Neural Network URL Detection via Rendered Content Analysis
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Solution Overview
Problem
Current methods for blocking undesirable URLs are time-consuming and prone to missing malicious resources, leading to security breaches and information leaks, as they rely on manual generation of block lists.
Innovation Solution
A system that uses machine learning algorithms, specifically neural networks, to analyze rendered web page content and identify undesirable attributes, storing detected URLs in a database for controlling access and blocking malicious content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual generation of block lists is used, then user control over blocking is maintained, but the process is time-consuming and prone to missing malicious resources
Solution Approach 1:
The system performs self-service by automatically detecting and classifying URLs using machine learning algorithms. The neural network analyzes rendered web page content, extracts features, and autonomously determines whether URLs should be blocked, eliminating the need for manual block list generation while maintaining high detection accuracy
Solution Approach 2:
The patent replaces the mechanical manual process of creating block lists with an automated machine learning system. The neural network substitutes human analysts by automatically processing web content, extracting malicious patterns, and generating block lists, thereby reducing time consumption while improving detection reliability
2Reliability
If manual block list generation is used, then simplicity of the system is maintained, but security breaches and information leaks occur due to overlooked malicious resources
Solution Approach 1:
The patent introduces an intermediary machine learning layer between web content and blocking decisions. The neural network acts as a mediator that analyzes rendered content, extracts meaningful features, and makes intelligent blocking decisions, thereby enhancing security protection while managing system complexity through a structured approach
3Productivity
If automated machine learning detection is implemented, then detection speed and accuracy are improved, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing web content and extracting features before final classification. The neural network analyzes rendered content in advance, identifies malicious patterns, and prepares blocking decisions, thereby improving detection speed and productivity while managing computational resource consumption through efficient feature extraction and processing
Data Source
AI summary
A system, apparatus and method for detecting uniform resource locators (URLs) of undesirable web pages comprising identifying a web page having an associated URL, rendering the content of the web page and logging the URL associated with the web page. The rendered content is analyzed by applying a machine learning algorithm comprising a neural network, where the neural network analyzes the rendered content to identify undesirable attributes within the rendered content. Upon identifying undesirable content, the URL is stored in a database for subsequent use by user devices to control access to the URL and its associated web page.