Neural Network Forecasting of Distributed Network Health Risk
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Solution Overview
Problem
Current methods for evaluating the security posture of distributed networks are static and fail to predictively assess how vulnerabilities impact the health status of systems over time, leading to inaccurate conclusions and inadequate prevention of anomalous health statuses.
Innovation Solution
A method utilizing an artificial neural network to identify and evaluate assets and links within a distributed network, calculating infection factors and forecasting future health statuses based on actual asset and site rankings, risks, and infection probabilities, trained through a feed-forward architecture with backpropagation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static evaluation methods are used to assess security posture, then the evaluation process is simple and quick, but the accuracy and predictive capability are insufficient
Solution Approach 1:
The patent transitions from static security posture evaluation to dynamic forecasting by implementing continuous monitoring of assets, vulnerabilities, and security measures over time. The system uses temporal metrics that update continuously and employs forecasting functions to predict future health status, transforming the evaluation from a snapshot to an evolving assessment that adapts to changing network conditions.
Solution Approach 2:
The patent introduces an artificial neural network as an intermediary between raw security data and health status evaluation. The neural network processes multiple input metrics (asset health, vulnerability scores, security measures) and produces forecasted health status outputs, acting as a intelligent mediator that synthesizes complex data relationships and provides accurate predictive assessments.
2Reliability
If holistic analysis of system evolution over time is implemented, then predictive accuracy improves, but computational requirements and processing time increase
Solution Approach 1:
The patent implements preliminary action by continuously collecting and storing security-relevant data (asset information, vulnerabilities, security measures) in advance before evaluation is needed. The system maintains updated databases of network assets and their security attributes, so when forecasting is required, the neural network can immediately process pre-prepared data without time-consuming collection delays.
Solution Approach 2:
The patent replaces traditional mechanical computation methods with neural network-based processing. Instead of using rule-based systems or mathematical models that require extensive computational steps, the system employs trained neural networks that can rapidly process security data and produce forecasts, significantly reducing processing time while maintaining high predictive accuracy.
3Ease of operation
If traditional security evaluation methods are used, then implementation is straightforward, but the ability to prevent security disruptions is inadequate
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring actual security posture and comparing it with forecasted health status. The system uses temporal metrics that capture changes over time and feeds this information back into the neural network for continuous refinement of predictions. This feedback loop enables the system to adapt to emerging threats and improve its preventive capabilities while maintaining ease of operation through automated processes.
Data Source
AI summary
The present invention relates to a method for forecasting health status of a distributed network by an artificial neural network comprising the phase of identifying one or more sites, one or more assets of the sides and the links between the identified assets in said distributed network, comprising the phase of evaluating the actual health status of each of the identified assets, the phase of evaluating the actual health status of each of said identified sites and the phase of forecasting, by the artificial neural network, the subsequent health status of each of the identified sites according to a forecasting function based on a set of values comprising the actual asset health status rank, the actual asset infection risk, the actual asset infection factor, the actual site health status rank and the actual site infection risk.