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Department of Electrical and Electronic Engineering, University of Cagliari, Italy, CINI, Rome, Italy
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Slash malware detection labeling costs by 90% using combined active and semi-supervised learning, without sacrificing performance.
LLMs can now automate and explain memory forensics, extracting more malware indicators of compromise than existing tools and making analysis accessible to non-experts.
