HTTP Header Embeddings for Scalable Network Device Categorization
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Solution Overview
Problem
Existing methods for network device categorization are complex, resource-intensive, and struggle to scale with the diversity and volume of modern network devices, often relying on deep packet inspection or outdated signature databases, and are limited by the availability of device-specific web interfaces.
Innovation Solution
A method using machine learning models to generate vector embeddings from network device response data, specifically focusing on response headers and status codes, enabling efficient and adaptive device categorization without deep packet inspection, and utilizing a comprehensive dataset updated regularly to handle diverse devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep packet inspection is used to analyze network device contents, then device categorization accuracy is improved, but processing power requirements and bandwidth consumption increase significantly
Solution Approach 1:
The patent extracts only the essential identifying features from network device responses - specifically response headers and status codes - rather than analyzing the complete packet contents. This extraction approach maintains categorization accuracy while dramatically reducing processing requirements by focusing only on the metadata portions of HTTP/HTTPS responses that contain device identification information.
Solution Approach 2:
The patent segments the network device identification process into distinct components: sending requests to multiple devices, collecting response data, generating vector embeddings from response headers and status codes, and categorizing devices based on these embeddings. This segmentation allows for efficient parallel processing and reduces the computational burden on any single processing component.
2Measurement precision
If deep packet inspection is used to analyze network device contents, then device categorization accuracy is improved, but network bandwidth consumption increases significantly
Solution Approach 1:
The patent extracts only the essential identifying features from network device responses - specifically response headers and status codes - rather than analyzing the complete packet contents. This extraction approach maintains categorization accuracy while dramatically reducing processing requirements by focusing only on the metadata portions of HTTP/HTTPS responses that contain device identification information.
3Device complexity
If traditional signature databases are used for device identification, then device categorization is simplified, but the methods become outdated quickly as new devices enter the market
Solution Approach 1:
The patent implements a dynamic vector embedding model that continuously adapts to new device types by learning from response patterns in real-time. Rather than relying on static signature databases that require manual updates, the system dynamically generates embeddings from response headers and status codes, allowing it to automatically recognize and categorize new device types as they appear on the network without requiring database updates.
Solution Approach 2:
The patent changes the fundamental parameters used for device identification from static signature matching to dynamic vector embeddings generated from response headers and status codes. This parameter transformation enables the system to capture subtle variations in device behavior and response patterns, providing both simplicity and adaptability simultaneously.
4Productivity
If machine learning models generate vector embeddings from response data, then device categorization speed is improved, but computational complexity increases
Solution Approach 1:
The patent extracts only the essential identifying features from network device responses - specifically response headers and status codes - rather than analyzing the complete packet contents. This extraction approach maintains categorization accuracy while dramatically reducing processing requirements by focusing only on the metadata portions of HTTP/HTTPS responses that contain device identification information.
Data Source
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AI summary
The present disclosure provides a method for identifying a device category of a network device. The method includes sending an HTTP and/or HTTPS request (41) to the network device, receiving a response (43) from the network device including response data consisting of a response status code and response headers (44), generating a vector embedding (45) from the response data (51) using a machine learning model, and identifying a device category (48) based on the vector embedding and further vector embeddings in a first data set (47). The identifying comprises identifying a cluster of network devices (46) in the first data set based on the vector embedding, and retrieving a device category of the identified cluster as the device category of the network device.