The paper proposes an LLM-driven, dual-layer 'situational knowledge' memory for extracting MITRE ATT&CK techniques from threat reports, claiming an 11% higher F1 than GPT-4o, though the baseline evaluation is incomplete.
Noise contrastive estimation-based matching framework for low-resource security attack pattern recognition,
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Instantiating Standards: Enabling Standard-Driven Text TTP Extraction with Evolvable Memory
The paper proposes an LLM-driven, dual-layer 'situational knowledge' memory for extracting MITRE ATT&CK techniques from threat reports, claiming an 11% higher F1 than GPT-4o, though the baseline evaluation is incomplete.