DEFENGRAPH integrates a dual-layer static-dynamic KG with LLMs via path retrieval, filtering, and re-ranking, raising reasoning-recall from 61.45% to 73.49% and ticket-action recall from 52.17% to 72.46% on GPT-4o in live red-blue cyber range data.
Ctinexus: Automatic cyber threat intelligence knowledge graph construction using large language models
4 Pith papers cite this work. Polarity classification is still indexing.
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Aggregating multiple CTI reports improves ATT&CK technique extraction F1 by about 26 percent over single-report baselines, with saturation after 5-15 reports and maximum F1 scores of 78.6 percent and 54.9 percent across the tested campaigns.
AI-native asset intelligence framework converts heterogeneous security signals into normalized asset importance scores by separating intrinsic exposure from contextual factors using modeling and deterministic aggregation.
A systematic review of neuro-symbolic AI in cybersecurity finds that deeper integration and causal reasoning improve performance across intrusion detection and vulnerability tasks, while identifying barriers and a research roadmap.
citing papers explorer
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DEFENGRAPH: Knowledge Graph-Enhanced LLMs for Blue Team Cyber Defense
DEFENGRAPH integrates a dual-layer static-dynamic KG with LLMs via path retrieval, filtering, and re-ranking, raising reasoning-recall from 61.45% to 73.49% and ticket-action recall from 52.17% to 72.46% on GPT-4o in live red-blue cyber range data.
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Beyond Single Reports: Evaluating Automated ATT&CK Technique Extraction in Multi-Report Campaign Settings
Aggregating multiple CTI reports improves ATT&CK technique extraction F1 by about 26 percent over single-report baselines, with saturation after 5-15 reports and maximum F1 scores of 78.6 percent and 54.9 percent across the tested campaigns.
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AI Native Asset Intelligence
AI-native asset intelligence framework converts heterogeneous security signals into normalized asset importance scores by separating intrinsic exposure from contextual factors using modeling and deterministic aggregation.
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Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunities
A systematic review of neuro-symbolic AI in cybersecurity finds that deeper integration and causal reasoning improve performance across intrusion detection and vulnerability tasks, while identifying barriers and a research roadmap.