  The current pandemic situation has increased cyber-attacks drastically
worldwide. The attackers are using malware like trojans, spyware, rootkits,
worms, ransomware heavily. Ransomware is the most notorious malware, yet we did
not have any defensive mechanism to prevent or detect a zero-day attack. Most
defensive products in the industry rely on either signature-based mechanisms or
traffic-based anomalies detection. Therefore, researchers are adopting machine
learning and deep learning to develop a behaviour-based mechanism for detecting
malware. Though we have some hybrid mechanisms that perform static and dynamic
analysis of executable for detection, we have not any full proof detection
proof of concept, which can be used to develop a full proof product specific to
ransomware. In this work, we have developed a proof of concept for ransomware
detection using machine learning models. We have done detailed analysis and
compared efficiency between several machine learning models like decision tree,
random forest, KNN, SVM, XGBoost and Logistic Regression. We obtained 98.21%
accuracy and evaluated various metrics like precision, recall, TP, TN, FP, and
FN.
