Diagnosing and Mitigating Domain Shift in Permission-Based Android Malware Detection
Machine learning-based Android malware detectors often fail in real-world deployment due to domain shift, where models trained on one data source perform poorly on applications from another. This paper presents a comprehensive study on the generalizability and interpretability of permission-based detectors under cross-domain conditions. Using two complementary datasets (PerMalDroid and NATICUSdroid) and five ensemble classifiers, we first establish an intra-domain baseline, where models achieve over 92% accuracy, and then quantify a severe asymmetric performance drop. While models trained on PerMalDroid generalize well to NATICUSdroid (86% accuracy), the reverse direction sees a drastic d...
| Authors | Md Rafid Islam |
|---|---|
| Primary category | cs.LG |
| Published | 9 May 2026 |
| Last updated at source | 15 July 2026 |
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