Automated Ransomware Data Recovery with Backup Health Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data recovery methods are inefficient in identifying healthy backups and prioritizing data restoration, leading to prolonged downtime and potential reinfection during ransomware attacks, as they rely on manual technician intervention and static disaster recovery plans that do not account for dynamic business needs.
Innovation Solution
Automated systems that identify healthy backups by analyzing metadata and usage patterns, and prioritize data restoration based on criticality, recent access, and business activities, using analytics and machine learning to select the most recent and least infected backups for immediate recovery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual technician intervention is used to identify healthy backups and prioritize data restoration, then expertise and judgment can be applied, but the process is time-consuming and increases human resource costs
Solution Approach 1:
The system performs self-service by automatically analyzing metadata, detecting ransomware infections, and prioritizing data restoration without requiring manual technician intervention. The automated system evaluates backup health status and determines restoration priorities based on pre-established business criticality criteria, eliminating the need for human experts to manually assess each backup and significantly reducing recovery time.
Solution Approach 2:
The patent replaces the mechanical system of manual technician assessment with an automated computational system that uses algorithms to analyze backup metadata, detect infections, and prioritize restoration. This substitution transforms the recovery process from a labor-intensive manual operation to an automated electronic system that can rapidly process and evaluate multiple backups simultaneously.
2Ease of manufacture
If static disaster recovery plans are used, then implementation is straightforward, but they do not adapt to dynamic business needs and changing data criticality
Solution Approach 1:
The system implements dynamic disaster recovery plans that automatically adapt to changing business needs by continuously evaluating data criticality based on metadata analysis and business rules. The restoration priorities are not fixed but dynamically adjusted based on the specific characteristics of each backup and the current business context, allowing the system to respond flexibly to varying recovery requirements without manual reconfiguration.
Solution Approach 2:
The patent applies parameter changes by modifying restoration priorities based on varying data characteristics and business criticality parameters. Instead of using a static recovery order, the system adjusts restoration parameters dynamically based on metadata analysis results, file types, access patterns, and business importance criteria, enabling the recovery process to adapt to different scenarios and business needs.
3Reliability
If all data is restored from the most recent backup, then recovery is comprehensive, but the risk of restoring infected data increases
Solution Approach 1:
The system performs preliminary action by detecting and identifying ransomware infections in backups before the restoration process begins. By analyzing metadata and evaluating backup health status in advance, the system can identify infected backups and exclude them from restoration, preventing the propagation of malware while still enabling comprehensive recovery of clean data from previous backup versions.
Solution Approach 2:
The patent applies the extraction principle by separating infected data from clean data through automated detection and analysis. The system extracts and identifies malicious components in backups, then excludes only the infected portions from restoration while still recovering healthy data, thereby maintaining comprehensive recovery of valid data while eliminating the risk of restoring infected files.
4Measurement precision
If extensive metadata analysis is performed to identify healthy backups, then accuracy of backup selection improves, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by performing metadata analysis selectively rather than exhaustively examining every file in every backup. The automated system analyzes key metadata parameters and uses this partial information to make informed decisions about backup health and restoration priorities, achieving sufficient accuracy for effective recovery without the excessive processing time that would result from complete forensic analysis of all backup data.
Data Source
AI summary
In the face of ransomware attacks, which can be increasingly difficult to effectively prevent, a solution can be considered to be the minimization of the cost and time taken to recover data and, hence business activities. Embodiments perform a restore operation that include automatically identifying the most recent healthy backup, from which data should be restored, and the prioritizing of the order in which data should be restored.