Cyber Threat Intelligence System

Abstract

In recent years, the rapid growth of digital technologies has led to an increase in sophisticated cyber threats, making traditional signature-based detection systems insufficient for modern cybersecurity needs. This paper presents the Cyber Threat Intelligence Dashboard, an intelligent and real-time cyber threat detection system that integrates machine learning, feature-based analysis, and threat intelligence into a unified platform. The system focuses on analyzing Indicators of Compromise (IOCs), such as IP addresses and domain names, by extracting meaningful structural and statistical features including length, character distribution, and entropy. The extracted features are utilized to train multiple machine learning models, such as Random Forest, Decision Tree, and Logistic Regression, for accurate classification of indicators into safe and malicious categories. A comparative analysis of these models is performed using evaluation metrics such as accuracy, precision, recall, and F¹-score to identify the most effective approach. Overall, the Cyber Threat Intelligence Dashboard offers a scalable, efficient, and intelligent approach to cyber threat detection, with the potential for future enhancements in predictive analytics and large-scale deployment.