Abstract :

The Cyber Supply Chain (CSC) Consists Of Multiple Interconnected Subsystems, Each Performing Essential Tasks To Ensure Seamless Operations. Due To This Complexity, Safeguarding The CSC Is Highly Challenging, As Vulnerabilities Can Be Exploited At Any Stage, Potentially Leading To Severe Business Disruptions. To Mitigate These Risks, It Is Critical To Understand And Forecast Possible Threats And Implement Effective Protective Measures. Cyber Threat Intelligence (CTI) Provides Valuable Insights By Analyzing Adversary Behavior, Motivations, Tactics, Techniques, Procedures (TTPs), And Indicators Of Compromise (IoCs). In This Study, We Propose A Predictive Approach That Combines CTI With Machine Learning (ML) Models To Identify And Forecast Threats Within The CSC Environment. We Utilize The Microsoft Malware Prediction Dataset And Apply Various ML Algorithms—including Logistic Regression, Support Vector Machines, Random Forests, And Decision Trees—to Analyze Attack Patterns And Predict Vulnerabilities And IoCs.Experimental Results Highlight That Threats Such As Spyware, Ransomware, And Spear-phishing Are The Most Prevalent And Predictable In Cyber Supply Chains. Based On These Predictions, We Recommend Specific Control Measures To Strengthen Security. This Study Emphasizes The Importance Of Integrating CTI With ML For Proactive Threat Detection And Enhancing Cybersecurity In Supply Chain Networks. Keywords: Cyber Supply Chain (CSC), Cyber Threat Intelligence (CTI), Machine Learning (ML), Indicators Of Compromise (IoC), Tactics Techniques And Procedures (TTP), Malware Prediction, Cybersecurity, Threat Prediction

Published:

11-7-2025

Issue:

Vol. 25 No. 7 (2025)

Page Nos:

272-283

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite