Methods to Collect and Assess Data for Recognizing, Halting, and Mitigating Cyber Threats

This research looks at sophisticated cyber threat intelligence (CTI) techniques, with an emphasis on how to gather, process, and use data to identify, stop, and mitigating cyber threats. We assess and contrast various data mining and machine learning techniques, such as supervised and unsupervised learning models, for threat analysis and detection. The efficiency of ensemble approaches that combine deep learning and conventional anomaly detection techniques is demonstrated by experimental results on real-world cyber threat datasets. The suggested hybrid model outperforms individual models with an accuracy of ۹۶.۳% in detecting threats. As a result of the CTI system's implementation, there were ۴۲% fewer successful attacks and ۳۵% fewer threats detected in the interim. Future research directions and the main obstacles and constraints in operationalizing CTI are examined.

Keywords:

Cyber threat intelligence techniques , Machine learning , Deep learning , Mitigate