File-Print Classification for Reliable File Type Identification
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Solution Overview
Problem
The rapid growth of data storage demand, exceeding the capacity for physical storage and transmission bandwidth, coupled with the limitations of existing file type identification methods that rely on file signatures, which can be unreliable or unavailable in modified or corrupted files, necessitates a new approach for identifying file types without relying solely on signature information.
Innovation Solution
A system and method utilizing statistical analysis to generate a 'file-print' from the binary distribution of files, which is then processed by a machine learning algorithm to predict the file type, enabling encoding decisions based on the predicted type, even in cases where file signatures are missing or unreadable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If file signature checking is used to identify file type, then the identification process is simple and fast, but the method becomes unreliable or unavailable when files are modified or corrupted
Solution Approach 1:
The patent introduces an intermediary statistical analysis process that examines binary distribution patterns of file contents. Instead of directly relying on file signatures, the system uses statistical characteristics of binary data as a mediator to infer file types, making the identification process more reliable for modified or corrupted files while maintaining reasonable complexity through automated analysis.
Solution Approach 2:
The patent replaces the mechanical file signature checking method with a statistical analysis approach. Instead of mechanically searching for specific signature bytes, the system substitutes this with statistical examination of binary distribution patterns, transforming the identification mechanism from a simple lookup to a more robust statistical inference process that handles corrupted files better.
2Quantity of substance
If additional physical storage capacity is added to meet data storage demand, then storage capacity increases, but the solution does not address the fundamental mismatch between data generation rate and storage capacity growth
Solution Approach 1:
The patent applies parameter changes by transforming file data into statistical representations (binary distribution patterns). This changes the parameter space from storing raw binary data to storing statistical characteristics, enabling more efficient storage and processing while maintaining the ability to identify and retrieve file types accurately.
Solution Approach 2:
The patent applies local quality by focusing statistical analysis on specific aspects of file data (binary distribution patterns) rather than processing entire files. This localized approach extracts essential characteristics for file type identification without requiring complete file storage or processing, improving storage efficiency while maintaining identification accuracy.
3Quantity of substance
If data compression is applied to increase storage capacity, then storage efficiency improves, but compression ratios decrease substantially for multi-media data or result in data degradation
Solution Approach 1:
The patent extracts essential statistical characteristics (binary distribution patterns) from file data without compressing the entire file. This extraction approach captures the essential information needed for file type identification while avoiding the data degradation issues of traditional compression, maintaining data integrity for the extracted features.
Solution Approach 2:
The patent creates statistical copies (binary distribution representations) of file data that preserve essential characteristics for identification purposes. These statistical copies serve as reliable representations that can be stored and analyzed without compromising the integrity of the original data, enabling efficient storage while maintaining identification accuracy.
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.