Inline URL Categorization Using Lightweight Deep Learning
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Solution Overview
Problem
Existing URL categorization systems in cloud-based security systems face challenges in efficiently categorizing new URLs without introducing undesirable latency, particularly in large-scale environments with high transaction volumes.
Innovation Solution
Utilizing lightweight machine learning models, such as BERT-tiny, with improved tokenization and language-based content filtering, and implementing data leakage detection to perform inline URL categorization, leveraging curated training data and preprocessing of HTML files for efficient URL classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning models are used for URL categorization, then classification accuracy can be achieved, but system latency increases and processing speed decreases
Solution Approach 1:
The patent employs lightweight machine learning models that are computationally efficient and可以快速 deployed, replacing heavier traditional models. These lightweight models provide sufficient accuracy for URL categorization while significantly reducing processing time and resource consumption, enabling real-time inline categorization without substantial latency.
Solution Approach 2:
The patent modifies model parameters and architecture to optimize for speed-accuracy tradeoff. By adjusting model complexity, layer depth, and parameter counts, the system achieves acceptable categorization accuracy with reduced computational overhead, thereby minimizing latency in high-volume traffic environments.
2Measurement precision
If comprehensive content inspection is performed on all URLs, then categorization accuracy improves, but processing throughput decreases
Solution Approach 1:
The patent implements a two-stage processing approach where not all URLs undergo complete inspection. A lightweight filtering mechanism first processes URLs to identify obvious cases, and only potentially ambiguous URLs are subjected to full content inspection. This partial action maintains high accuracy for critical cases while preserving overall processing throughput.
Solution Approach 2:
The patent divides the URL categorization process into multiple stages: initial filtering, lightweight model classification, and detailed analysis only when needed. This segmentation allows the system to handle high volumes of traffic efficiently while maintaining accuracy through selective deep inspection of specific URL subsets.
3Speed
If lightweight models are used to reduce latency, then processing speed improves, but model complexity reduction may affect classification precision
Solution Approach 1:
The patent uses curated training data that replicates real-world URL distributions and characteristics. By training lightweight models on this carefully constructed dataset, the models achieve classification precision comparable to heavier models while maintaining the speed advantages of their simplified architecture.
Solution Approach 2:
The patent performs extensive model training and optimization in advance before deployment. Pre-computing features, pre-processing training data, and fine-tuning model parameters beforehand allow the lightweight models to achieve high precision during actual online categorization without sacrificing processing speed.
Data Source
AI summary
Systems and methods for inline Uniform Resource Locator (URL) categorization include training a lightweight machine learning model to score content associated with unknown Uniform Resource Locators (URLs) to determine a category of the plurality of categories for each of the unknown URLs; deploying the trained lightweight machine learning model to a node in a cloud-based system for use in production; and utilizing the trained lightweight machine learning model to monitor traffic inline to categorize unknown URLs.