Adversarial Vulnerability Under Temporal Concept Drift: A Longitudinal Study of Android Malware Detection

ArXi:2605.23623v1 Announce Type: cross We present a longitudinal, drift-aware evaluation of adversarial robustness across 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