AI RESEARCH
McNdroid: A Longitudinal Multimodal Benchmark for Robust Drift Detection in Android Malware
arXiv CS.LG
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ArXi:2605.06894v1 Announce Type: cross 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.