Ransomware Mitigation for Secure and Smart Cloud Manufacturing Using GeniLeaf Decision Tree with Load Balancing

Abstract

Background: Cyber-attacks related to ransomware have increased significantly in cloud manufacturing industries over the last decade, causing considerable disruptions to organizations. This type of information may also include personal details, patent rights, bank account details, etc. This type of malware requires new and better mitigation methods. Objective: The primary objective of this study was to provide an algorithm to execute experiments using genetic algorithms for load balancing with decision trees and mitigate ransomware. Methods: Hybrid analysis and machine learning techniques were used in this study to identify ransomware. Since a wide range of samples impacted by ransomware share most of the characteristics, it may be possible to use this study to detect current and future malware variants in industries. Results: In patented industrial technology, ransomware mitigation plays a crucial role based on the analysis of various papers and patents-to-science references. In this study, a machine learning mitigation algorithm, GeniLeaf Decision Tree (GLDT), was applied to a featured dataset using a genetic algorithm with a decision tree Conclusion: Machine learning and load balancing are used to gain insight into ransomware behavior. By using the proposed approach to mitigate ransomware and spoofing patterns, a high level of accuracy is achieved. Using GeniLeaf Decision Trees to mitigate ransomware is a significant innovation.