Ransomware Resilient Database Backup and Detection System
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Solution Overview
Problem
Current systems lack effective methods to detect and remediate database data affected by ransomware attacks, which can lead to data loss and the need for costly ransom payments.
Innovation Solution
A system comprising a database backup handler, a ransomware detector, and a ransomware remediator that monitors database changes, uses deep learning and machine learning to detect ransomware attacks, and restores data to a previous state using snapshots or database logs, thereby preventing data loss without paying a ransom.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ransomware detector monitors all database changes to detect attacks, then detection capability is improved, but system performance and resource consumption worsen
Solution Approach 1:
The system segments database changes into different categories (transaction logs, snapshots, backup data) and processes them through different detection mechanisms. Rule-based detection handles structured log data while machine learning handles unstructured backup data, allowing parallel processing that maintains both detection accuracy and system performance.
Solution Approach 2:
The patent introduces an intermediary layer (backup data and transaction logs) that decouples the detection process from direct database operations. The ransomware detector analyzes these intermediary artifacts rather than monitoring every database operation in real-time, reducing overhead while maintaining detection effectiveness.
2Measurement precision
If multiple detection methods (rule-based and machine learning) are used, then detection accuracy is improved, but system complexity worsens
Solution Approach 1:
The detection system is segmented into distinct rule-based and machine learning components that operate independently on different data types. Rule-based detection processes structured transaction logs while machine learning models process unstructured backup data, allowing each component to be optimized separately and reducing overall system complexity.
Solution Approach 2:
The system uses a universal backup mechanism that serves multiple functions: data protection, change tracking, and detection training data generation. The same backup infrastructure supports both rule-based and machine learning detection methods, reducing the need for separate specialized systems.
3Reliability
If frequent database snapshots are taken for backup, then data recovery capability is improved, but storage requirements and system overhead worsen
Solution Approach 1:
The system applies different backup frequencies and retention policies to different parts of the database based on their importance and change patterns. Critical tables receive more frequent snapshots with longer retention, while less critical data uses less frequent backups, optimizing storage usage while maintaining adequate recovery capability.
Solution Approach 2:
The backup system dynamically adjusts snapshot frequency and retention parameters based on detected threat levels and database activity patterns. During normal operation, backup frequency is reduced to minimize storage overhead, while detection of suspicious activity triggers increased backup frequency and extended retention periods.
Data Source
AI summary
A system for protecting a database against a ransomware attack includes a database backup handler configured to selectively output database backup data associated with a database to a storage device. A ransomware detector is configured to monitor changes to the database and to detect data changes to the database resulting from a ransomware attack. A ransomware remediator communicates with the ransomware detector and the database backup handler and is configured to restore data in the database to a point prior to the ransomware attack based upon the backup data in the storage device.