Spam Filtering via User Reputation Weighting
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Solution Overview
Problem
Conventional anti-SPAM methods fail to provide 100% effective filtering due to issues like false positives, reliance on large datasets, and inability to accurately assess user knowledge and expertise, leading to inefficient message filtering.
Innovation Solution
A system and method utilizing a cloud service that determines user reputation and uses SPAM reports to modify filtering rules, prioritizing reports from competent users to enhance SPAM detection and filtering efficiency across all users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional anti-SPAM methods (black list, mass mail detection, header checking, grey list, content filtering) are used, then SPAM filtering is provided, but 100% effectiveness is not achieved due to false positives, reliance on large datasets, and inability to accurately assess user knowledge
Solution Approach 1:
The system implements feedback loops where users report SPAM messages, and the system updates filtering rules based on these reports. The filtering rules are continuously refined by analyzing user reports and updating the knowledge base, creating a self-improving system that enhances filtering effectiveness over time while reducing false positives.
Solution Approach 2:
The patent introduces a SPAM filtering system that acts as an intermediary between users and SPAM messages. This system uses a knowledge base and filtering rules to mediate the interaction, blocking SPAM before it reaches users while allowing legitimate messages through. The system serves as a intelligent mediator that learns from user feedback to improve its mediation capabilities.
2Reliability
If mass mail detection technology is used to detect identical messages, then SPAM detection capability is improved, but the method requires very large volumes of mails and can only be used by very large mail providers
Solution Approach 1:
The system enables self-service filtering where each user's reports directly contribute to improving the filtering rules. Users actively participate in the filtering process by reporting SPAM, and the system automatically processes these reports to update the knowledge base and filtering rules, eliminating the need for centralized processing of large mail volumes.
Solution Approach 2:
The patent segments the SPAM detection task into individual user reports rather than requiring centralized analysis of mass mail volumes. Each user's reporting activity is processed independently to update filtering rules, allowing the system to achieve effective SPAM detection without requiring access to large volumes of mails from a single provider.
3Reliability
If grey list method is used to reject messages with error codes, then SPAM filtering is provided, but delivery of all messages is delayed
Solution Approach 1:
The system performs preliminary filtering by pre-populating a knowledge base with SPAM characteristics and filtering rules before messages arrive. This allows the filtering system to quickly compare incoming messages against known SPAM patterns without requiring time-consuming analysis or interaction with sending servers, thus avoiding delivery delays.
Solution Approach 2:
The patent replaces the mechanical grey list method (which requires sending rejection codes and waiting for server responses) with an information-based filtering system that uses a pre-populated knowledge base. This substitution eliminates the time-consuming communication loop with sending servers while maintaining effective SPAM filtering.
4Measurement precision
If content filtering with SPAM-filters is used to analyze all parts of incoming messages, then SPAM detection accuracy is improved, but the system requires continuous updates of filtering rules to remain effective
Solution Approach 1:
The system implements continuous feedback loops where user reports automatically trigger updates to the knowledge base and filtering rules. This feedback mechanism ensures that the filtering rules are continuously refined and updated based on real-world SPAM patterns, maintaining high detection accuracy without requiring manual intervention or frequent external updates.
Solution Approach 2:
The patent establishes continuous operation of the filtering system through automated processes that constantly analyze user reports, update the knowledge base, and refine filtering rules. This continuous useful action ensures the system remains effective against evolving SPAM techniques without requiring periodic maintenance or manual rule updates.
Data Source
AI summary
System for updating filtering rules for messages received by a plurality of users including a filtering rules database storing filtering rules for the users; means for distributing the filtering rules to the users; a user reputation database comprising a reputation weight for each user; and means for receiving and processing of user reports that indicate that a message belongs to a particular category. The means for receiving (i) calculates a message weight in its category based on a number of reports received from multiple users and a reputation weights of those users, (ii) decides whether the message belongs to the particular category if the message weight exceeds a predefined threshold, (iii) updates the filtering rules in the filtering rules database based on the deciding, and (iv) distributes the updated filtering rules from the filtering rules database to the users using the means for distributing.