MAD-CTI: Cyber Threat Intelligence Analysis of the Dark Web Using a Multi-Agent Framework
The dark web is a host to illicit activities where hacker forums, blogs, and articles provide significant insights into Cyber Threat Intelligence (CTI) that are frequently unavailable on the surface web. The increasing incidence of security breaches underscores the necessity for advanced CTI solutio...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10908603/ |
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| Summary: | The dark web is a host to illicit activities where hacker forums, blogs, and articles provide significant insights into Cyber Threat Intelligence (CTI) that are frequently unavailable on the surface web. The increasing incidence of security breaches underscores the necessity for advanced CTI solutions to defend against emerging threats. This paper introduces MAD-CTI, a novel multi-agent framework based on Large Language Models (LLM) designed to extract insights from dark web sources. It independently scrapes, analyzes, and classifies content related to vulnerabilities, malware, and hacking, by leveraging a multi-agent architecture to improve efficiency, scalability, and consistency. By utilizing state-of-the-art LLM models and agents, we demonstrate how organizations can adopt this methodology to enhance the accuracy and efficiency of CTI. |
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| ISSN: | 2169-3536 |