A Supervised Machine Learning Ransomware Host-Based Detection Framework

Today, the term ransomware is frequently used in cybercrime headlines, its consequences have been on rise leaving a trail of terrible losses wake. Both people and businesses victimized by ransomware, costing victims millions dollars ransom payments. In addition, who were unable to pay or decrypt data experienced losses. This study uses dynamic malware analysis artifacts supervised machine learning detect at host level. It takes thorough examination operational specifics suggests machine-learning approach detection using various features derived from analysis. According findings, Logistic Regression algorithm model with 97.7% accuracy score offers 99% success rate detection. demonstrates how well work together activity Systems security administrators can mitigate risks this method.