Neural Network File Classification Using Unordered Bit Sequences
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Solution Overview
Problem
Existing file classification methods using neural networks are not robust against changes in file formats, as they rely on specific patterns that can be easily altered, leading to unreliable classifications.
Innovation Solution
Training neural networks to classify files by breaking down test files into bit sequences and using them in an unordered manner, particularly with recurrent neural networks, to focus on general structural features rather than specific patterns, enabling flexible and robust classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are trained using specific patterns or strings in files, then classification accuracy is improved for known file formats, but robustness against format changes deteriorates
Solution Approach 1:
The file is divided into multiple segments or chunks, and the neural network processes these segments individually. This segmentation approach allows the network to learn from various parts of the file structure without relying on a single specific pattern, thereby improving robustness while maintaining classification accuracy.
Solution Approach 2:
The training process uses multiple parameter variations of file formats, including different encodings, structures, and representations. By training with these varied parameters, the neural network learns to recognize essential file characteristics across different format variations, enhancing both accuracy and robustness simultaneously.
2Ease of manufacture
If traditional string-based classification methods are used, then simplicity of implementation is improved, but adaptability to new file formats deteriorates
Solution Approach 1:
The traditional mechanical string-matching classification system is replaced with a neural network-based system. This substitution maintains relative simplicity through automated training while providing superior adaptability to new file formats through the network's ability to learn from diverse data representations.
Solution Approach 2:
The neural network is pre-trained with comprehensive file format data before actual classification tasks. This preliminary training action enables the system to handle new file formats effectively without requiring complex runtime adjustments, balancing implementation simplicity with format adaptability.
Data Source
AI summary
Various embodiments of the teachings herein include a method for training a first neural network to classify files into file classes. An example includes: assigning each file of a plurality of test files to a file class; breaking down each of the files into bit sequences assigned to the previously associated file class; and training the neural network using the bit sequences.