Abstract
Ransomware is malicious software that encrypts data before demanding payment to unlock them. The majority of ransomware variants use nearly identical command and control (C&C) servers but with minor upgrades. There are numerous variations of ransomware, each of which can encrypt either the entire computer system or specific files. Malicious software needs to infiltrate a system before it can do any real damage. Manually inspecting all potentially malicious file types is a time-consuming and resource-intensive requirement of conventional security software. Using established metrics, this research delves into the complex issues of identifying and preventing ransomware. On the basis of real-world malware samples, we created a parameterized categorization strategy for functional classes and suggestive features. We also furnished a set of criteria that highlights the most commonly featured criteria and investigated both behavior and insights. We used a distinct operating system and specific cloud platform to facilitate remote access and collaboration on files throughout the entire operational experimental infrastructure. With the help of our proposed ransomware detection mechanism, we were able to effectively recognize and prevent both state-of-art and modified ransomware anomalies. Aggregated log revealed a consistent but satisfactory detection rate at 89%. To the best of our knowledge, no research exists that has investigated the ransomware detection and impact of ransomware for PureOS, which offers a unique platform for PC, mobile phones, and resource intensive IoT (Internet of Things) devices.