Global Journal of Engineering and Technology Research (GJETR) · Research article · Open access
Natural Language Processing for Real-Time Semantic Correlation of Shared Cyber Threat Indicators in National Defense
Chukwunenye Amadi, Abolaji Adebayo, Ayokunle Olamide Ijagbemi
5 September 2026 · Vol. 2, Issue 9, pp. 428-447
DOI: 10.65150/EP-gjetr/V2E9/2026-05
Download full text (PDF)Cite this article
What is this article about?
Defense intelligence systems receive continuous streams of natural-language threat reports in which the same event is frequently described using different terminology, structure, and coded language. Traditional keyword systems classify such reports as unrelated, fragmenting the intelligence picture and delaying coordinated response.
Article at a glance
- Published
- 5 September 2026
- Volume / issue / pages
- Vol. 2, Issue 9, pp. 428-447
- ISSN
- 3051-3782 (online) · 3051-3774 (print)
- Licence
- CC BY 4.0 — free to read and reuse with attribution
- Peer review
- Double-blind, at least two independent reviewers
- Publisher
- EP Journals Group
Abstract
Defense intelligence systems receive continuous streams of natural-language threat reports in which the same event is frequently described using different terminology, structure, and coded language. Traditional keyword systems classify such reports as unrelated, fragmenting the intelligence picture and delaying coordinated response. This paper examines how Natural Language Processing (NLP) enables the real-time semantic correlation of shared cyber threat indicators. It reviews the progression from term-weighting and word embeddings to contextual transformer representations, and specifies an NLP pipeline that combines semantic encoding, similarity scoring, named-entity and relationship extraction, and clustering to link related threats into unified, analyst-ready groups. Using representative open-source and synthetic corpora, the study shows conceptually and through illustrative results that embedding-based correlation recognizes semantically equivalent reports that share few surface tokens, thereby exposing coordinated and evolving threats. The paper contributes a structured account of semantic correlation for defense CTI, a categorization of similarity thresholds into actionable intelligence bands, and a discussion of the operational and ethical conditions for deployment. The evaluation is illustrative rather than comparative: the behavior described and the similarity bands reported are indicative defaults presented together with a specified evaluation protocol and calibration procedure, not measured results, and the relationship of this work to the first author's earlier study of semantic correlation and de-duplication is disclosed and delineated explicitly. Subject to those qualifications, the findings suggest that NLP-driven correlation materially improves situational awareness and the speed of national defense response.
Keywords: natural language processing, semantic similarity, word embeddings, cyber threat intelligence, entity extraction, clustering, situational awareness.
Cite this article
Chukwunenye Amadi, Abolaji Adebayo, & Ayokunle Olamide Ijagbemi (2026). Natural Language Processing for Real-Time Semantic Correlation of Shared Cyber Threat Indicators in National Defense. Global Journal of Engineering and Technology Research, 2(9), 428-447. https://doi.org/10.65150/EP-gjetr/V2E9/2026-05
@article{Chukwunenye2026,
title = {Natural Language Processing for Real-Time Semantic Correlation of Shared Cyber Threat Indicators in National Defense},
author = {Chukwunenye Amadi and Abolaji Adebayo and Ayokunle Olamide Ijagbemi},
journal = {Global Journal of Engineering and Technology Research},
year = {2026},
volume = {2},
number = {9},
pages = {428-447},
doi = {10.65150/EP-gjetr/V2E9/2026-05},
url = {https://doi.org/10.65150/EP-gjetr/V2E9/2026-05}
}Questions about this article
Who wrote this article?
Chukwunenye Amadi, Abolaji Adebayo, Ayokunle Olamide Ijagbemi.
Where and when was it published?
In Global Journal of Engineering and Technology Research, Volume 2, Issue 9, pages 428-447, published by EP Journals Group on 5 September 2026.
Is this article free to read?
Yes. It is open access under a Creative Commons Attribution 4.0 (CC BY 4.0) licence, so anyone can read, share and reuse it with attribution.
What is the DOI of this article?
10.65150/EP-gjetr/V2E9/2026-05 (https://doi.org/10.65150/EP-gjetr/V2E9/2026-05).
How do I cite this article?
APA: Chukwunenye Amadi, Abolaji Adebayo, & Ayokunle Olamide Ijagbemi (2026). Natural Language Processing for Real-Time Semantic Correlation of Shared Cyber Threat Indicators in National Defense. Global Journal of Engineering and Technology Research, 2(9), 428-447. https://doi.org/10.65150/EP-gjetr/V2E9/2026-05
More research from GJETR
- Optimized Deep Convolutional Networks for MRI Brain Tumor Classification and Segmentation: A Systematic Review
Maher Talal Al-Asaady · Oct 2026
- Industry Micro-Credential Infusion in PQF Level 5 Technology Diploma Programs: Opportunities and Challenges
Gamaliel B. Vicente Jr., Alma O. Medio, Redjie D. Arcadio · Oct 2026
- Assessment of Process Mapping, Risk Management, and Iso 9001 Quality Management Standards: Basis for a Technology-Enabled Quality Management Framework
Patrick E. Arendain, Redjie D. Arcadio · Oct 2026
- Integrating Solar-Powered PV Cells And SCR-Controlled Three-Phase Rectifiers to Design A Two-Level DC Grid Architecture
Sadia Sultana Turna, Amzad Ali Sarkar · Oct 2026
- Density Functional Theory Investigation of Defect-Induced Electronic Behavior in Iodine-Deficient Mapbi₃ Perovskites for Solar Cells
Sani Kado, Muhammad Y. Onimisi, Dahiru M. Sanni · Sept 2026