后进行监督分类。在时间感知评估下，该方法达到98%准确率与89%F1分数。我们进一步利用VirusTotal与MITRE ATT&CK框架对真阳性与假阴性样本进行分析，刻画恶意软件行为特征。为支持可复现性与后续创新，我们公开了数据集与源代码。