URL Typo Detection Using Probabilistic Error Modeling
Find Innovative SolutionsGenerate Solutions
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 generated using transformation error probabilities derived from a training dataset to predict the likelihood of typographical errors in URLs, allowing for the detection and prevention of typosquatting by alerting users or prioritizing security checks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a user enters a URL into a browser address bar, then the browser navigates to the intended website, but typographical errors cause navigation to unintended and potentially malicious websites
Solution Approach 1:
The system performs preliminary analysis of the entered URL by comparing it against a probabilistic graphical model of expected domain name transformations before navigation occurs. This advance detection allows the system to identify potential typosquatting attempts and alert users or block navigation before the harmful effect can manifest.
Solution Approach 2:
The patent introduces an intermediary security checking layer between the URL input and navigation execution. This intermediary component uses transformation error probabilities and probabilistic graphical models to assess the safety of URLs, acting as a mediator that can intercept and handle potentially malicious URLs before they reach the navigation function.
2Measurement precision
If the system analyzes transformation error probabilities to detect typographical errors, then the accuracy of detecting typos improves, but the complexity of the detection system increases
Solution Approach 1:
The detection system is segmented into independent components: a training data collection module, a probabilistic graphical model builder, and a URL analysis module. Each component handles specific aspects of the detection process independently, making the overall complex system more manageable and easier to implement incrementally.
Solution Approach 2:
The system uses transformation error probabilities as key parameters to quantify the likelihood of typographical errors. By changing the approach from binary error detection to probabilistic parameter analysis, the system achieves higher measurement precision while maintaining manageable complexity through statistical modeling.
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.