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arXiv · 2502.05011

Learning the Language of NVMe Streams for Ransomware Detection

Barak Bringoltz·Elisha Halperin·Ran Feraru·Evgeny Blaichman·Amit Berman
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Abstract

We apply language modeling techniques to detect ransomware activity in NVMe command sequences. We design and train two types of transformer-based models: the Command-Level Transformer (CLT) performs in-context token classification to determine whether individual commands are initiated by ransomware, and the Patch-Level Transformer (PLT) predicts the volume of data accessed by ransomware within a patch of commands. We present both model designs and the corresponding tokenization and embedding schemes and show that they improve over state-of-the-art tabular methods by up to 24% in missed-detection rate, 66% in data loss prevention, and 84% in identifying data accessed by ransomware.

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Barak Bringoltz, Elisha Halperin, Ran Feraru, Evgeny Blaichman, Amit Berman. 2025-02-07. Learning the Language of NVMe Streams for Ransomware Detection. https://arxiv.org/abs/2502.05011

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