Network Device Categorization Using Response Header Embeddings
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for network device categorization are complex, resource-intensive, and struggle with scalability and accuracy due to reliance on deep packet inspection and static signature databases, which are inefficient and prone to obfuscation.
Innovation Solution
A method utilizing machine learning models to generate vector embeddings from network device response data, specifically focusing on status codes and headers, enabling efficient and adaptive device categorization without deep packet inspection, and employing clustering techniques to group similar 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 time 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 entire packet content. This extraction approach maintains categorization accuracy while dramatically reducing processing power requirements and time consumption.
Solution Approach 2:
The patent segments the device identification process into distinct components: extracting response headers, generating vector embeddings from these headers, and comparing embeddings to known device profiles. This segmentation allows for efficient processing of only the most relevant data portions.
2Measurement precision
If deep packet inspection is used to analyze network device contents, then device categorization accuracy is improved, but time 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 entire packet content. This extraction approach maintains categorization accuracy while dramatically reducing processing power requirements and time consumption.
Solution Approach 2:
The patent performs preliminary extraction of response headers and generation of vector embeddings before the actual categorization comparison. This preliminary action prepares the data in advance in an optimized format, reducing the time required for the final categorization step.
3Device complexity
If static databases of device signatures are used for device identification, then device categorization is simplified, but the databases become outdated quickly as new devices enter the market
Solution Approach 1:
The patent implements a dynamic device identification system where vector embeddings of new devices are continuously added to the database. The system naturally adapts to new device types through embedding comparison, allowing the database to evolve without manual updates while maintaining simple categorization logic.
4Productivity
If MAC addresses or device IDs are analyzed for device identification, then device categorization is quick and simple, but these identifiers can be spoofed or obfuscated
Solution Approach 1:
Instead of relying on directly observable but spoofable identifiers like MAC addresses, the patent creates a functional copy of device identity through vector embeddings generated from response headers. These embeddings capture the device's actual behavioral characteristics, making spoofing difficult while maintaining quick identification speed.
Data Source
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.