MACHINE LEARNING-BASED MITIGATING CYBER THREAT PREDICTION FOR STRENGTHENING SECURITY ACROSS CYBER SUPPLY CHAINS - Rajput Jayasree; Sk.Mahaboob Basha (Utilitas Mathematica, 2025)
Cyber supply chains are complex systems that can be jammed by cyber threats affecting the business. They need CTI to keep threat actor behavior, Tactics, Techniques, and Procedures , and Indicators of Compromise under study and thus derive possible threats to minimize them. This paper incorporates CTI characteristics into a ML-based Cyber Threat Prediction model. Classification algorithms are used to predict cyber threats on the Microsoft Malware Prediction dataset. The research focuses on creating models for predicting advanced persistent threats, command-and-control exploit attacks, and industrial espionage. Experiment results show that LG, SVM does well at an accuracy level of 85%. The predictive analytics validated herein using ML will be used for revealing vulnerabilities that enhance the security of cyber supply chains. It recommends countermeasures for new threats like ransomware and spear-phishing in enhancing proactive defense mechanisms.