Multi-Modal Email Models for Text-and-Image Threat Detection
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Solution Overview
Problem
Existing email security systems struggle to effectively detect sophisticated malicious email attacks due to the complexity of modern markup languages and the rapid evolution of attack methods, leading to inefficiencies in security mechanisms.
Innovation Solution
An email security system that utilizes a combination of text and image encoder machine learning models to analyze both textual and visual data from email payloads, generating holistic representations to predict maliciousness, thereby improving detection accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional email security scanning methods are used, then the system can process emails quickly, but the detection accuracy fails to keep pace with sophisticated malicious attacks
Solution Approach 1:
The email security system segments the email into multiple components (text content, HTML markup, embedded images, links, attachments) and processes each component separately through specialized analysis modules. This segmentation allows the system to apply different detection techniques to different parts of the email, improving overall detection accuracy while managing complexity through modular processing
Solution Approach 2:
The system transitions from traditional single-dimension text-based analysis to multi-dimensional analysis by incorporating visual rendering of HTML content, analyzing markup structure separately from rendered output, and examining multiple email components simultaneously. This dimensional expansion enables detection of sophisticated attacks that evade traditional scanners
2Measurement precision
If sophisticated multi-modal analysis is implemented, then detection accuracy improves, but processing speed decreases
Solution Approach 1:
The system performs preliminary actions by pre-processing and rendering HTML content into visual representations before the actual security analysis. This preliminary rendering allows the analysis phase to work with pre-computed visual data rather than raw HTML, speeding up the detection process while maintaining multi-modal analysis capabilities
Solution Approach 2:
The system employs self-service mechanisms through automated multi-threaded processing where different email components are analyzed in parallel. The rendering engine, text analyzer, and image processor operate simultaneously on different segments of the email, maintaining high processing speed while performing sophisticated multi-modal analysis
3Reliability
If comprehensive email analysis is performed on all components, then detection effectiveness increases, but computational resources are overconsumed
Solution Approach 1:
The system applies local quality by directing different levels of analysis to different email components based on their risk profiles. High-risk elements like suspicious links and attachments receive intensive multi-modal analysis, while low-risk text content receives lighter processing. This localized quality adjustment maintains detection effectiveness while reducing overall computational resource consumption
Data Source
AI summary
In some aspects, the techniques described herein relate to a method for detecting malicious emails, the method including: receiving an email, wherein the email is associated with a markup payload; determining, based on the markup payload, text data associated with the email; determining, using the text data and a first machine learning model, a first representation of the email representing text associated with the email; rendering the email to generate image data that represents a rendering of the email; determining, using the image data and a second machine learning model, a second representation of the email that represents at least the rendering of the email; and determining a prediction for the email based on the first representation and the second representation, wherein the prediction represents whether the email is predicted to be malicious based on the first representation and the second representation.