NLP FOR CYBERSECURITY: TEXT ANALYSIS IN THREAT INTELLIGENCE

Abstract

This study explores the use of Natural Language Processing (NLP) for cybersecurity threat intelligence in Pakistan’s multilingual and evolving digital landscape. Using a mixed-methods approach, 50,000 text samples (60% Urdu, 40% English) from local cybersecurity sources were analyzed. Techniques such as tokenization, named entity recognition, sentiment analysis, and topic modeling were applied using SVM, Random Forest, and LSTM algorithms. Results showed 87.3% accuracy in threat classification, a 65% increase in processing speed, and a 72% improvement in identifying emerging threats. Culturally adapted NLP models outperformed generic ones by 23%. Key challenges included code-switching, shifting threat vocabularies, and data privacy. The study offers actionable insights for building effective, localized cybersecurity solutions in multilingual settings