McNdroid: A Longitudinal Multimodal Benchmark for Robust Drift Detection in Android Malware
Machine learning (ML) in real-world systems must contend with concept drift, adversarial actors, and a spectrum of potential features with varying costs and benefits. Malware naturally exhibits all of these complexities, but for the same reason, it is challenging to curate and organize data to study these factors. We present McNdroid, to our knowledge the largest longitudinal multimodal Android malware benchmark for malware detection and drift analysis. McNdroid spans 2013--2025, excluding 2015, and represents each application with three aligned modalities--static features from manifests and smali code, dynamic behavioral features from sandbox execution, and graph-based features from func...
| Authors | Md Mahmuduzzaman Kamol, Jesus Lopez, Saeefa Rubaiyet Nowmi et al. |
|---|---|
| Primary category | cs.CR |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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