研究人员对安卓恶意软件检测系统十年的对抗性鲁棒性进行了纵向研究。他们的发现表明，随着时间的推移数据分布发生变化的“概念漂移”会显著降低这些系统的对抗性鲁棒性。尽管使用累积历史数据重新训练模型可以缓解部分损失，但并不能完全消除问题，这凸显了对“漂移感知”评估框架的需求。 AI
影响 强调了长周期对抗性AI系统需要进行“漂移感知”鲁棒性评估，影响安全AI开发者。
排序理由 该聚类包含一篇学术论文，详细介绍了安卓恶意软件检测中对抗性漏洞和概念漂移的研究。
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arXiv:2605.24294v1 Announce Type: cross Abstract: Android malware detectors often degrade after deployment because of concept drift, while full retraining at each maintenance step is costly. We propose a chronological adaptive maintenance framework that models deployment-time mai…
arXiv:2605.24903v1 Announce Type: cross Abstract: Machine learning based malware detectors become obsolete over time due to concept drift in benign and malware applications. Recent methods rely on fully labeled data and use hierarchical contrastive loss (HCL) with active learning…
arXiv:2605.23623v1 Announce Type: cross Abstract: We present a longitudinal, drift-aware evaluation of adversarial robustness across more than a decade of Android applications using static and dynamic feature representations extracted from emulator and real-device executions. The…
We present a longitudinal, drift-aware evaluation of adversarial robustness across more than a decade of Android applications using static and dynamic feature representations extracted from emulator and real-device executions. The dataset is organized into yearly slices and evalu…