A File-Level Continuous Data Protection Scheme for Enforcing Security Baseline

Massive data is the basis of machine learning, and continuous data protection is an effective means to ensure data integrity and availability. At present, continuous data protection adopts the same backup strategy in different host security environments, ignoring the potential relationship between host security state and data destruction, resulting in a low utilization rate of backup storage space. To make better use of backup storage space and improve data recovery speed, this paper proposes a file-level continuous data protection scheme for enforcing security baseline (SB-CDP). SB-CDP combines a security baseline with continuous data protection to dynamically adjust the backup strategy based on the host security status provided by the security baseline. The experimental results show that, on the one hand, SB-CDP can effectively utilize the backup storage space, on the other hand, it can effectively improve the data recovery speed and reduce the impact on the system performance.