File Format Identification Using Byte N-gram Embeddings
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Solution Overview
Problem
Conventional systems face difficulties in identifying file formats associated with binary data, especially when metadata is missing, altered, or when files are partial, corrupted, or formatted with multiple formats, as they rely on hard-coded parsers that struggle with incomplete or obfuscated data.
Innovation Solution
A file format identification system that uses byte n-grams, such as 4-grams, extracted from binary data to generate embedding arrays, which are then compared by a trained classifier to predict the likely file formats, even in cases of missing or altered metadata, partial files, or multiple formats, using a neural network-based embedding model and classifier.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional hard-coded parsers are used to identify file formats, then the system works well for complete and standard files, but it fails when metadata is missing, altered, or when files are partial or corrupted
Solution Approach 1:
The patent transforms the file format identification approach from relying on fixed metadata parameters to using n-gram frequency distributions. By extracting byte sequences (n-grams) from the binary data and analyzing their statistical patterns, the system adapts to various file conditions including partial or corrupted files where traditional metadata may be missing or altered.
Solution Approach 2:
The patent replaces the mechanical hard-coded parser system with a machine learning-based statistical analysis system. Instead of using rigid rule-based parsing that checks for specific metadata signatures, the system uses trained models to recognize file formats through probabilistic patterns in n-gram distributions, enabling more flexible and robust format identification.
2Ease of manufacture
If hard-coded parsers are used for file format identification, then the implementation is simple and direct, but the system cannot handle files with multiple formats or obfuscated data
Solution Approach 1:
The patent performs preliminary extraction of n-grams from the binary data before analysis. By pre-processing the data to extract and count n-gram frequencies, the system creates a statistical foundation that can be used by trained machine learning models to identify file formats even when metadata is compromised or files contain multiple formats.
Solution Approach 2:
The patent moves the analysis from the traditional single-dimension approach of checking metadata fields to a multi-dimensional statistical space defined by n-gram frequency distributions. This dimensional transformation allows the system to capture subtle patterns in the binary data that indicate file formats, providing robustness against metadata manipulation or loss.
3Productivity
If traditional metadata-based identification is used, then file formats can be quickly identified for standard files, but the method fails when metadata is intentionally altered or missing
Solution Approach 1:
The patent extracts n-grams (byte sequences) from the binary data as feature representations for file format identification. By taking out these sequential patterns and analyzing their frequency distributions, the system creates a robust feature set that reflects the underlying structure of the file format without relying on potentially compromised metadata fields.
Solution Approach 2:
The system uses trained machine learning models that have learned from labeled training data to interpret n-gram frequency patterns. The feedback mechanism is embedded in the trained model's ability to recognize patterns associated with specific file formats, allowing accurate identification even when traditional metadata-based approaches would fail due to altered or missing information.
Data Source
AI summary
A file format identification system can predict file formats associated with binary data. The file format identification system can extract n-grams, such as byte 4-grams, from the binary data. A trained neural network with at least one embedding layer can generate embedding arrays that correspond to the extracted n-grams. A trained file format classifier can compare values in the embedding arrays with patterns of values associated with known file formats. The trained file format classifier can accordingly determine which of the known file formats are most likely to be associated with the binary data.