用自然语言自动生成网络安全训练环境，提升自动化水平
ARCeR: an Agentic RAG for the Automated Definition of Cyber Ranges
- 基于智能体RAG框架，理解自然语言指令生成安全环境
- 在复杂提示下表现优于传统大模型和基础RAG系统
- 适配任意网络安全框架，适合安全培训与漏洞研究者
日益增长且不断演变的网络威胁催生了对支持性工具的需求，以构建在虚拟、受控环境中运行的真实IT环境，即网络安全训练场（Cyber Ranges, CRs）。CRs可用于分析漏洞、测试防御措施有效性，并作为培养信息技术人员网络安全技能的训练平台。本文提出ARCeR，一种从用户提供的自然语言描述自动生成并部署CRs的创新方案。ARCeR基于智能体增强的检索增强生成（Agentic RAG）范式，充分融合前沿AI技术。实验结果表明，即使在大语言模型（LLMs）或基础RAG系统无法应对的情况下，ARCeR仍能成功处理提示。此外，只要提供相应知识，ARCeR可适配任意CR框架。
原文摘要 · Abstract (English)
The growing and evolving landscape of cybersecurity threats necessitates the development of supporting tools and platforms that allow for the creation of realistic IT environments operating within virtual, controlled settings as Cyber Ranges (CRs). CRs can be exploited for analyzing vulnerabilities and experimenting with the effectiveness of devised countermeasures, as well as serving as training environments for building cyber security skills and abilities for IT operators. This paper proposes ARCeR as an innovative solution for the automatic generation and deployment of CRs, starting from user-provided descriptions in a natural language. ARCeR relies on the Agentic RAG paradigm, which allows it to fully exploit state-of-art AI technologies. Experimental results show that ARCeR is able to successfully process prompts even in cases that LLMs or basic RAG systems are not able to cope with. Furthermore, ARCeR is able to target any CR framework provided that specific knowledge is made available to it.
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