Abstract :

The Proliferation Of Unstructured Cyber Intelligence Data Has Made Real-time Threat Identification A Critical Challenge. This Research Presents A Context-aware Natural Language Processing (NLP) Pipeline Designed To Semantically Identify, Interpret, And Analyze Emerging Cyber Threats Across Heterogeneous Data Sources. Unlike Classifier-based Models, The Proposed Pipeline Focuses On Linguistic Interpretation, Contextual Embeddings, And Semantic Correlation To Extract Indicators Of Compromise (IoCs). The System Integrates Adaptive Sentence Encoding, Dependency Parsing, And Ontology-based Normalization To Create A Coherent Representation Of Cyber Threat Narratives. It Semantically Maps Relationships Between Actors, Attack Types, And Targets Without The Dependency On Supervised Training Datasets. The Framework Demonstrates High Adaptability, Allowing It To Interpret Evolving Cybersecurity Terminology And Discourse. The Results Highlight The Efficiency Of The Proposed NLP Pipeline In Transforming Raw Text Into Actionable Intelligence That Enhances Early Warning And Proactive Threat Response Mechanisms. Keywords: Cybersecurity, NLP, Semantic Analysis, Context-Aware Systems, Threat Intelligence, Entity Normalization.

Published:

20-11-2025

Issue:

Vol. 25 No. 11 (2025)

Page Nos:

175-185

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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