Distributed Ledger Component Exposure Assessment
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Solution Overview
Problem
Current computing environments face challenges in accurately and efficiently tracking the trustworthiness of components within a computing environment, as existing methods are resource-intensive and lack precision in determining component-level exposure and misfunction likelihoods.
Innovation Solution
A system utilizing a distributed ledger to receive and record the likelihood of misfunction for components, based on network interaction history and security requirements, with machine learning to identify anomalous behavior and update misfunction likelihoods, thereby enhancing accuracy and reducing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional centralized methods are used to track component trustworthiness, then comprehensive monitoring can be achieved, but computational resources and network traffic increase significantly
Solution Approach 1:
The system divides the trust assessment function into distributed segments, where each computing component maintains its own exposure assessment data and shares it peer-to-peer through the blockchain network. This eliminates the need for a centralized computation hub, reducing overall computational overhead while maintaining comprehensive monitoring capabilities.
Solution Approach 2:
Each computing component autonomously generates and updates its own exposure assessment records, storing them on the blockchain. The components self-report their trustworthiness metrics and interaction histories, eliminating the need for external centralized monitoring systems and reducing network traffic to central servers.
2Measurement precision
If traditional centralized methods are used to track component trustworthiness, then comprehensive monitoring can be achieved, but network traffic increases significantly
Solution Approach 1:
The system segments the monitoring architecture into distributed nodes that communicate directly with each other via blockchain transactions. This peer-to-peer communication model reduces network traffic compared to centralized approaches where all components must communicate with a central server for every update.
Solution Approach 2:
Components autonomously publish their exposure assessment data to the blockchain without requiring centralized collection. The blockchain network itself handles the data propagation and storage, eliminating the need for dedicated centralized monitoring infrastructure and reducing overall network traffic.
3Reliability
If dynamic updates of misfunction likelihoods are implemented, then trust assessment accuracy improves, but computational overhead increases
Solution Approach 1:
The system implements continuous feedback loops where components monitor their own exposure metrics and automatically update their misfunction likelihood assessments. These updates are recorded on the blockchain, creating a dynamic yet distributed trust assessment system that improves accuracy without requiring centralized computation.
Solution Approach 2:
Each component autonomously performs the computational work of assessing its own trustworthiness and updating its exposure records. This self-service approach distributes the computational overhead across all participants rather than concentrating it in a central system, maintaining dynamic accuracy while reducing overall system complexity.
Data Source
AI summary
Systems, computer program products, and methods are described herein for component-level exposure assessment in a computing environment. The present disclosure is configured to receive, from a distributed ledger, a likelihood of misfunction associated with a first component; receive a network interaction history of the first component with a second component; determine an updated likelihood of misfunction associated with the first component based on at least the network interaction history of the first component with the second component; and record the updated likelihood of misfunction associated with the first component in the distributed ledger.