Label-Efficient Training Updates for Malware Detection over Time

🗓️ 30 Mar 2026 00:00:00Reported by Daniele Ghiani, Luca Minnei, Cristian Manca, Angelo Sotgiu, Giorgio Giacinto, Maura Pintor, Davide Maiorca, Battista Biggio, Giorgio PirasType

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This work proposes a model-agnostic framework for active learning and semi-supervised malware detection under drift, reducing labeling costs.

30 Mar 2026 00:00Current

5.9Medium risk

Vulners AI Score5.9

model-agnostic active learning semi-supervised malware detection drift analysis Android malware Windows malware feature drift labeling costs