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Comparing Traditional and AI-Driven Approaches to Cyber Threat Identification: A Comparative EvaluationUsing MITRE ATT & CK
Obasi, Clementina Njideka
University West, Department of Engineering Science.
Nabi, Muhammad Mahmudun
University West, Department of Engineering Science.
2026 (English)
Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE credits
Student thesis
Place, publisher, year, edition, pages
2026. , p. 54
Keywords [en]
Cyber threat identification, MITRE ATT&CK, ISO/IEC 27005, Large language models (LLMs), Comparative case study
National Category
Other Computer and Information Science
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URN:
urn:nbn:se:hv:diva-25468
Local ID: EXD630
OAI: oai:DiVA.org:hv-25468
DiVA, id:
diva2:2075232
Subject / course
Computer engineering
Educational program
Master in Cybersecurity
Supervisors
Djebbar, Fatiha
University West, Department of Engineering Science, Division of computer engineering and computer science.
Examiners
Mohammad, Abdulghafour
University West, Department of Engineering Science, Division of computer engineering and computer science.
Available from:
2026-06-22
Created:
2026-06-18
Last updated:
2026-07-21
Bibliographically approved
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51 downloads
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FULLTEXT01.pdf
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47c239e0c73917128a25efd61e205df036446828af9761c8f9b26b1ce9362fbd9c4a92a2f0c483c841c0c565cc2c976c9f49d78f8e998c4e8fab7761642bb8ba
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modern-language-association-8th-edition
vancouver
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apa
ieee
modern-language-association-8th-edition
vancouver
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