ROFBS$α$: Real Time Backup System Decoupled from ML Based Ransomware Detection

This study introduces ROFBS$α$, a new defense architecture that addresses delays in detection in ransomware detectors based on machine learning. It builds on our earlier Real Time Open File Backup System, ROFBS, by adopting an asynchronous design that separates backup operations from detection tasks. By using eBPF to monitor file open events and running the backup process independently, the system avoids performance limitations when detection and protection contend for resources. We evaluated ROFBS$α$ against three ransomware strains, AvosLocker, Conti, and IceFire. The evaluation measured the number of files encrypted, the number of files successfully backed up, the ratio of backups to encrypted files, and the overall detection latency. The results show that ROFBS$α$ achieves high backup success rates and faster detection while adding minimal extra load to the system. However, defending against ransomware that encrypts files extremely quickly remains an open challenge that will require further enhancements.