Machine Learning Web Page Risk Analysis for Nuanced Scam Detection
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Solution Overview
Problem
Modern Internet scams have evolved to evade detection by traditional web browser checks, posing a risk to users by tricking them into providing sensitive information on seemingly legitimate web pages, even for savvy users.
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 to provide security recommendations and potentially generate temporary credit card numbers based on calculated risk levels.
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 nuanced scams
Solution Approach 1:
A machine learning model is introduced as an intermediary between the web browser and the user. This model analyzes web page content, user behavior patterns, and contextual data to detect nuanced scams that traditional checks miss. The model processes multiple data sources and provides risk assessments, enabling sophisticated detection without requiring direct complex analysis by the browser itself.
Solution Approach 2:
Traditional mechanical check systems (rule-based detection, protocol verification) are replaced with an intelligent machine learning system. The ML model learns from training data to identify scam patterns, substituting rigid mechanical detection rules with adaptive intelligent analysis that can detect subtle and evolving scam techniques.
2Reliability
If comprehensive user activity monitoring is implemented to improve scam detection, then detection capability improves, but user privacy concerns increase
Solution Approach 1:
The system extracts and analyzes only the specific user activity data necessary for scam detection (browsing history, interaction patterns with suspicious pages) while leaving other personal information private. The machine learning model processes this extracted data locally or in a privacy-preserving manner to generate risk assessments without requiring comprehensive monitoring of all user activities.
Solution Approach 2:
The system performs preliminary risk assessment by analyzing web page content and user behavior patterns before users provide sensitive information. This preliminary action allows the system to warn users about potential scams before privacy-sensitive transactions occur, reducing the need to monitor and store detailed personal data.
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.