LLM-based framework generates weak labels for cybersecurity tweets

Researchers have developed a novel teacher-student framework utilizing large language models (LLMs) to generate weak labels for cybersecurity tweets. This approach addresses the bottleneck of manual annotation for cyber threat intelligence (CTI) by enabling the training of specialized pre-trained language models (PLMs) without extensive expert input. Experiments demonstrated that PLMs trained using these LLM-generated labels achieved F1 scores above 0.92, performing comparably to models trained with full human supervision. AI

IMPACT This method could significantly reduce the cost and time required to build specialized AI models for cybersecurity threat detection.

RANK_REASON The cluster contains an academic paper detailing a new methodology for weak supervision in a specific domain. [lever_c_demoted from research: ic=1 ai=1.0]

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• cybersecurity tweet classification
• cyber threat intelligence
• Hugging Face
• LLM
• pre-trained language models
• teacher-student framework
• X

• paper
• product

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