AUTOMATED EMERGING CYBER THREAT IDENTIFICATION AND PROFILING BASED ON NATURAL LANGUAGE PROCESSING

Abstract

The rapid evolution of cyber threats poses significant challenges for security analysts, who must continuously process large volumes of unstructured information from threat reports, security blogs, vulnerability databases, and dark-web discussions. Manual analysis of these sources is time-consuming, inconsistent, and unable to keep pace with the increasing frequency and sophistication of emerging attacks. This work presents an automated framework for emerging cyber-threat identification and profiling using Natural Language Processing (NLP). The proposed system collects real-time textual data from multiple cybersecurity intelligence sources and applies advanced NLP techniques—such as entity extraction, topic modeling, semantic similarity, and threat classification—to detect newly emerging vulnerabilities, exploits, malware families, and attack trends. Using machine-learning–based clustering and profiling mechanisms, the system generates structured threat intelligence reports that summarize threat attributes, severity levels, affected platforms, and potential attack vectors. The automated pipeline reduces analyst workload, minimizes detection delays, and provides an adaptive, scalable solution for enterprise threat intelligence. Experimental results show that the NLP-based approach significantly enhances the accuracy and speed of early threat discovery when compared to traditional manual analysis.