Development Of Cyber Threat Intelligence Tools Using Python

Abstract

The increasing complexity of modern cyberattacks demands intelligent systems capable of identifying, analyzing, and predicting threats in real time. This study focuses on the development of cyber threat intelligence (CTI) tools using Python to automate the collection, analysis, and correlation of threat data from diverse sources. The proposed framework integrates Python-based modules for data harvesting, machine learning–driven threat classification, and entity extraction, enabling the transformation of raw indicators into actionable intelligence. Advanced analytics techniques, including natural language processing (NLP) and graphbased correlation, are employed to detect patterns, associate attack entities, and forecast potential threat vectors. Furthermore, the system leverages open-source platforms such as MISP and Wazuh, enhancing interoperability and intelligence sharing within security ecosystems. Experimental validation demonstrates that the developed Python-powered CTI tools can significantly improve situational awareness, reduce analysis time, and support proactive defense strategies. This work contributes to the evolution of automated, adaptive, and scalable CTI solutions for modern cybersecurity operations.