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.

翻译：目前,全球范围内的流行病情况急剧增加。攻击者正在大量使用诸如特洛伊、间谍软件、根基、虫子、赎金软件等恶意软件。兰索姆软件是最臭名昭著的恶意软件,但我们没有防止或探测零天攻击的防御机制。该行业的大多数防御产品都依靠基于签名的机制或基于交通的异常检测。因此,研究人员正在采用机器学习和深层学习来开发一种基于行为的机制来检测恶意软件。虽然我们有一些混合机制,对可检测的执行进行静态和动态分析,但我们没有完整的概念检测证据,可用于开发针对赎金软件的完整证明产品。在这项工作中,我们开发了使用机器学习模型检测赎金软件概念的证明。我们已经对决策树、随机森林、KNN、SVM、XGBOst和物流倒退等若干机器学习模型进行了详细分析和比较效率。我们获得了98.21%的精确率,并对各种指标进行了评估,如精确度、回顾、TP、TN、F和FN。