Automated Device Classification Rules via Telemetry Analysis
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Solution Overview
Problem
The complexity of generating and maintaining device classification rules in computer networks, especially in IoT environments, leads to inaccurate rules and potential security risks due to the dynamic nature of connected devices and the difficulty in manually providing detailed information about each device type.
Innovation Solution
A system that uses machine learning-based classifiers to analyze telemetry data and form graphical representations of device attributes, allowing for the automatic generation and assessment of device classification rules, and a universal rule database to optimize and compare these rules across different networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If device classification rules are manually generated and maintained, then device type identification can be achieved, but the complexity of rule generation and maintenance increases significantly
Solution Approach 1:
The system automatically generates device classification rules by observing device behavior patterns without requiring manual rule creation. The network device autonomously learns classification criteria from telemetry data, eliminating the need for manual rule generation and maintenance while maintaining high classification accuracy
Solution Approach 2:
Manual rule generation and maintenance processes are replaced with automated machine learning-based classification systems. The system uses algorithms to automatically analyze device behavior and generate classification rules, substituting human effort with computational processes
2Device complexity
If device classification rules are generated automatically by observing device behavior, then rule generation complexity is reduced, but classification accuracy may deteriorate due to rule conflicts and inaccuracies
Solution Approach 1:
The system continuously monitors device behavior and uses feedback loops to refine classification rules. By observing actual device performance and classification outcomes, the system automatically adjusts and optimizes rules to maintain high accuracy while managing rule conflicts
Solution Approach 2:
Device classification rules are made dynamic rather than static. The system continuously adapts rules based on observed device behavior patterns, allowing classification criteria to evolve and improve accuracy over time while automatically resolving conflicts through behavioral analysis
3Measurement precision
If detailed information is collected about each device type, then classification accuracy improves, but the difficulty of manually providing and maintaining this information increases
Solution Approach 1:
The system automatically collects and analyzes detailed device information through telemetry data without requiring manual data entry. Network devices autonomously gather behavioral characteristics, performance metrics, and operational patterns, eliminating the manual burden of collecting detailed device information while maintaining high classification accuracy
Data Source
AI summary
In various embodiments, a device obtains a set of device classification rules. Each device classification rule specifies one or more attributes from a set of attributes and being configured to assign a device type to an endpoint in a network when the endpoint exhibits the one or more attributes specified by that rule. The device forms a graphical representation of the set of attributes. The device performs an analysis of the graphical representation of the set of attributes. The device provides a result of the analysis to a user interface.