ARCeR: an Agentic RAG for the Automated Definition of Cyber Ranges
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912500693336064 |
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| author | Lupinacci, Matteo Blefari, Francesco Romeo, Francesco Pironti, Francesco Aurelio Furfaro, Angelo |
| author_facet | Lupinacci, Matteo Blefari, Francesco Romeo, Francesco Pironti, Francesco Aurelio Furfaro, Angelo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_12143 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ARCeR: an Agentic RAG for the Automated Definition of Cyber Ranges Lupinacci, Matteo Blefari, Francesco Romeo, Francesco Pironti, Francesco Aurelio Furfaro, Angelo Cryptography and Security Artificial Intelligence 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. |
| title | ARCeR: an Agentic RAG for the Automated Definition of Cyber Ranges |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2504.12143 |