This research study examines the pivotal role of Large Language Models (LLMs) and Natural Language Processing (NLP) in transforming national defense intelligence operations faced with information overload. In the contemporary digital security landscape, defense agencies are inundated with vast volumes of unstructured, redundant, and fragmented threat data from diverse global sources, which hinders timely and accurate analysis. The study addresses this critical challenge by designing and evaluating an AI-driven framework specifically for the real-time semantic correlation and intelligent de-duplication of shared cyber threat indicators.
Utilizing open-source and synthetic intelligence datasets, the proposed system employs advanced embedding techniques to understand contextual meaning, cluster related threats, and eliminate semantic redundancies. The results conclusively demonstrate that this LLM-based approach substantially outperforms conventional keyword-matching systems in both accuracy and processing speed. The integration of such semantic intelligence tools not only alleviates the cognitive burden on human analysts but also provides a clearer, more actionable intelligence picture, thereby accelerating response times and strengthening overall national cybersecurity posture and defense readiness.
- Arbeit zitieren
- Chukwunenye Amadi (Autor:in), 2025, Accelerating National Defense: Using Large Language Models (LLM) and NLP for Real-Time Semantic Correlation and De-Duplication of Shared Threat Indicators, München, GRIN Verlag, https://www.grin.com/document/1683825
