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.
When LLMs meet cybersecurity: a systematic literature review
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A 16-factor structured prompt framework strengthens CoT reasoning in LLMs for security analysis, yielding up to 40% reasoning gains in smaller models and stable accuracy improvements validated by human raters with Cohen's k > 0.80.
OntoLogX is a system that applies LLMs with ontology guidance, RAG, and iterative fixes to build valid knowledge graphs from cybersecurity logs and predict ATT&CK tactics from aggregated sessions.
XekRung achieves state-of-the-art performance on cybersecurity benchmarks among same-scale models via tailored data synthesis and multi-stage training while retaining strong general capabilities.
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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Strengthening Human-Centric Chain-of-Thought Reasoning Integrity in LLMs via a Structured Prompt Framework
A 16-factor structured prompt framework strengthens CoT reasoning in LLMs for security analysis, yielding up to 40% reasoning gains in smaller models and stable accuracy improvements validated by human raters with Cohen's k > 0.80.
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OntoLogX: Ontology-Guided Knowledge Graph Extraction from Cybersecurity Logs with Large Language Models
OntoLogX is a system that applies LLMs with ontology guidance, RAG, and iterative fixes to build valid knowledge graphs from cybersecurity logs and predict ATT&CK tactics from aggregated sessions.
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XekRung Technical Report
XekRung achieves state-of-the-art performance on cybersecurity benchmarks among same-scale models via tailored data synthesis and multi-stage training while retaining strong general capabilities.
- Honeyquest for LLMs: Rethinking Cyber Deception for AI Attackers