In recent years, ransomware type malware has proven to be a security threat to businesses and individuals, this is because the detection and prevention methods are insufficient and the variants of ransomware act differently due to different attack vectors used to compromise a computer, so understanding the behavior and operation of the large number of variables is complicated, in this research were taken 22 representative samples and were tested in a controlled environment in order to understand the process of the life cycle of ransomware. The methodology proposed for the detection and prevention of ransomware was elaborated with the existing scientific bases of the different methods of detection and prevention ransomware and supported by a tool or software developed in the programming language Python and the scalable logical framework called (Malice) minimizing the negative impact that ransomware has in companies or homes. The research presents the formulation of a methodological scheme based on the detection and prevention of crypto ransomware type badware, was developed by searching for existing methods determining the effectiveness in detecting and preventing ransomware. It began with the selection and characterization of the most common criteria and variables of ransomware through the dynamic analysis of the variants of ransomware that were used to know the origin and evolution of this badware. Once the behavior of ransomware was understood, the actions that combat the behavior patterns of each variant of ransomware were grouped together, and from there the conceptualization of the different methods of detection and prevention of ransomware began, achieving the design of the methodological scheme that brought together all the methods or actions for the detection and prevention of ransomware on a workstation. Finally, the development of a software based on some of the methods proposed in the methodological scheme was started. We also assessed the effectiveness of the detection and prevention method with respect to the established behavior patterns. The proposed solution generated new mechanisms for the prevention and detection of new types of crypto ransomware