Profiling Vectors for Network Device Clustering
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Solution Overview
Problem
Network service providers face challenges in identifying and targeting specific users or devices for actions such as firmware upgrades or notifications, particularly those at risk of network service disruptions, due to the lack of efficient profiling and clustering methods that can automatically categorize users based on network communication protocols.
Innovation Solution
A profiling server generates profiling vectors from network communication protocol parameters and applies clustering algorithms to create user groups, enabling the identification of users with affinities for targeted actions or activities, such as firmware upgrades or security notifications, without requiring manual user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual user input and identification methods are used, then accuracy of user targeting is improved, but productivity and efficiency deteriorate
Solution Approach 1:
The system enables self-service by automatically generating profiling vectors from network communication protocol parameters without requiring manual user input. The clustering algorithm autonomously categorizes users into groups based on their device characteristics and network behavior, eliminating the need for manual identification while maintaining high accuracy in targeting specific user groups for actions like firmware upgrades.
2Productivity
If automated profiling and clustering methods are implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The profiling server implements multi-functionality by combining multiple capabilities into a single system: generating profiling vectors from network parameters, performing clustering algorithms to create user groups, and identifying target users for specific actions. This universal approach handles various user categorization needs (firmware upgrades, security notifications, service disruptions) through one integrated system, managing complexity through consolidation rather than proliferation of separate tools.
3Measurement precision
If detailed network communication protocol parameters are collected, then measurement precision is improved, but loss of information increases
Solution Approach 1:
The system extracts only the essential profiling information needed for user categorization from network communication protocol parameters. Instead of collecting and storing all raw network data, the profiling server extracts relevant features to generate compact profiling vectors that capture user characteristics while minimizing data retention. This extraction approach maintains measurement precision for clustering purposes while reducing information loss and privacy concerns.
Data Source
AI summary
A network device determines when multiple users each connect to a network using one or more devices. The network device obtains device or network-related parameters associated with the one or more devices and generates profiling vectors for each of the multiple users, that connects to the network using the one or more devices, to produce multiple profiling vectors. The network device clusters the multiple profiling vectors to identify cluster centers associated with a plurality of user groups, and determines first users of the multiple users for, or with, whom to perform certain actions or activities based on the first users' with one or more of the plurality of user groups. The network device performs the certain actions or activities with respect to the determined first users.