Predictive SPAM Filter Generation via Reputation Database
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Solution Overview
Problem
Existing message filtering systems are ineffective in preventing unwanted messages, such as SPAM, from reaching recipients before they are identified as harmful, as filters are typically created after the message has already been sent.
Innovation Solution
A method and apparatus for creating predictive filters by storing reputation information for message features in a reputation database, where a filter generation module determines message features associated with SPAM and generates predictive filters to prevent the transmission of potentially hazardous messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If filters are created after messages are sent and identified as harmful, then the filtering process is simple and reactive, but damage may occur before the filter is created and the system cannot prevent unwanted messages proactively
Solution Approach 1:
The system performs preliminary actions by proactively creating predictive filters for message features associated with unwanted messages before any harmful messages are sent. The filter generation module analyzes reputation information and generates filters in advance, so when unwanted messages are sent, they are immediately blocked without causing damage or delay.
Solution Approach 2:
The system applies preliminary anti-action by creating filters that preemptively counteract potential harmful messages. By identifying message features associated with SPAM and unwanted messages through reputation analysis, the system establishes defensive filters before the harmful messages can reach recipients, effectively neutralizing the threat in advance.
2Measurement precision
If the system analyzes and stores reputation information for multiple message features, then the ability to create accurate predictive filters is improved, but the complexity of the system increases
Solution Approach 1:
The system segments the filtering task by focusing on specific message features (sender address, domain name, host IP address, etc.) rather than analyzing entire messages. The filter generation module creates targeted filters for each feature type, breaking down the complex filtering problem into manageable segments that can be handled independently and efficiently.
Solution Approach 2:
The system introduces a filter generation module as an intermediary between message analysis and filter creation. This module simplifies the process by automatically analyzing reputation information, identifying associated message features, and generating appropriate filters, thereby reducing the overall system complexity while maintaining high filter accuracy.
3Object-affected harmful factors
If predictive filters are generated for message features associated with SPAM, then unwanted messages can be blocked before reaching recipients, but additional processing resources are required
Solution Approach 1:
The system applies partial action by generating filters only for message features that are specifically associated with unwanted messages based on reputation analysis, rather than creating filters for all possible message features. This selective approach provides sufficient protection against harmful messages while avoiding the excessive processing resources that would be required to filter all messages universally.
Data Source
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AI summary
A method and apparatus for creating predictive filters for messages. In one embodiment, filter information is coupled to a reputation database. One or more filters for a message feature are generated if a reputation of the message feature is associated with one or more portions of the filter information. In one embodiment, SPAM filters are generated. In yet another embodiment, one or more message features are tested using heuristics. One or more message features are blacklisted based on a determination of the heuristics. One or more additional message filters are generated if a reputation of the message feature is associated with a blacklisted feature.