Statistical File-Print Analysis for Corruption-Resistant Type Detection
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Solution Overview
Problem
Current data storage technologies face challenges in keeping pace with rapidly increasing data demand due to limited storage capacity and transmission bandwidth, and existing file type identification methods rely heavily on unreliable file signatures, especially for modified or corrupted files.
Innovation Solution
A system and method for file type identification that uses statistical analysis to generate a 'file-print' from binary distributions, which is then processed by a machine learning algorithm to predict the file type without relying on file signatures, enabling encoding and decoding of files based on predicted types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If file signature checking is used for file type identification, then the identification process is simple and fast, but the reliability is poor when files are modified or corrupted
Solution Approach 1:
The system performs preliminary statistical analysis on file contents to generate binary distribution data before machine learning classification. This pre-processing step creates a robust feature set that enables reliable file type identification even when file signatures are modified or corrupted, resolving the contradiction between reliability and complexity by preparing data in advance for the classification process
Solution Approach 2:
The patent introduces binary distribution analysis as an intermediary between the raw file contents and the machine learning classifier. This intermediary layer transforms file data into statistical representations that capture essential characteristics while being resistant to signature modifications, thereby improving reliability without requiring direct access to potentially corrupted signature fields
2Quantity of substance
If additional physical storage capacity is added, then storage demand is temporarily met, but the problem persists as data growth outstrips manufacturing capacity
Solution Approach 1:
The patent applies parameter changes by transforming file data from its original binary form into statistical parameters (binary distribution characteristics). This transformation enables more efficient data representation and compression, allowing storage systems to handle growing data volumes more effectively by changing the fundamental parameters of data storage rather than simply adding more capacity
3Quantity of substance
If data compression is applied, then storage capacity is doubled, but compression effectiveness decreases for multi-media data types
Solution Approach 1:
The system applies local quality by using machine learning to identify specific file type characteristics and apply appropriate statistical analysis methods to different data regions. This enables optimized compression and analysis for different media types (text, images, audio, video) by tailoring the approach to local data characteristics rather than applying a uniform compression method, thereby maintaining effectiveness across diverse data types
Data Source
AI summary
A system and method for file type identification involving extraction of a file-print of a file, the file-print being a unique or practically-unique representation of statistical characteristics associated with the distribution of bits in the binary contents of the file, similar to a fingerprint. The file-print is then passed to a machine learning algorithm that has been trained to recognize file types from their file-prints. The machine learning algorithm returns a predicted file type and, in some cases, a probability of correctness of the prediction. The file may then be encoded using an encoding algorithm chosen based on the predicted file type.