Backup Data Corruption Detection via Cyber-Security Module Analysis
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Solution Overview
Problem
Current methods for detecting and mitigating ransomware and cyber-attacks on backup data are costly, time-consuming, and often ineffective, allowing significant damage to occur before detection, especially since they are typically employed only when an attack is suspected.
Innovation Solution
A system that periodically analyzes backup data sets using a cyber-security module to detect and characterize corruption, allowing for informed decisions on purging or using corrupted data sets, and providing quick identification of potential ransomware attacks by determining the percentage of corruption and displaying it to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current methods for detecting and mitigating ransomware are used, then detection capability is provided, but the methods are costly, time-consuming, and often ineffective
Solution Approach 1:
The patent performs preliminary actions by calculating corruption percentages and characterizing backup data sets before actual ransomware attacks occur. The system proactively analyzes backup integrity, assigns risk scores, and prepares mitigation strategies in advance, rather than waiting for attacks to be suspected. This preliminary characterization enables rapid response when threats are detected.
Solution Approach 2:
The patent replaces traditional mechanical security scanning methods with a data-driven corruption analysis system. Instead of using complex security software to scan for malware signatures, the system substitutes a mathematical approach that calculates corruption percentages based on data characteristics, providing faster and more reliable detection without the overhead of traditional security mechanisms.
2Measurement precision
If comprehensive backup analysis is performed to detect corruption, then detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent changes the parameter of analysis from binary corruption detection (corrupted/not corrupted) to a continuous corruption percentage metric. This parameter transformation allows the system to measure the degree of corruption precisely while using efficient mathematical calculations that do not require exhaustive scanning, thereby maintaining high measurement precision without sacrificing processing speed.
Solution Approach 2:
The patent creates simplified copies or representations of backup data characteristics for analysis purposes. Instead of analyzing the entire backup data set in detail, the system generates corruption percentage metrics and risk scores that represent the overall state, enabling rapid assessment without processing every individual data element at full resolution.
3Adaptability or versatility
If backup data sets are retained for historical purposes, then data recovery options are improved, but storage space is consumed and potential corruption risk increases
Solution Approach 1:
The patent applies local quality by differentiating between various backup data sets based on their individual corruption percentages and risk scores. Instead of uniformly retaining or deleting all backups, the system characterizes each backup locally with specific metrics, allowing selective retention of high-quality backups and purging of corrupted ones. This enables optimized storage allocation where space is conserved by removing unnecessary backups while preserving recovery options for viable backups.
Data Source
AI summary
In general, one or more embodiments of the invention relates to systems and methods for performing a backup and later determining a level or percentage of corruption of the resulting backup set. By having a cyber-security module analyze the backup data periodically, corruption of backup data both caused by cyber-attacks or by hardware failures may be detected and characterized. By knowing how corrupted a particular corrupted backup data set is, an informed decision may be made with regards to purging the backup data set and/or using the backup data set or portion thereof in any further restorations. By making these determinations, a quick identification of possible ransomware attacks may be made, and additional degradation of a user's data may be avoided.