Enhancing Cyber Supply Chain Security through Prophetic Analytics for Cyber Threats

The Cyber Provide series system a multifaceted system with a multiple subsystems carrying out distinct functions. The potential weaknesses and risks originating out of a segment and arrangement that will manipulated at some stage in the provide series. make supply chain security difficult As a result, it is critical for organizations to comprehend and assess the risks in order to implement the appropriate supply chain security management measures. Using machine learning (ML) techniques in conjunction with cyber threat intelligence (CTI), we have been able To examine and predict potential risks using the characteristics of CTI. This makes it possible to pinpoint the innate CSC vulnerabilities and implement the proper countermeasures for an overall increase in cyber security. In order to show the practicality of our method, we collect CTI data and use the Microsoft viruses forecast dataset to produce prophetic investigation using several machine learning (ML) algorithms, The test considers the pounce and TTP as capture variable, while the measures of compromise and vulnerabilities are considered as output parameters, incorporating Decision Tree, Random Forest, Support Vector Machine.