Natural Language Processing for Real-Time Semantic Correlation of Shared Cyber Threat Indicators in National Defense

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.