Twin Inference Model for Distributed Device Authentication
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Solution Overview
Problem
In distributed environments, re-establishing a root of trust for data processing systems without user intervention is computationally expensive and time-consuming, especially when historical telemetry data is limited and repeatedly used for authentication and other purposes.
Innovation Solution
The system generates a security questionnaire based on shared knowledge derived from newly obtained data points and a twin inference model, allowing for authentication of data processing systems without user intervention and conserving computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional authentication methods are used to re-establish root of trust in distributed environments, then authentication reliability is improved, but computing resource expenditure increases and user intervention is required
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing telemetry data from devices before authentication is needed. This historical data is prepared in advance and can be quickly retrieved during authentication events, eliminating the need for computationally intensive real-time analysis and reducing energy consumption during actual authentication processes.
Solution Approach 2:
The patent creates copies of device telemetry data and stores them in centralized repositories. During authentication, instead of accessing and processing original device data repeatedly, the system uses these pre-created copies, significantly reducing computing resource expenditure while maintaining authentication reliability.
2Reliability
If traditional authentication methods are used to re-establish root of trust, then authentication security is improved, but time consumption increases
Solution Approach 1:
Security questions and answers are generated in advance using historical telemetry data and machine learning models before authentication events occur. This preliminary generation of authentication challenges and responses dramatically reduces the time required during actual authentication, while maintaining strong security through the use of device-specific behavioral patterns.
Solution Approach 2:
The authentication system dynamically adapts by using machine learning models that continuously learn from new telemetry data. The security questions and answers are not static but evolve over time based on device behavior changes, allowing fast authentication that adapts to current device states without requiring lengthy re-authentication processes.
3Adaptability or versatility
If historical telemetry data is repeatedly used for authentication and other purposes, then authentication functionality is improved, but data availability deteriorates
Solution Approach 1:
The system segments telemetry data usage by creating multiple copies and distributing them across different purposes. Historical data is divided into authentication-specific subsets and other usage subsets, allowing repeated authentication operations without depleting data needed for other functions. This segmentation enables parallel usage without data exhaustion.
Solution Approach 2:
The patent implements a universal data collection system where telemetry data serves multiple functions simultaneously - authentication, device monitoring, analytics, and model training. By making the data collection infrastructure multi-functional and creating redundant copies, the system improves authentication functionality while preventing data availability deterioration through efficient resource sharing.
Data Source
AI summary
Methods and systems for authenticating data processing systems throughout a distributed environment without user intervention are disclosed. To authenticate data processing systems without user intervention, a system may include a network core and one or more data processing systems. A previously established root of trust between the network core and a data processing system may be lost and the network core may attempt to re-authenticate the data processing system using shared knowledge. The shared knowledge may be based on data obtained from the data processing system and an inference generated by a twin inference model. The network core may provide the data processing system with a security questionnaire based on the shared knowledge and the data processing system may use the twin inference model to respond to the security questionnaire. If the answers to the security questions are considered accurate, the data processing system may be re-authenticated.