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The University of Alabama
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Current open-source LLMs fall short in effectively classifying complex CTI reports, with the best model only achieving an F1 score of 0.22.
Fine-tuning Small Language Models on a comprehensive multi-source cybersecurity log dataset resulted in a dramatic leap in classification accuracy, highlighting the potential of cross-source data for enhanced threat detection.
LLMs can almost perfectly detect malicious software packages, but their accuracy plummets when asked to pinpoint *why* a package is malicious.