An Enhanced Ransomware Defense Strategy through Behavior-Based Detection using Machine Learning Algorithms

This paper investigates the topic of Ransomware Defense Strategy through Behavior-Based Detection (BBD) using Machine Learning (ML) and propose a novel solution to the limitation of the existing approaches which were previously proposed for ransomware detection however they are facing challenges of high false positives during detection and cannot detect zero-day attacks or unknown threats. The importance of ransomware defence strategy using the BBD approach provides a proactive, adaptive, and comprehensive solution to the evolving threat of ransomware. The research aims to bridge the gap in current knowledge by proposing a Ransomware Defense Strategy through BBD using ML algorithm approach. By combining these techniques, the paper aims to reduce high false positive rates and improves real-time detection and response capabilities, ensuring rapid identification and containment of threats and address the issue of zero day attacks. The research methodology will involve conducting a simulations to evaluate enhanced ransomware defense strategy using BBD and ML algorithms within a hypervisor environment approach. A relative analysis will be put forward to prove how the proposed solution is unparalleled and superior to the existing methods. The results of the simulations will provide empirical evidence of the effectiveness of the Ransomware Defense Strategy through BBD using ML in improving ransomware detection. The findings of this paper will be suitable for any network analyst and researcher to determine the appropriate set of handover techniques for ransomware detection under various circumstances, such as feasibility and cost