Email Campaign Domain Classification via Neural Feature Extraction
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Solution Overview
Problem
Current email protection tools are ineffective in preventing unwanted email campaigns due to the massive number of registered domains and the ease with which legitimate domain names can be spoofed, making it difficult to detect and eliminate spoofed domains.
Innovation Solution
The development of systems, methods, and computer program products for email campaign domain classification, which utilizes machine learning models to classify domains as 'campaign' or 'not campaign' by extracting and reducing domain features, training neural networks, and applying ensemble modeling to improve classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional email protection tools are used to filter spam, then email filtering is provided, but the tools are ineffective against spoofed domains due to the massive number of registered domains
Solution Approach 1:
The patent segments the domain analysis process into multiple independent features (domain age, registrar information, DNS records, email headers, sending patterns) that can be evaluated separately and combined. This segmentation allows the system to analyze specific suspicious characteristics without being overwhelmed by the total number of domains, improving detection effectiveness against spoofed domains.
Solution Approach 2:
The patent introduces an intermediary classification system that acts as a mediator between raw email data and final spam determination. This intermediary layer analyzes domain features and sends email characteristics to determine whether an email is part of a campaign, providing a bridge that enhances detection reliability without requiring direct comparison against all registered domains.
2Measurement precision
If feature extraction and reduction techniques are applied to domain data, then classification accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent extracts only the most relevant domain features (domain age, registrar, DNS records, email headers) from the complete domain data set, separating essential classification information from unnecessary data. This extraction process improves classification accuracy by focusing on discriminative features while reducing the computational burden of processing all available domain information.
Solution Approach 2:
The patent applies partial action by implementing feature reduction that selects a subset of the most important domain characteristics rather than analyzing all possible features. This partial analysis approach achieves sufficient classification accuracy without the excessive computational complexity of comprehensive feature analysis, balancing precision and system complexity.
3Productivity
If machine learning models are trained to classify domains as campaign or not campaign, then email campaign detection is improved, but false positive and false negative rates persist
Solution Approach 1:
The patent applies preliminary action by training the machine learning model on extensive labeled data before deployment, pre-adjusting classification thresholds and feature weights to minimize false positives and negatives. This preliminary training phase establishes a foundation of accuracy that improves detection efficiency while reducing error rates during actual email classification operations.
Solution Approach 2:
The patent implements feedback mechanisms where classification results are continuously evaluated and used to refine the machine learning model. By analyzing false positive and false negative cases and adjusting the model accordingly, the system improves measurement precision over time while maintaining high productivity in email campaign detection.
Data Source
AI summary
A domain processing system receives or collects raw data containing sample domains each having a known class identity indicating whether a domain is conducting an email campaign. The domain processing system extracts features from each of the sample domains and selects features of interest from the features, including at least a feature particular to a seed domain and features particular to email activities over a time line that includes days before and after a domain creation date. The features of interest are used to create feature vectors which, in turn, are used to train a machine learning model, the training including optimizing a neural network structure iteratively until stopping criteria are satisfied. The trained model functions as an email campaign domain classifier operable to classify candidate domains with unknown class identities such that each of the candidate domain is classified as conducting or not conducting an email campaign.