Web Page Risk Detection Using ML and User Activity Signals
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Solution Overview
Problem
Modern Internet scams are increasingly sophisticated and difficult to detect, even for advanced users, as existing web browser checks are often insufficient for identifying nuanced scams, leading to potential financial loss and data theft.
Innovation Solution
Implementing a risk detection machine learning model that processes user activity and page data to evaluate web page risk, using a computing device with a web browser plug-in, and providing security recommendations, including temporary credit card numbers if necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional web browser checks are used to detect scams, then the system is simple and easy to operate, but the detection precision is insufficient for modern sophisticated scams
Solution Approach 1:
A machine learning model is introduced as an intermediary component between the web browser and the user. This model analyzes web page features, user behavior patterns, and contextual information to detect sophisticated scams that traditional checks miss. The model processes multiple data sources and provides risk assessments, enabling accurate detection without requiring users to directly analyze complex scam techniques.
Solution Approach 2:
The patent replaces traditional mechanical scam detection methods (manual checks, simple protocol validation) with an intelligent machine learning system. The machine learning model automatically analyzes web page content, user behavior, and contextual factors to identify scams, substituting the need for users to manually detect sophisticated fraudulent patterns.
2Measurement precision
If comprehensive user activity monitoring is implemented to improve scam detection, then the detection accuracy improves, but the loss of user information and privacy increases
Solution Approach 1:
The machine learning model applies different levels of monitoring to different aspects of user activity. Sensitive personal information is protected and not stored, while behavioral patterns and contextual information necessary for scam detection are analyzed. The system processes data locally where possible and only transmits essential features to the machine learning model, maintaining privacy while achieving accurate detection.
Solution Approach 2:
The system extracts only the necessary features from user activity data that are relevant for scam detection, rather than storing or processing complete user information. The machine learning model receives processed features such as browsing patterns and contextual information while personal identifiers and sensitive data are excluded, achieving detection accuracy without compromising privacy.
Data Source
AI summary
Methods, systems, and apparatuses for risk analysis of web pages using a machine learning model are described herein. A computing device may receive a risk detection machine learning model trained to receive input corresponding to a web page and output an indication of risk associated with the web page. The computing device may execute a web browser application and collect user activity data by monitoring user activity associated with the web browser application. The computing device may access, via the web browser application, a first web page, and collect page data associated with the first web page. The computing device may calculate a risk level of the first web page. The risk level may be calculated by processing, using the risk detection machine learning model, both the user activity data and the page data. A security recommendation may be output based on the risk level.