Spam URL Detection via Redirect Chain Tracking
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Solution Overview
Problem
Current spam email filtering systems are limited in blocking spam URLs, especially those that use shortened or redirecting URLs, as these can be easily modified to evade detection, leading to increased loads on servers and reduced effectiveness in identifying spam at various points in time.
Innovation Solution
A method and system that extract URLs from emails, determine if they are redirecting URLs, and track subsequent redirections, identifying all URLs involved as spam if they exceed a predetermined number of redirects, using techniques such as virtual browser access and screenshot comparison to determine spam characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If shortened URLs and redirecting links are used to make tracking difficult, then ease of attachment and user access is improved, but URL tracking capability deteriorates
Solution Approach 1:
The system performs preliminary actions by extracting URLs from emails before users interact with them, and proactively tracks redirect chains to identify spam destinations in advance. This includes extracting URLs when emails are received, pre-tracking redirect paths, and storing redirect information for future reference, thereby maintaining tracking capability despite the use of shortened URLs.
Solution Approach 2:
The system introduces an intermediary tracking mechanism that acts between the shortened URL and the final destination. A virtual browser or tracking server serves as an intermediary that automatically follows redirect chains and maps shortened URLs to their ultimate destinations, enabling indirect tracking without requiring direct user interaction with the redirecting links.
2Reliability
If redirecting URLs are continuously modified to avoid spam block systems, then spam email delivery reliability is improved, but detection accuracy deteriorates
Solution Approach 1:
The system performs preliminary tracking of redirect chains and stores redirect relationships in advance. By pre-following redirect paths and caching redirect information, the system builds a knowledge base of spam URL patterns before they are used in actual spam campaigns, improving detection accuracy even as spammers modify their redirecting URLs.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring and analyzing redirect patterns. When spam URLs are identified through tracking, this information feeds back into the spam detection system to improve future detection. The system learns from identified spam patterns and adjusts its detection algorithms, creating a self-improving detection mechanism that adapts to evolving spam techniques.
3Measurement precision
If comprehensive URL tracking and analysis is performed to identify all redirecting URLs as spam, then spam detection accuracy is improved, but system complexity and processing time increase
Solution Approach 1:
The system segments the URL tracking process into distinct modular components: URL extraction module, redirect tracking module, virtual browser module, and analysis module. Each component handles a specific aspect of the tracking process independently, making the overall system more manageable and maintainable while achieving comprehensive spam detection through the coordinated work of these segmented modules.
Solution Approach 2:
The system applies partial tracking by following redirect chains only up to a predetermined number of times or until a stopping criterion is met, rather than exhaustively tracking every possible redirect path indefinitely. This partial action approach maintains detection accuracy for practical purposes while preventing excessive processing and system complexity that would arise from complete exhaustive tracking.
4Measurement precision
If URL tracking is performed at multiple points in time, then spam detection coverage is improved, but processing load and time consumption increase
Solution Approach 1:
The system performs preliminary URL extraction and initial spam assessment when emails are first received, before full delivery. This early detection phase identifies obviously malicious URLs quickly. Additional tracking occurs at later points in time only when needed, such as when redirect patterns suggest potential spam or when users interact with the emails, thereby improving detection coverage without requiring continuous processing at all times.
Solution Approach 2:
The system implements periodic spam detection by scheduling URL tracking and analysis at specific intervals rather than continuously. URLs are tracked periodically based on factors such as time since email receipt, user interaction events, or changes in redirect patterns. This periodic approach maintains adequate spam detection coverage while significantly reducing overall processing time and resource consumption compared to continuous monitoring.
Data Source
AI summary
A method for determining a spam URL includes: (a) extracting a URL from an e-mail; (b) determining whether the extracted URL is a redirecting URL; (c) when the extracted URL is a redirecting URL, accessing a redirection URL that is connected as a result of access to the extracted URL; (d) when the redirection URL is a redirecting URL, accessing a redirection URL that is connected as a result of access to the redirection URL; (e) repeating operation (d); (f) when a last accessed URL in one of operations (c), (d), and (e) is not a redirecting URL, determine whether the last accessed URL is a spam URL; and (g) when it is determined that the last accessed URL is a spam URL, determining the extracted URL, the last accessed URL and any redirection URL connected between the extracted URL and the last accessed URL as spam URLs.