mlsanityData to 7 Oct 2026

MoRSE: Bridging the Gap in Cybersecurity Expertise with Retrieval Augmented Generation

arXiv 2407.15748 ↗First posted 22 Jul 2024cs.CR cs.AI cs.IRcited by 8

On the map: Retrieval-augmented generation → Privacy and security of RAG

Abstract (arXiv)

In this paper, we introduce MoRSE (Mixture of RAGs Security Experts), the first specialised AI chatbot for cybersecurity. MoRSE aims to provide comprehensive and complete knowledge about cybersecurity. MoRSE uses two RAG (Retrieval Augmented Generation) systems designed to retrieve and organize information from multidimensional cybersecurity contexts. MoRSE differs from traditional RAGs by using parallel retrievers that work together to retrieve semantically related information in different formats and structures. Unlike traditional Large Language Models (LLMs) that rely on Parametric Knowledge Bases, MoRSE retrieves relevant documents from Non-Parametric Knowledge Bases in response to user queries. Subsequently, MoRSE uses this information to generate accurate answers. In addition, MoRSE benefits from real-time updates to its knowledge bases, enabling continuous knowledge enrichment without retraining. We have evaluated the effectiveness of MoRSE against other state-of-the-art LLMs, evaluating the system on 600 cybersecurity specific questions. The experimental evaluation has shown that the improvement in terms of relevance and correctness of the answer is more than 10% compared to known solutions such as GPT-4 and Mixtral 7x8.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Placed in Privacy and security of RAG, one of 15 directions in Retrieval-augmented generation. Builds on 45 of its 97 references that are in the map. First posted on arXiv 22 Jul 2024.

Builds on

45 of 97 references.

References · 45

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33 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 5.

8 papers.

  1. 2025AutoBnB-RAG: Enhancing Multi-Agent Incident Response with Retrieval-Augmented Generationcited by 3
  2. 2025CyberMentor: AI Powered Learning Tool Platform to Address Diverse Student Needs in Cybersecurity Educationcited by 1
  3. 2025TITAN: Graph-Executable Reasoning for Cyber Threat Intelligencecited by 0
  4. 2026PriMod4AI: Lifecycle-Aware Privacy Threat Modeling for AI Systems using LLMcited by 0
  5. 2026CyberCane: Neuro-Symbolic RAG for Privacy-Preserving Phishing Detection with Formal Ontology Reasoningcited by 0
  6. 2026From Detection to Response: A Deep Learning and Retrieval-Augmented Generation Framework for Network Intrusion Mitigationcited by 0
  7. 2026MITRE-SAGE: A Multi-Agent Cybersecurity Question-Answering Modelcited by 0
  8. 2026NeuroGraph: An AI Graph-Driven Neuro-Symbolic Framework for Explainable Threat Reasoning in Advanced Manufacturingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025CyberBOT: Towards Reliable Cybersecurity Education via Ontology-Grounded Retrieval Augmented Generation
  2. 2024IntellBot: Retrieval Augmented LLM Chatbot for Cyber Threat Knowledge Delivery
  3. 2026Beyond RAG for Cyber Threat Intelligence: A Systematic Evaluation of Graph-Based and Agentic Retrieval
  4. 2025Large Language Models for Explainable Threat Intelligence
  5. 2025Adapting Large Language Models to Emerging Cybersecurity using Retrieval Augmented Generation
  6. 2024A RAG-Based Question-Answering Solution for Cyber-Attack Investigation and Attribution
  7. 2024Ontology-Aware RAG for Improved Question-Answering in Cybersecurity Education
  8. 2024CyberMetric: A Benchmark Dataset based on Retrieval-Augmented Generation for Evaluating LLMs in Cybersecurity Knowledge