INTELLIGENT NLP-DRIVEN FRAMEWORK FOR AUTOMATED DETECTION AND PROFILING OF EMERGING CYBER THREATS

Abstract

The rapid evolution of digital ecosystems has given rise to a surge in sophisticated and emerging cyber threats that challenge traditional defense mechanisms. This study proposes an Intelligent NLP-Driven Framework designed to automatically detect and profile cyber threats by extracting, analyzing, and interpreting threat-related information from unstructured textual data sources such as social media, dark web forums, and cybersecurity reports. Leveraging advanced Natural Language Processing (NLP) and machine learning models, the framework identifies key threat indicators, attack patterns, and entity relationships in real time. The system aims to enhance the efficiency of cyber threat intelligence (CTI) generation while reducing analyst workload. Experimental evaluation demonstrates improved accuracy in early detection and profiling compared with baseline keyword-based systems, establishing the framework as a scalable and adaptive solution for next-generation cyber defense.