Image File Section Analysis with Deep Learning for Malware Detection
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Solution Overview
Problem
Existing malware detection techniques are inadequate in identifying malicious image files, as they are often disguised within benign image formats, leading to potential security breaches.
Innovation Solution
A deep learning approach is employed to analyze image files by categorizing them into datasets and training a neural network to distinguish between benign and malicious images, utilizing specific sections of the file format to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional malware detection techniques are used, then detection simplicity is maintained, but detection accuracy for malicious image files deteriorates
Solution Approach 1:
The patent divides the image file into multiple sections (header, image data, trailing data) and analyzes each section separately using different techniques. The header and image data are analyzed using traditional image processing, while trailing data is analyzed using machine learning models trained to detect malicious patterns, allowing targeted complexity application where most needed
Solution Approach 2:
The patent introduces machine learning models as intermediary components between the raw image data and the detection decision. These models serve as mediators that process file sections and provide probabilistic assessments, improving detection accuracy while isolating the complexity within specific modular components rather than throughout the entire system
2Measurement precision
If deep learning analysis is applied to all image sections, then detection accuracy improves, but processing time increases
Solution Approach 1:
The patent segments the image file analysis into different sections processed by different methods. Low-complexity sections (header, main image data) are processed quickly using traditional methods, while only specific trailing data sections are subjected to computationally intensive deep learning analysis, reducing overall processing time while maintaining high detection accuracy for malicious content
Solution Approach 2:
The patent applies deep learning analysis selectively to specific portions of the file (trailing data sections) rather than the entire image. This partial application of the more intensive analysis method achieves sufficient detection accuracy without the full time cost of analyzing every byte, balancing precision and speed
Data Source
AI summary
Techniques for using deep learning to identify malicious image files are disclosed. A plurality of sections of a first image are received. The received sections are used to determine a likelihood that the first image is malicious. The determination is made, at least in part, using a model trained using a set of sections extracted from a set of sample images. A verdict is provided for the first image.