Machine Learning URL Content Classification via Feature Extraction
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Solution Overview
Problem
Current cloud-based security systems face challenges in dynamically classifying and categorizing new Uniform Resource Locator (URL) content at scale, necessitating an automated and efficient method for content classification to effectively allow or block web requests.
Innovation Solution
The implementation of a Machine Learning (ML) classifier for URLs, involving data labeling, preprocessing, feature extraction, and serialization into a flat buffer decision tree structure, enables accurate and efficient categorization of new URL content, using techniques like Term Frequency (TF) and Inverse Document Frequency (IDF) and models such as Light Gradient Boosted Machine (LightGBM) for classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional content classification methods are used for URL categorization, then the system can process known URLs, but it cannot efficiently classify new URLs at scale
Solution Approach 1:
The system performs preliminary actions by pre-processing HTML files, extracting features, and training the machine learning model in advance. The model is trained on a dataset of URLs with known categories before deployment, enabling it to quickly classify new URLs without requiring real-time analysis of entire web pages. This preliminary preparation allows the system to handle new URLs efficiently at scale.
Solution Approach 2:
The system creates a simplified representation (copy) of the original HTML content by extracting key features such as text content, metadata, and structural elements. Instead of analyzing complete HTML files during classification, the system uses these extracted feature copies, which significantly reduces processing time while maintaining classification accuracy for both known and new URLs.
2Measurement precision
If complete HTML files are analyzed for URL classification, then accurate content understanding is achieved, but processing time and computational resources increase
Solution Approach 1:
The system extracts only the essential and relevant features from complete HTML files, such as text content, titles, descriptions, and key metadata elements. By taking out only these critical components rather than analyzing the entire HTML structure including scripts, styles, and redundant elements, the system maintains high classification accuracy while significantly reducing processing time and computational resource requirements.
Solution Approach 2:
The HTML analysis process is segmented into distinct stages: initial scanning for key elements, extraction of relevant features, and discarding of redundant content. This segmentation allows the system to focus computational resources on the most informative parts of the HTML content, achieving accurate classification without processing the entire file, thus reducing time loss while maintaining precision.
3Reliability
If manual labeling of URL content is performed, then training data quality is high, but the process is too slow for dynamic classification needs
Solution Approach 1:
The system employs self-service mechanisms where the machine learning model automatically labels new URLs based on the patterns learned during training. Once trained on initially labeled data, the model can independently classify new URLs without requiring continuous manual intervention. This self-service capability maintains high reliability by consistently applying learned classification rules while achieving high productivity through automated processing of large volumes of URLs.
Solution Approach 2:
The system implements feedback mechanisms where classification results are continuously evaluated and used to refine the model. Initially, high-quality manually labeled data provides feedback for training. As the model processes more URLs, its predictions can be validated and used to expand the training dataset, creating a feedback loop that improves both reliability and productivity over time without requiring proportional increases in manual labeling effort.
Data Source
AI summary
Systems and methods include obtaining data from Uniform Resource Locator (URL) transactions monitored by a cloud-based system; labeling the data for the URL transactions with a category of a plurality of categories that describe the content of a page associated with the URL; performing preprocessing of raw Hypertext Markup Language (HTML) files for the URL transactions; extracting features from the preprocessed raw HTML files; and creating a machine learning model based on the features, wherein the machine learning model is configured to score content associated with an unknown URL to determine a category of the plurality of categories.