A Novel Algorithm for Cyber Security Analysis for Supply Chain Security

Abstract

The Cyber Supply Chain (CSC) system is a multifaceted system with multiple subsystems carrying out distinct functions. The inherent vulnerabilities and threats from any portion of the system that can be exploited at any point in the supply chain make supply chain security difficult. This can cause a severe disruption on the overall business continuity. Thus, it is critical for organizations to comprehend and assess the risks in order to implement the appropriate supply chain security management measures. In order to analyse and forecast the risks based on the features of Cyber Threat Intelligence (CTI), we have combined CTI with Machine Learning (ML) approaches. To demonstrate the applicability of our approach, CTI data is gathered and a number of ML algorithms, i.e., Logistic Regression (LG), Support Vector Machine (SVM), Random Forest (RF) and Decision Tree (DT), are used to develop predictive analytics using the Microsoft Malware Prediction dataset. KEYWORDS: Cyber Supply Chain, Malware, Cyber Threat Intelligence, Machine Learning.