时间概念漂移下的对抗脆弱性：Android恶意软件检测的纵向研究
Adversarial Vulnerability Under Temporal Concept Drift: A Longitudinal Study of Android Malware Detection
AI总结：
通过纵向评估十年间Android应用的静态和动态特征，研究时间分布漂移对对抗鲁棒性的影响，提出时序关联指标并发现时间分离降低鲁棒性，扩展窗口重训练可缓解但无法消除鲁棒性损失。
摘要：
我们提出了一种纵向的、考虑漂移的对抗鲁棒性评估，使用从模拟器和真实设备执行中提取的静态和动态特征表示，跨越超过十年的Android应用。数据集按年度切片组织，并在三种模拟现实学习场景的部署协议下进行评估：（1）同年度训练和测试，（2）跨年度部署且不更新模型，（3）使用累积历史数据进行扩展窗口重训练。在多个分类器家族中，使用FGSM和SPSA在可行性约束下生成对抗样本。我们测量了干净性能、对抗准确率（AA）、攻击成功率（ASR），并引入了时序关联指标——RobustDrop、$\Delta$ASR和对抗放大因子（AAF）——以量化分布漂移与鲁棒性退化之间的关系。结果表明，在评估的基于迁移的特征空间设置下，时间分离与对抗鲁棒性降低相关。随着训练-测试间隔增加，干净准确率和对抗准确率下降，而攻击成功率呈现配置相关的增加，特别是在FGSM扰动和静态特征下。扩展窗口重训练可以缓解但无法消除在持续分布演化下的鲁棒性损失。这些发现表明，在评估智能检测系统在演化数据分布下的长期鲁棒性时，应考虑时间漂移，并强调了在长期对抗环境中需要漂移感知的鲁棒性评估框架。
英文摘要：
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 evaluated under three deployment protocols that emulate realistic learning scenarios: (1) same-year training and testing, (2) cross-year deployment without model updates, and (3) expanding-window retraining with cumulative historical data. Across multiple classifier families, adversarial examples are generated using FGSM and SPSA under feasibility constraints. We measure clean performance, Adversarial Accuracy (AA), Attack Success Rate (ASR), and introduce temporal linkage metrics -- RobustDrop, $Δ$ASR, and Adversarial Amplification Factor (AAF) -- to quantify the relationship between distribution shift and robustness degradation.nResults show that temporal separation is associated with reduced adversarial robustness under the evaluated transfer-based feature-space setting. As the train-test gap increases, clean accuracy and adversarial accuracy decline, while attack success exhibits configuration-dependent increases, particularly under FGSM perturbations and static features. Expanding-window retraining mitigates, but does not eliminate, robustness loss under continued distributional evolution. These findings indicate that temporal drift should be considered when assessing the long-term robustness of intelligent detection systems under evolving data distributions and highlight the need for drift-aware robustness assessment frameworks in long-lived adversarial environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
