Deep Mutual Learning for Phishing PDF Detection
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Solution Overview
Problem
Current malware detection techniques are inadequate in identifying and mitigating phishing PDFs and other document-based threats, as they often rely on outdated methods like fingerprint comparisons and YARA rules, which are ineffective against evolving phishing attacks that use redirection and lack hard-coded malicious links.
Innovation Solution
An image-based deep learning approach is employed to detect phishing PDFs by converting document pages into images and training a deep neural network to distinguish between benign and malicious files, minimizing false positives and adapting to new threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional malware detection methods (fingerprint comparisons, YARA rules) are used, then the detection process is simple and fast, but the detection accuracy is low and cannot identify evolving phishing attacks
Solution Approach 1:
The patent replaces traditional mechanical rule-based detection systems with a deep learning-based automated detection system. The system uses neural networks to automatically learn and identify phishing patterns from document images, substituting manual rule creation and fingerprint matching with automated machine learning models that can adapt to evolving threats without requiring explicit programming of detection rules.
Solution Approach 2:
The patent transforms the detection approach by changing the input parameters from structured data (text, metadata) to unstructured image data. By converting document pages into images and applying image-based deep learning techniques, the system can detect visual phishing indicators that traditional text-based methods miss, such as visual cloning of legitimate websites and subtle graphical manipulations.
2Reliability
If deep learning methods are used to improve detection accuracy, then the false positive rate is reduced, but the processing time and computational resources increase
Solution Approach 1:
The patent segments the document analysis process into distinct stages: converting document pages to images, extracting visual features using deep learning models, and making classification decisions. This segmentation allows the system to process only relevant visual elements rather than analyzing entire documents, reducing computational overhead while maintaining high detection accuracy and low false positive rates.
Solution Approach 2:
The patent applies partial action by using deep learning models to analyze only the critical visual elements of documents that are most indicative of phishing, rather than performing exhaustive analysis of all document content. The system focuses computational resources on key visual indicators such as headers, logos, and URL displays, achieving high reliability without requiring complete document processing.
3Adaptability or versatility
If image-based deep learning is used to detect phishing PDFs, then the ability to identify redirection techniques is improved, but the ease of operation and implementation is reduced
Solution Approach 1:
The patent implements a universal deep learning-based detection system that can identify multiple types of phishing techniques through a single platform. The image-based approach naturally handles various phishing methods including direct linking, URL redirection, and visual cloning, as all these techniques manifest as visual patterns in document images. This multi-functional system eliminates the need for separate detection mechanisms for different phishing types, simplifying the overall implementation despite the advanced technology used.
Data Source
AI summary
The detection of malicious documents using deep mutual 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 a mutual learning process in conjunction with training an image based model. A verdict for the document is provided as output based at least in part on the determined likelihood.