Correlating Browsing History with Spam Lists for Detection
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Solution Overview
Problem
Current systems for identifying spam websites require large server resources and become impractical as the number of websites grows, as they rely on honeypots to detect spam, which is inefficient and resource-intensive.
Innovation Solution
A method that correlates users' website browsing behavior with spam mailing lists to identify common websites, updating reputation information and associating website browsing behavior with spam lists, leveraging client endpoints to determine the origin of spam emails and assign an annoyance score to websites.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If honeypots are used to detect spam websites, then spam detection capability is improved, but server resource consumption increases significantly
Solution Approach 1:
The patent enables client endpoints to perform spam detection locally by analyzing their own browsing behavior and correlating it with spam mailing lists. Each client device independently determines whether visited websites are spam sources, eliminating the need for centralized honeypot infrastructure and reducing server resource consumption while maintaining detection capability.
2Adaptability or versatility
If the number of monitored websites increases, then spam detection coverage is improved, but system complexity and resource requirements worsen
Solution Approach 1:
The patent extracts the spam detection functionality from the server infrastructure and relocates it to client endpoints. By taking out the complex analysis operations from the server and performing them locally on client devices, the system can monitor an increasing number of websites without proportionally increasing server complexity or resource requirements.
3Speed
If real-time processing of browsing behavior is implemented, then spam identification speed is improved, but computational requirements increase
Solution Approach 1:
The patent implements partial real-time processing by continuously monitoring browsing behavior and immediately processing it when spam correlations are detected. Rather than processing all browsing data in full real-time, the system processes only the necessary portions (browsing history correlations with spam lists) at real-time speeds, reducing computational requirements while maintaining fast spam identification.
Data Source
AI summary
A computer-implemented method for associating website browsing behavior with a spam mailing list is described. A history of website browsing behavior is collected for a plurality of users. At least one spam mailing list is identified that includes an e-mail address for at least two users of the plurality of users. A determination is made as to whether a common website exists between the histories of website browsing behavior for the at least two users. Reputation information for the common website is updated.