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AI-Augmented Threat Hunting: Leveraging NLP for Analyzing Dark Web Threat Intelligence

Author

Listed:
  • Joy Selasi Agbesi

    (Department; J. Warren McClure School of Emerging Communication & Technology, Ohio University, USA)

  • Gbenga Alex Ajimatanrareje

    (Department of Data Science and Artificial Intelligence, Bournemouth University, UK)

Abstract

The proliferation of cyber threats originating from the dark web has necessitated advanced methodologies for threat intelligence gathering and analysis. This paper explores the integration of artificial intelligence (AI) and natural language processing (NLP) techniques in augmenting traditional threat hunting practices. By leveraging machine learning algorithms and sophisticated linguistic analysis, cybersecurity professionals can now extract actionable intelligence from unstructured dark web communications, forum discussions, and threat actor narratives. This comprehensive review examines current state-of-the-art approaches, challenges, and future directions in AI-augmented threat hunting, with particular emphasis on NLP applications for dark web threat intelligence analysis.

Suggested Citation

  • Joy Selasi Agbesi & Gbenga Alex Ajimatanrareje, 2025. "AI-Augmented Threat Hunting: Leveraging NLP for Analyzing Dark Web Threat Intelligence," Journal of Computer Science and Information Technology, Bluemark Publishers, vol. 2(1), pages 74-87, February.
    • Handle: RePEc:cxi:jcsit0:v:2:y:2025:i:1:id:499
    DOI: 10.61424/jcsit.v2i1.499
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