URL Spam Detection via Binary Classifier and Tokenization
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Solution Overview
Problem
Current search engines face inefficiencies in detecting spam webpages without relying on blacklists or rigid URL patterns, leading to resource consumption and incomplete indexing due to spammers' tactics.
Innovation Solution
A binary classifier is trained on URL sequences to predict spam probability, allowing for spam detection without content retrieval, using tokenization and periodic retraining to adapt to changing spam tactics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional spam detection methods using blacklists and rigid URL patterns are used, then spam detection can be performed, but spammers can easily evade detection by changing tactics and URL structures
Solution Approach 1:
The patent applies dynamics by transitioning from static blacklist/pattern matching to a dynamic machine learning classifier that continuously adapts to new spam tactics. The classifier is periodically retrained with new data, allowing the system to evolve and maintain effectiveness against changing spam strategies without requiring manual updates to blacklists or patterns.
Solution Approach 2:
The patent changes the fundamental parameters of spam detection by moving from exact string matching to probabilistic classification based on numerical features. The system extracts multiple features from URLs (length, character diversity, path depth, etc.) and uses a classifier to compute spam probability, fundamentally changing how spam is detected from rigid pattern matching to flexible parameter-based assessment.
2Reliability
If all webpages are retrieved and content is extracted for spam analysis, then comprehensive spam detection can be achieved, but computational resources are consumed excessively
Solution Approach 1:
The patent applies preliminary action by performing spam classification on URL features before retrieving webpage content. The machine learning classifier evaluates extracted URL features and computes a spam probability score, allowing the system to filter out obvious spam cases without consuming resources on full content retrieval and analysis, thus reducing overall computational burden.
Solution Approach 2:
The patent implements partial action by selectively retrieving and analyzing only those webpages that the classifier deems potentially spammy based on URL features. Instead of analyzing all webpages or only those matching rigid patterns, the system performs partial content retrieval based on probabilistic assessment, optimizing resource usage while maintaining detection effectiveness.
3Quantity of substance
If spam webpages are identified and excluded from indexing, then storage needs are reduced, but the detection system must accurately distinguish spam from legitimate content
Solution Approach 1:
The patent implements feedback by using the results of spam classification to continuously improve the system. The classifier is periodically retrained with new labeled data, including cases where the classification may have been incorrect. This feedback loop allows the system to learn from mistakes and improve measurement precision over time, ensuring more accurate distinction between spam and legitimate content while maintaining efficient storage management.
Data Source
AI summary
Described herein are technologies relating to predicting whether a resource is spam based solely upon a Uniform Resource Locator (URL) for the resource. The URL is tokenized in connection with generating a sequence of numerical identifiers for the resource. A score for the URL is computed based upon the sequence of numerical identifiers, where the score is indicative of a probability that the resource pointed to by the URL is spam. generating a score for the URL based upon the sequence of numbers, wherein the score is indicative of a probability that the resource pointed to by the URL is spam. When the score is above a predefined threshold, a label is assigned to the URL that indicates that the resource pointed to by the URL is spam, and an entry for the resource is not included in a search engine index based upon the label assigned to the URL.