Email Spoof Detection Using Brand Logo and Sender Authorization
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Solution Overview
Problem
Existing email security platforms struggle to effectively detect and mitigate spoofed emails that impersonate legitimate brands, particularly those using unauthorized brand logos, as they evolve in frequency and sophistication, making it difficult for providers to maintain security mechanisms at the same pace.
Innovation Solution
An email security system that processes incoming emails to identify the claimed source entity, verifies sender authorization using DNS registry records, and updates the email with indicators of authenticity, utilizing machine learning and computer vision to recognize brand logos and context, thereby distinguishing authorized from unauthorized communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If email security platforms use traditional detection methods, then they can identify obvious spoofed emails, but they struggle to detect sophisticated emails using unauthorized brand logos and evolve at the same rate as attacks
Solution Approach 1:
The patent replaces traditional mechanical/Rule-based email security detection systems with an AI/ML-based system that uses computer vision to analyze email images and detect unauthorized brand logos. The system employs deep learning models (such as ResNet, VGG, or EfficientNet) that automatically learn patterns from training data to identify spoofed emails, enabling adaptive detection of evolving attack methods without requiring manual rule updates.
Solution Approach 2:
The system changes the detection parameters from traditional text-based rules to image-based visual features. By extracting visual characteristics from email images using computer vision techniques and comparing them against learned patterns from training data, the system can detect sophisticated spoofing attempts that use brand logos, enabling reliable detection of evolving attack methods.
2Reliability
If email security systems implement comprehensive authentication verification, then they can improve security against spoofing, but the processing time and system complexity increase
Solution Approach 1:
The system performs preliminary action by pre-training deep learning models on large datasets of authorized and unauthorized brand logos before actual email processing. This pre-training enables the system to make rapid predictions during email processing without performing exhaustive analysis in real-time, thus improving authentication security while minimizing processing time delays.
Solution Approach 2:
The patent applies partial action by selectively analyzing only the image components of emails that appear to contain brand logos or suspicious content, rather than performing comprehensive analysis on all email attributes. This targeted approach improves authentication security for critical cases while reducing overall processing time by avoiding unnecessary analysis of clear-cut legitimate emails.
3Reliability
If email security platforms block all unauthorized emails, then they can prevent impersonation attacks, but they may also block legitimate emails and reduce deliverability
Solution Approach 1:
The system implements feedback mechanisms by continuously learning from both authorized and unauthorized email patterns through training data updates. The deep learning models adapt to new legitimate email formats and brand representations over time, allowing the system to maintain high protection against impersonation while reducing false positives that would block legitimate emails, thus improving deliverability.
Solution Approach 2:
The patent applies dynamics by making the authentication system adaptive and evolving rather than static. The deep learning models continuously update their understanding of authorized brand logos and email patterns based on training data, enabling the system to dynamically adjust its blocking decisions. This dynamic approach protects against new impersonation techniques while maintaining flexibility to accommodate legitimate email changes, improving both security and deliverability.
Data Source
AI summary
Techniques are described herein for detecting an authorization status for an email based on content included in that email. In embodiments, such techniques may comprise receiving an electronic communication associated with an origination address, identifying, within the electronic communication, information indicating a claimed source entity, determining, based on the information, a claimed source entity, determining a number of authorized addresses associated with the claimed source entity, determining whether the electronic communication is authorized by the claimed source entity based on whether the origination address is included within the number of authorized addresses, updating the electronic communication to include an indication of whether the electronic communication is authorized by the claimed source entity, and transmitting the electronic communication to its intended recipient.