URL Typo Scoring Using Probabilistic Models for Typosquatting Risk
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Solution Overview
Problem
Inadvertent typographical errors in URLs can lead to navigation to unintended and potentially malicious websites, posing security risks such as downloading malicious code or falling victim to phishing attacks.
Innovation Solution
A probabilistic graphical model is used to predict the likelihood of a typographical error in a URL by analyzing a training dataset and generating transformation error probabilities, allowing for the detection and prevention of typosquatting through alerting users or prioritizing security checks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a user enters a URL into a browser address bar, then the browser navigates to the website indicated by the URL, but typographical errors can cause the browser to navigate to unintended and potentially malicious websites
Solution Approach 1:
The system performs preliminary analysis of the URL string before navigation occurs. By analyzing transformation error probabilities and populating probabilistic graphical models in advance, the system can predict potential typographical errors and alert users before they navigate to malicious sites, thus preventing security risks while maintaining ease of URL entry
Solution Approach 2:
The patent introduces an intermediary security checking mechanism between the URL entry and navigation steps. This intermediary system uses probabilistic graphical models and transformation error analysis to detect potential typos and intervene by alerting users, thereby blocking the direct path to malicious websites while preserving the simplicity of URL input
2Measurement precision
If the system analyzes transformation error probabilities and populates probabilistic graphical models to detect typographical errors, then the accuracy of detecting typographical errors improves, but the device complexity increases
Solution Approach 1:
The system segments the URL analysis process into distinct components: character-level transformation error probability generation, probabilistic graphical model population, and likelihood prediction. This segmentation allows each component to be optimized independently and simplifies the overall system architecture while maintaining high detection accuracy through modular processing
Solution Approach 2:
The patent transforms the detection problem into a parameter estimation problem by calculating transformation error probabilities for different character substitutions. By changing the approach from pattern matching to probability parameter analysis, the system achieves high accuracy with more straightforward computational processes, reducing overall system complexity
Data Source
AI summary
A computing system generates transformation error probabilities by analyzing a training data set containing training strings, each transformation error probability indicating a probability that a per-character transformation applied to a character of a training string results in a typographical error in a resulting transformation string, wherein the training data set includes strings from a historical dataset of strings including typographical errors. The computing system populates a probabilistic graphical model with the transformation error probabilities corresponding to each resulting transformation string and predicts a likelihood that an input string contains a typographical error based on the probabilistic graphical model.