Threat Intelligence Automation Using Natural Language Processing on Dark Web Data

Abstract: This study presents an automated framework for threat intelligence gathering using Natural Language Processing (NLP) on dark web data. The growing sophistication of cyberattacks necessitates real-time detection of emerging threats. Traditional manual analysis of dark web forums is time-consuming and insufficient. This research proposes a hybrid NLP pipeline that integrates named entity recognition, sentiment analysis, and topic modeling to extract actionable threat indicators from darknet discussions. A dataset comprising over 100,000 dark web posts was analyzed, yielding high accuracy in identifying cyber threat actors, malware variants, and planned attacks. The proposed model outperformed baseline models by 17% in F1 score. The findings highlight the utility of automated threat intelligence systems in reducing human workload and accelerating cyber defense responses. Keywords Threat Intelligence, Natural Language Processing, Dark Web, Cybersecurity, Automation, Topic Modeling