Spam Detection via Metadata Clustering and Rating
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Solution Overview
Problem
Current spam detection methods often result in false positives, leading to legitimate messages being misclassified as spam or vice versa, causing inconvenience and security risks due to the high volume of spam messages overwhelming user mailboxes and potential phishing threats.
Innovation Solution
A system that classifies electronic messages by clustering metadata based on similarity, using hash sums and IP addresses, and assigns ratings to clusters, with thresholds to determine whether messages are spam or legitimate, and quarantines messages with uncertain ratings for further evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional spam detection methods are used, then spam filtering is performed, but false positives increase causing legitimate messages to be misclassified
Solution Approach 1:
The patent segments the spam detection process into multiple independent analysis dimensions: content analysis, sender analysis, metadata analysis, and clustering analysis. Each dimension processes specific features separately and contributes to the overall spam score, allowing the system to avoid false positives by requiring consensus across multiple dimensions rather than relying on a single classification criterion
Solution Approach 2:
The system dynamically changes classification parameters by adjusting spam thresholds and weighting factors based on cluster ratings and message characteristics. The spam score calculation uses variable weights for different features (content, sender, metadata) that are adjusted according to the cluster's overall spam probability, enabling adaptive precision while maintaining reliability
2Productivity
If spam detection sensitivity is increased to catch more spam, then spam filtering effectiveness improves, but false positives increase causing legitimate messages to be blocked
Solution Approach 1:
The patent introduces an intermediary mechanism in the form of a cluster-based rating system that mediates between individual message evaluation and final classification. Messages are first grouped into clusters, and the cluster's overall rating acts as an intermediary assessment that moderates the classification decision, preventing hasty blocking of legitimate messages while maintaining aggressive spam filtering
Solution Approach 2:
The system performs preliminary clustering and rating of message groups before final classification decisions are made. By pre-grouping messages and calculating cluster-level spam probabilities in advance, the system can apply more conservative thresholds to individual messages within high-confidence clusters, reducing false positives while maintaining high spam detection rates
3Measurement precision
If clustering based on hash sums is used, then spam message identification improves, but computational complexity increases
Solution Approach 1:
The patent extracts only the essential identifying features (hash sums of content, sender addresses, metadata) from complete messages for clustering purposes. By working with these extracted key features rather than entire message contents, the system achieves high identification accuracy while significantly reducing computational complexity and memory requirements
Solution Approach 2:
The system applies partial action by using hash sums of specific message portions (subject, body, attachments) rather than processing complete messages. This selective hashing approach provides sufficient precision for spam identification while avoiding the computational overhead of analyzing entire message contents, achieving an optimal balance between accuracy and complexity
Data Source
AI summary
Disclosed are systems, methods and computer program products for detection of spam. In one example, a system receives electronic messages and attempts to classify the messages as legitimate or spam messages. For an unknown message, the system obtains its metadata including hash sum of the message and sender's IP address. The system then places the metadata of the unknown messages into one cluster of a plurality of clusters based on degree of similarity between hash sums of different unknown messages. The system then rates each unknown message in accordance with a rating of the cluster, wherein the rating of the cluster is based, at least, on a number of similar hash sums of unknown messages received from different addresses of message senders contained in said cluster. Finally, the system classifies unknown messages as legitimate or spam based on the message rating.