CRVA-TGRAG combines parent-document segmentation, ensemble retrieval, and teacher-guided fine-tuning to mitigate knowledge conflicts and improve accuracy in LLM-based CVE vulnerability analysis.
Using LLMs to Automate Threat Intelligence Analysis Workflows in Security Operation Centers, July 2024
3 Pith papers cite this work. Polarity classification is still indexing.
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A RAG system with query-based log filtering achieves up to 94% recall in malware incident analysis and 96% attack-step detection, with ablation studies confirming the filtering step is essential.
An integrated framework using autoencoders, deep reinforcement learning, and LLMs automates risk-based prioritization and contextual analysis of suspicious network traffic within Splunk SOC environments.
citing papers explorer
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Tug-of-War within A Decade: Conflict Resolution in Vulnerability Analysis via Teacher-Guided Retrieval-Augmented Generations
CRVA-TGRAG combines parent-document segmentation, ensemble retrieval, and teacher-guided fine-tuning to mitigate knowledge conflicts and improve accuracy in LLM-based CVE vulnerability analysis.
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Retrieval-Augmented LLMs for Security Incident Analysis
A RAG system with query-based log filtering achieves up to 94% recall in malware incident analysis and 96% attack-step detection, with ablation studies confirming the filtering step is essential.
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Policy-Guided Threat Hunting: An LLM enabled Framework with Splunk SOC Triage
An integrated framework using autoencoders, deep reinforcement learning, and LLMs automates risk-based prioritization and contextual analysis of suspicious network traffic within Splunk SOC environments.