AI Malware Detection Using Multi-Image File Classification
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Solution Overview
Problem
Existing malware detection methods, particularly static signature matching and dynamic analysis, struggle to keep up with the rapid evolution of malware signatures and are computationally intensive, and fail to effectively handle files of varying sizes and structures, while obfuscation techniques complicate detection.
Innovation Solution
A method involving converting computer files into multiple image types using machine learning, specifically deep learning, to classify malware through static analysis, utilizing image classification modules and a classifier module to evaluate the images, ensuring uniform image sizes and random data subset selection for robust detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static signature matching is used for malware detection, then detection speed is improved, but detection accuracy deteriorates due to exponential growth of malware signatures
Solution Approach 1:
The patent replaces the traditional mechanical signature matching system with a neural network-based image classification system. Computer files are converted into image representations, and deep learning models classify these images to detect malware, substituting the manual signature comparison process with automated neural network analysis that adapts to new malware patterns without requiring explicit signature updates
Solution Approach 2:
The patent transforms the detection parameters by converting file data into image space representations. Instead of analyzing raw file bytes or traditional features, the system transforms files into visual representations where malware patterns become detectable through image classification techniques, changing the fundamental parameter space from binary data to image features
2Measurement precision
If dynamic analysis is used for malware detection, then detection accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The patent performs preliminary transformation of computer files into image representations before classification. This pre-processing step converts files into a standardized image format that captures essential structural features, enabling the neural network to perform accurate classification without requiring resource-intensive dynamic execution or analysis of the actual file contents
Solution Approach 2:
The patent creates an image copy or representation of the original computer file for analysis. Instead of directly analyzing or executing the original file, the system works with an image-based copy that preserves structural characteristics while allowing efficient neural network processing, thus avoiding the computational overhead of dynamic analysis
3Productivity
If traditional static analysis methods are used, then computational efficiency is maintained, but adaptability to varying file sizes and structures deteriorates
Solution Approach 1:
The patent creates a universal image-based representation system that handles diverse file types and sizes through a common processing pipeline. The neural network model trained on image data can process various file formats (PDF, Office documents, etc.) by converting them all into standardized image representations, providing multi-functional capability without requiring separate analysis methods for each file type
Solution Approach 2:
The patent transitions from analyzing files in their native dimensional space (binary data, text, structured formats) to a two-dimensional image space. This dimensional transformation allows the neural network to perceive file structures and patterns in a visual format, enabling consistent processing of files with varying sizes and structures through a unified image-based approach
4Measurement precision
If signature matching is updated frequently to keep up with malware evolution, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent implements a self-learning neural network system that automatically adapts to new malware patterns through continuous training on image data. Instead of requiring manual signature updates and system reconfiguration, the model autonomously improves its detection capabilities by learning from new examples, eliminating the complexity of frequent signature management and system updates
Data Source
AI summary
For improving the efficiency of malware detection, a method is proposed that can handle computer files (1) of varying types and sizes and at the same time maintain a high detection performance by classifying a number of different types (A, B, C, D, E) of images (4), each calculated or derived from a particular computer file under test (1) (CFUT), using artificial intelligence methods such as machine learning, in particular deep learning, for example as provided by neuronal networks (24) or supervised deep learning algorithms. The different image types (A, B, C, D, E) are generated using different image conversion techniques and a number of approaches are presented for computing images (4) of uniform size Si that contain relevant information for classifying the CFUT (1).