Focused URL Recrawl for Policy Enforcement Appliances
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Solution Overview
Problem
Firewalls and security devices face challenges in accurately classifying URLs, leading to misclassification issues due to the sheer volume of URLs and limitations in existing classification models, resulting in incorrect blocking of websites that should be accessible.
Innovation Solution
A policy enforcement appliance with a URL classification system that uses crawlers, machine learning approaches, and a database to classify URLs, along with a process for reclassifying misclassified sites based on user reports and updates using a current classification model, and a scalable architecture for efficient URL management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a comprehensive URL classification system is implemented to cover all URLs, then URL classification coverage is improved, but system complexity and resource requirements increase due to the sheer volume of URLs
Solution Approach 1:
The patent segments the URL classification task by focusing on a specific subset of URLs (those that have been blocked or are suspected of misclassification) rather than attempting to classify all URLs comprehensively. This targeted approach reduces system complexity while maintaining high accuracy for the critical cases that matter most for security policy enforcement.
Solution Approach 2:
The patent implements a feedback mechanism where users can report misclassified URLs, and the system uses this feedback to refine its classification. The recrawl process re-evaluates previously blocked URLs using updated classification models, allowing the system to learn from past decisions and improve accuracy over time without needing to reclassify every URL from scratch.
2Reliability
If frequent recrawling of all URLs is performed to update classifications, then classification accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
Instead of recrawling all URLs frequently, the patent segments the recrawling process to focus only on URLs that have been blocked or are suspected of misclassification. This selective recrawling approach maintains classification accuracy for critical URLs while significantly reducing the overall time and computational resources required compared to comprehensive recrawling.
Solution Approach 2:
The patent performs preliminary identification of potentially misclassified URLs through initial classification passes and user reports before initiating full recrawling. This preliminary screening ensures that recrawling resources are allocated efficiently to only those URLs that need re-evaluation, avoiding unnecessary processing of correctly classified URLs.
3Object-affected harmful factors
If strict URL blocking policies are enforced to prevent access to malicious sites, then security is improved, but legitimate access is blocked due to misclassification
Solution Approach 1:
The patent implements feedback mechanisms at multiple levels: user reporting channels allow legitimate users to flag false blocks, and the system incorporates this feedback to refine its classification. The recrawl process re-evaluates blocked URLs using updated models, creating a feedback loop that continuously improves both security protection and access accuracy by learning from both security threats and false positives.
Solution Approach 2:
The patent makes the blocking policy dynamic rather than static. URLs are not permanently blocked based on initial classification; instead, the system continuously re-evaluates blocked URLs using updated classification models and user feedback. This dynamic approach allows the system to adapt to changing website content and correct misclassifications while maintaining security against actual threats.
Data Source
AI summary
A website misclassification report is received. A determination is made that a current classification model correctly classifies a website. The current classification model is different from a model that was previously used to classify the website. In response to a determination that the website is correctly classified by the current classification model, a reclassification operation is performed, using the current classification model, on a second website.