Image-Based Deep Learning for Phishing PDF Detection
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Solution Overview
Problem
Existing malware detection systems struggle to effectively identify and mitigate phishing attacks through deceptive PDF documents, particularly those using visual tricks and redirection techniques, leading to potential security breaches.
Innovation Solution
An image-based deep learning approach is employed to analyze PDF files for phishing threats, utilizing machine learning models to detect malicious patterns and behaviors within the documents, enabling real-time identification and prevention of phishing attempts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional malware detection systems are used to identify phishing PDFs, then the system structure remains simple and easy to implement, but the detection accuracy is insufficient and false positives increase due to evolving phishing techniques
Solution Approach 1:
The patent replaces traditional rule-based and signature-based detection mechanisms with deep learning-based image analysis. PDF documents are converted to images and analyzed using convolutional neural networks, substituting mechanical detection rules with intelligent visual pattern recognition to achieve higher accuracy against evolving phishing techniques
Solution Approach 2:
The patent introduces an image conversion intermediary step that transforms PDF documents into image format before analysis. This intermediary representation enables the application of computer vision techniques to detect phishing patterns that are difficult to identify through traditional text-based or structural analysis methods
2Measurement precision
If deep learning models are trained on large datasets to improve detection accuracy, then detection precision increases, but the training time and computational resources required increase significantly
Solution Approach 1:
The patent performs preliminary actions by pre-processing PDF documents into image format and pre-processing images during the training phase. This includes converting PDFs to images in advance and performing image enhancements such as resizing, normalization, and augmentation beforehand, which streamlines the training process and reduces computational overhead during model training
Solution Approach 2:
The patent applies image augmentation techniques that generate additional training samples through partial modifications of existing images. By creating augmented versions of training images with various transformations (rotation, scaling, cropping), the system effectively increases dataset size and improves model generalization without requiring proportionally more original training data
Data Source
AI summary
The detection of phishing Portable Document Format (PDF) files using an image-based deep learning approach is disclosed. A PDF document is received. A likelihood that the received PDF document represents a threat is determined, at least in part, by using an image based model that was previously trained, at least in part, using a plurality of images that were generated using one or more tools that collectively convert a set of given PDF document files to the respective plurality of images. A verdict for the PDF document is provided as output based at least in part on the determined likelihood.