PDF Malware Detection Using Knowledge Distillation and Image Cues
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Solution Overview
Problem
Existing malware detection methods struggle to effectively identify and mitigate phishing PDFs, particularly those using deceptive images and redirection techniques, leading to potential security breaches and data theft.
Innovation Solution
Employ an image-based deep learning approach to analyze PDF documents, utilizing knowledge distillation techniques to enhance malware detection by identifying malicious patterns and behaviors within the documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional malware detection methods are used, then the detection process is simple, but the detection accuracy is low and cannot effectively identify phishing PDFs
Solution Approach 1:
The patent segments the malware detection task into multiple specialized models: a document type identification model that analyzes file structure and metadata, and an image analysis model that processes embedded images. This segmentation allows each model to specialize in specific features, improving overall detection accuracy while maintaining manageable complexity through modular architecture
Solution Approach 2:
The patent introduces knowledge distillation as an intermediary process that transfers learned patterns from the complex multi-model analysis system to a simplified detection framework. The distillation process captures essential detection knowledge from the segmented models and embeds it in a compact form, achieving high accuracy without requiring the full complex system to be deployed
2Productivity
If knowledge distillation is used to simplify the model, then the detection speed improves, but the detection accuracy may be compromised
Solution Approach 1:
The patent performs preliminary action by pre-training specialized models on extensive malware datasets before deployment. The document type identification model and image analysis model are trained in advance to recognize various phishing patterns, so that during actual detection, the knowledge distillation process can quickly transfer this pre-acquired knowledge without sacrificing accuracy
Solution Approach 2:
The patent employs parameter changes by adjusting the distillation temperature parameter to control the balance between speed and accuracy. By optimizing distillation parameters and model architecture, the system achieves fast detection speeds while maintaining high accuracy through careful tuning of the knowledge transfer process
3Measurement precision
If multiple analysis models are deployed to improve detection accuracy, then the detection precision increases, but the computational resources and system complexity increase
Solution Approach 1:
The patent applies partial action by implementing a two-stage detection process: first, the lightweight document type identification model performs initial filtering to quickly identify suspicious documents, and only then is the more resource-intensive image analysis model activated. This partial deployment of multiple models reduces overall computational resource consumption while maintaining high detection precision through selective analysis
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.