A SYSTEM FOR DETECTING OAUTH 2.0 VULNERABILITIES RUNNING OVER HTTP/HTTPS IN SMART ENVIRONMENTS

With the growth in the spread of ransomware, this malware has become a major threat to businesses and computer users. Ransomware is a different kind of malware that can block the screen of infected computers and/or encrypt the files, and only release them for payment. Due to the evolution of the techniques of obfuscation of ransomware, it becomes more difficult to detect by antivirus software among others. Because of the financial return it provides, because in most attacks users make the payment because they do not have an information security policy and together with the lack of regular backups. The present work uses an approach in which it identifies and classifies types of ransomware using machine learning algorithms such as Naive Bayes, Support Vector Machines - SVM, and K-nearest neighbors KNN. In the end, it is expected that the samples presented can be correctly identified and classified, and that which algorithm has obtained the best result.