NATURAL LANGUAGE PROCESSING FOR CYBERSECURITY: A STUDY ON TEXT ANALYSIS FOR THREAT INTELLIGENCE

Abstract

This study investigated the application of Natural Language Processing (NLP) techniques in cybersecurity threat intelligence within Pakistan's unique linguistic and technological landscape. The research employed a mixed-methods approach analyzing 50,000 text samples from Pakistani cybersecurity sources, with 60% in Urdu and 40% in English. Advanced NLP techniques including tokenization, named entity recognition, sentiment analysis, and topic modeling were implemented using machine learning algorithms such as Support Vector Machines, Random Forest, and LSTM neural networks. The findings revealed that NLP-based threat intelligence systems achieved 87.3% accuracy in threat classification, with significant improvements in processing speed (65% faster) and identification of emerging threats (72% improvement). The study demonstrated that culturally adapted NLP models performed 23% better than generic models when processing Pakistani cybersecurity communications. The research highlighted critical challenges including code-switching between languages, evolving threat terminologies, and data privacy concerns. The results provide valuable insights for developing contextually appropriate cybersecurity solutions for multilingual environments and contribute to the growing body of knowledge in AI-driven cybersecurity defense mechanisms.