PDF Malware Detection Using Distilled Visual Threat Analysis
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Solution Overview
Problem
Existing malware detection techniques are inadequate in identifying sophisticated phishing attacks, particularly those using PDF documents, as they often rely on signature-based methods that can be evaded by attackers through redirection and deceptive content.
Innovation Solution
Employ an image-based deep learning approach to analyze PDF documents for malicious intent, utilizing knowledge distillation to enhance the detection of phishing threats by recognizing deceptive visual elements and traffic redirection patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If signature-based methods are used for malware detection, then detection speed is improved, but detection accuracy deteriorates due to evasion techniques
Solution Approach 1:
The patent replaces traditional signature-based mechanical detection methods with a deep learning-based image analysis system. The system converts PDF documents into images and uses convolutional neural networks to analyze visual patterns, thereby substituting the mechanical signature-matching approach with an intelligent visual recognition system that can detect sophisticated phishing attacks while maintaining detection speed.
Solution Approach 2:
The patent introduces an intermediary image conversion process between the PDF document and the detection algorithm. By converting PDFs into images, the system creates an intermediate representation that preserves visual characteristics of phishing content while enabling the application of image-based deep learning techniques, thus bridging the gap between document analysis and visual pattern recognition.
2Measurement precision
If advanced deep learning analysis is used to improve detection accuracy, then detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the detection process into distinct stages: PDF-to-image conversion, image preprocessing, deep learning analysis, and result interpretation. This segmentation allows each component to be optimized independently, managing computational complexity by breaking down the complex task into smaller, more manageable processing steps that can be executed efficiently.
Solution Approach 2:
The patent transitions from analyzing PDF documents in their native structured format to analyzing them as images in a visual dimension. This dimensional change enables the application of image-based deep learning techniques that can capture visual patterns and characteristics of phishing content, improving detection accuracy while leveraging established image processing algorithms.
3Reliability
If image-based analysis is used to detect phishing content, then false positives are reduced, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by converting PDF documents into images and preprocessing them before the main deep learning analysis. This preliminary processing prepares the data in advance, enabling faster and more accurate detection during the actual analysis phase, thereby reducing the overall processing time while maintaining low false positive rates through improved feature extraction.
Data Source
AI summary
The detection of malicious documents using knowledge distillation assisted learning is disclosed. A document is received for maliciousness determination. A likelihood that the received document represents a threat is determined. The determination is made, at least in part, using a raw bytes model that was trained, at least in part, using image model prediction probabilities. A verdict for the document is provided as output based at least in part on the determined likelihood.