Ransomware Detection and Prevention

Ransomware is a rapidly evolving cyber threat that encrypts user data and demands a ransom for its release, causing severe financial and operational disruptions. The following study integrates behavioral study, real-time file scrutiny, and signature- based methods into one comprehensive approach to combat ransomware attacks. The proposed system employs heuristic methods and machine learning techniques to identify unwanted accesses, encryption attempts, and other suspicious file activity. Furthermore, the method is anti- ransom proactive in that damage containment through process isolation and file backup is executed prior to the successful deployment of the ransomware. An interactive interface is presented in which infected files can be quarantined by checking them for malware and compromised data can be retrieved from safe storage. The results indicate that it is capable of mounting further regime against the ransomware as well as protecting the afflicted user or group, preserving the data, and saving a lot of money that may go into financial suffering.