Edward Raff
3 indexed papers
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The paper introduces McNdroid, a large longitudinal multimodal benchmark for Android malware, demonstrating that temporal drift significantly degrades detection performance, which is best mitigated by multimodal fusion.
The paper introduces ASSEMBLAGE-DEEPHISTORY, a novel, comprehensive binary dataset that unifies cross-compiler builds, historical versions, and vulnerability labels into a single, queryable structure.
The paper introduces the first byte-native Large Language Model (LLM) capable of analyzing raw executable binary data, achieving high accuracy in tasks like malware and architecture classification.
Papers
Large Byte Model: Teaching Language Models About Compiled Code
The paper introduces the first byte-native Large Language Model (LLM) capable of analyzing raw executable binary data, achieving high accuracy in tasks like malware and architecture classification.