Abstract: Provided is a computer agent and method to detect and prevent ransomware attacks early without losing customer data, without polluting the customer's file system or consuming any space on a storage device. The computer agent installs a kernel mode layered file system driver which is attached to encryption level group of a filter manager. With this layered file system filter driver design, it creates virtual canary files in directories which appear like normal files to any user or application though these files will not reside on disk. These virtual canary files are spread as baits across the file system to detect and prevent any malicious attack from any process or application. So that the Virtual Canary Files do not consume a large amount of memory, internal structures representing Virtual Canary Files are placed in virtual memory, but actual contents of the Virtual Canary Files do not reside in virtual memory. Other embodiments disclosed.

Abstract: An approach is provided for automating an adoption of cloud services. A machine learning model is devAppliceloped to learn application source code accessed from a source code repository. The machine learning model is trained by building training data that includes the application source code and technical labels. Services are extracted from the application source code and a context of the application source code is derived. Using a decision tree model, a mapping of the extracted services to respective cloud services is determined. Based on the mapping, portions of the application source code are identified that are recommended to be replaced by the respective cloud services.