Machine Learning File Type Identification for Corrupted Files
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Solution Overview
Problem
The rapid growth of data storage demand outstrips the capacity to store it, and existing methods like data compression and adding physical storage are inadequate, while file type identification based on file signatures is unreliable for modified or corrupted files.
Innovation Solution
A system and method using machine learning to identify file types by comparing statistical similarities in binary distributions, generating a 'file-print' for unknown files, and using a trained algorithm to predict the file type without relying on file signatures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If file type identification is performed using file signature checking, then the identification process is simple and fast, but the reliability is poor for modified or corrupted files
Solution Approach 1:
The patent segments the file analysis process into two distinct approaches: traditional file signature checking for intact files, and machine learning-based binary distribution analysis for modified or corrupted files. This segmentation allows the system to select the appropriate method based on file condition, improving overall reliability while managing complexity through modular design.
Solution Approach 2:
The patent introduces machine learning algorithms as an intermediary between the file contents and type identification. Instead of directly comparing file signatures, the system uses trained ML models to analyze binary distributions and predict file types, serving as a mediator that handles cases where traditional signature matching fails.
2Quantity of substance
If data compression is used to increase storage capacity, then storage capacity is doubled, but the solution is inadequate for multi-media data and results in data degradation
Solution Approach 1:
The patent employs lossless compression techniques that, while providing moderate storage savings (2:1 ratio), ensure complete data retention without degradation. The system accepts the limitation of moderate compression ratios in exchange for preserving full data integrity, treating compression as a temporary intermediary step rather than a permanent reduction.
Solution Approach 2:
The patent changes the approach from focusing on compression ratio to focusing on file type identification accuracy. By accurately identifying file types through machine learning analysis of binary distributions, the system can select appropriate encoding and storage methods that preserve data integrity while optimizing storage efficiency for different data types.
3Productivity
If transmission bandwidth is increased to handle large data sets, then data transmission capability is improved, but transmission bandwidth becomes increasingly a bottleneck
Solution Approach 1:
The patent changes file formats and encoding parameters based on identified file types to optimize transmission efficiency. By accurately categorizing files using machine learning, the system can apply type-specific compression and encoding schemes that reduce overall bandwidth requirements while maintaining data integrity.
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.