Retrieval-Augmented LLMs Enhance Cybersecurity Incident Analysis Efficiency

Researchers have developed a Retrieval-Augmented Generation (RAG) system to automate the analysis of cybersecurity incidents. This system uses targeted queries and a library of MITRE ATT&CK techniques to extract indicators from log data, then leverages LLMs for semantic reasoning to reconstruct attack sequences. Evaluations showed varying performance and cost tradeoffs among different LLM configurations, with Claude Sonnet 4 achieving high recall but DeepSeek V3 offering significantly lower costs, and a locally deployed Llama 3.1 model providing zero per-query cost. AI

影响 This RAG-based approach could significantly reduce the time and cost of cybersecurity incident analysis, freeing up human analysts for more complex tasks.

排序理由 Academic paper detailing a new system for security incident analysis using LLMs and RAG. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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• Claude Sonnet 4
• DeepSeek V3
• Llama 3.1
• LLM
• MITRE ATT&CK

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