Email Classification Using Obfuscation Detection and Regional Risk
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Solution Overview
Problem
Existing methods fail to effectively classify email addresses for malicious activity due to email obfuscation techniques like tumbling and private relays, and do not adequately consider regional risk factors, leading to incomplete data and biased predictions.
Innovation Solution
A system is developed to train machine learning models using features such as domain reputation, email tumbling detection, and hyperparameter tuning, with machine learning to predict risk, and adjust thresholds based on regional nuances to improve classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing classification methods are used, then the system is simple to operate, but classification accuracy deteriorates due to email obfuscation techniques
Solution Approach 1:
The patent introduces an intermediary data processing layer that detects and reverses obfuscation techniques (tumbling, private relays) before classification. This intermediary processing transforms obfuscated email addresses into their original forms, enabling accurate classification without requiring the classification system itself to become significantly more complex.
Solution Approach 2:
The system performs preliminary actions by pre-processing email addresses to detect and reverse obfuscation techniques before they are fed into the classification model. This preliminary detection and reversal of tumbling and private relay techniques ensures the classification model receives clean data, improving accuracy without adding complexity to the core classification function.
2Reliability
If regional risk factors are not considered, then the classification system is simpler, but prediction bias increases
Solution Approach 1:
The patent applies local quality by incorporating region-specific risk factors and adjusting classification thresholds based on geographic location. Different regions have different risk profiles, so the system tailors its evaluation criteria to local characteristics, improving prediction accuracy for each region while maintaining a unified overall system architecture.
Solution Approach 2:
The system dynamically adjusts classification thresholds and risk assessments based on regional nuances. Rather than using fixed thresholds, the system adapts its decision criteria according to the geographic context, allowing it to account for varying risk patterns across different regions without requiring a completely different system for each location.
3Reliability
If email obfuscation techniques are not addressed, then the system is easier to implement, but classification reliability deteriorates
Solution Approach 1:
The patent introduces an intermediary processing layer that specifically targets obfuscation techniques. This intermediary component detects tumbling patterns, private relay usage, and other obfuscation methods, reversing them to their original forms before classification. This approach improves reliability by ensuring accurate classification of obfuscated addresses without requiring the entire system to be redesigned.
Solution Approach 2:
The system replaces manual or simple string matching with automated machine learning-based detection and reversal of obfuscation techniques. By using ML models to identify and reverse tumbling and private relay patterns, the system achieves high reliability in handling obfuscated emails while maintaining ease of implementation through automated processing rather than complex manual rules.
Data Source
AI summary
The disclosure relates to methods and systems of email address classification. A system may generate features based on an email address. The features may include an identity-graph based feature that indicates a number of identity data points associated with the email address, an email tumbling based feature that indicates whether the email address has been tumbled, and a feature that indicates a recency of a domain associated with the email address. The system may vectorize the plurality of features to generate a feature vector. The system may execute, based on the feature vector, an email classification model comprising an ensemble machine learning model trained to classify email addresses according to the plurality of features of email addresses in training data and classification labels associated with the email addresses. The system may determine, based on execution of the email classification model, a classification of the email address.