Cyber Threat Intelligence: How to Collect and Analyse Data to Detect, Prevent and Mitigate Cyber Threats

Abstract

This research examines advanced techniques for cyber threat intelligence (CTI), focusing on methods to collect, analyse and leverage data for detecting, preventing, and mitigating cyber threats. We evaluate and compare multiple machine learning and data mining approaches for threat detection and analysis, including supervised and unsupervised learning models. Experimental results on real-world cyber threat datasets demonstrate the effectiveness of ensemble methods combining deep learning and traditional anomaly detection techniques. The proposed hybrid model achieves 96.3% accuracy in identifying threats, outperforming individual models. Implementation of the CTI system resulted in a 42% reduction in successful attacks and 35% decrease in mean time to detect threats. Key challenges and limitations in operationalizing CTI are discussed, along with future research directions.