Machine Learning-Based Crypto Ransomware Detection Model On Windows Platforms

Abstract

Ransomware, an evolving and highly destructive form of malware, presents substantial challenges in terms of detection and prevention. Despite extensive research and the application of Machine Learning (ML) models, existing defense mechanisms have struggled to provide complete protection, as most ML models fall short of achieving perfect detection rates. The study aimed to achieve several objectives related to Crypto- Ransomware detection. Firstly, it involved an examination of current ML frameworks employed in this field and the identification of associated challenges. Subsequently, the study focused on the creation of a new machine learning model designed for the detection and analysis of Crypto-Ransomware. By capitalizing on the shared behavioral patterns exhibited by ransomware, the proposed model attains an impressive 98% accuracy in recognizing ransomware on Windows systems. Lastly, the developed model's effectiveness in identifying Crypto-Ransomware was assessed through validation processes. Through evaluating multiple classifiers, the study identifies the Random Forest algorithm as the optimal choice for the model. This research marks a notable advancement in robust ransomware detection, working towards mitigating the far-reaching impacts of Crypto ransomware, a pervasive cyber threat.