Enhancing Cyber-Threat Intelligence in the Arab World: Leveraging IoC and MISP Integration

Abstract

Cybercrime threat intelligence enables proactive measures against threat actors and informed, data-driven security decisions. This study proposes a practical implementation of cybercrime threat intelligence in the Arab world by integrating Indicators of Compromise and collecting security alerts from honeypot systems and open-source intelligence. The data collected are stored on the Malware Information Sharing Platform, an open-source platform used to create and share Indicators of Compromise. This study highlights the intuitive interface of the Malware Information Sharing Platform for data analysis, threat identification, and the correlation of Indicators of Compromise. In addition, machine learning techniques are applied to improve predictive accuracy and identify patterns in the data. The decision tree classifier achieves a high accuracy of 99.79%, and the results reveal significant potential cyber-threats, demonstrating the effectiveness of the platform in providing actionable information to prevent, detect, and respond to cybercrime. This approach aims to improve the security posture of the Arab region.