Graph Vector Variation Detection for Partial File Corruption
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Solution Overview
Problem
Cybersecurity attacks, particularly those employing partial data modification, are challenging to detect and evaluate due to their disruptive yet subtle effects on files, complicating the identification of affected data and the scope of the attack.
Innovation Solution
A data storage computing system employs vector-based analysis to identify variations in file content by generating vectors for file portions, comparing them to other portions, and determining actions based on threshold deviations to detect and mitigate data integrity anomalies indicative of malware or other corruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cybersecurity measures (software updates, network protocols, user training) are deployed, then protection against malware is improved, but detection of partial data modification attacks remains difficult
Solution Approach 1:
The system divides the file into multiple portions or segments and analyzes each segment individually using vector representations. This allows detection of partial modifications that affect only specific segments, making previously undetectable partial data corruptions visible to the security system.
Solution Approach 2:
The patent transforms file content into vector space representations, adding a dimensional aspect to file analysis. By comparing vectors in this transformed space rather than analyzing raw file content directly, the system can detect subtle semantic changes and partial modifications that traditional methods miss.
2Difficulty of detecting and measuring
If vector-based analysis is applied to detect data integrity anomalies, then detection capability is improved, but system complexity increases
Solution Approach 1:
The system creates vector representations (copies) of file portions rather than analyzing the original file content directly. These vector copies capture the essential characteristics of the data in a simplified format that is easier to compare and analyze, reducing the complexity of the analysis process while maintaining detection accuracy.
Solution Approach 2:
The patent changes the parameters used for file analysis from raw data characteristics to vector-based semantic representations. This parameter transformation enables more effective detection of data integrity anomalies while the vector comparison mechanism provides a standardized approach that manages system complexity through mathematical operations rather than complex algorithmic analysis.
Data Source
AI summary
Disclosed herein are methods, systems, and apparatus for the detection of data integrity anomalies indicative of malware for a datastore of an organization. To identify an anomaly in a file, a portion of a file is identified to be used in a vector comparison. The portion can comprise sentences or paragraphs for text files, entries, rows, or columns for spreadsheet files, or some other divisible portion of a file. A vector having multiple dimensions is generated for the portion based on the content in the portion. Each dimension of the multiple dimensions corresponds to a feature of the portion. A variation is determined between the vector and one other vector associated with one other portion of the file. One or more actions to take with respect to the file is determined based on the variation, such as malware mitigation, and the action is performed with respect to the file