A photo-based ransomware classifier reaches 93.6% accuracy from one training image per variant plus data augmentation, and uses Monte Carlo dropout uncertainty to flag unknown inputs.
Ransomware, threat and detection techniques: A review,
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A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation
A photo-based ransomware classifier reaches 93.6% accuracy from one training image per variant plus data augmentation, and uses Monte Carlo dropout uncertainty to flag unknown inputs.