Dynamic Attribute Prediction for Access Control Systems
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Solution Overview
Problem
Current systems for controlling access to secured resources do not account for changes in attributes associated with target entities, leading to inefficient access and erroneous denial of access.
Innovation Solution
A risk assessment system that predicts the rate of change in attributes for target entities by generating models for different attribute tiers, ranking entities based on predicted changes, and assigning scores to determine access eligibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current access control systems use static attribute verification, then access decisions are simple and fast, but they result in erroneous denial of access when attributes change over time
Solution Approach 1:
The patent transforms static attribute verification into dynamic attribute verification by continuously monitoring attribute changes over time. The system now considers temporal variations in attributes such as device characteristics, user behavior patterns, and environmental context, allowing access decisions to adapt as attributes evolve rather than relying on fixed snapshots.
Solution Approach 2:
The system performs preliminary attribute baseline establishment during a learning phase before formal access control begins. By pre-collecting and analyzing attribute data to establish normal ranges and patterns, the system prepares prediction models in advance that can quickly evaluate whether current attributes deviate from expected values, enabling faster and more accurate real-time decisions.
2Reliability
If the system implements dynamic attribute monitoring and prediction models, then access control accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary attribute baseline establishment during a learning phase before formal access control begins. By pre-collecting and analyzing attribute data to establish normal ranges and patterns, the system prepares prediction models in advance that can quickly evaluate whether current attributes deviate from expected values, enabling faster and more accurate real-time decisions.
Solution Approach 2:
The patent transforms static attribute verification into dynamic attribute verification by continuously monitoring attribute changes over time. The system now considers temporal variations in attributes such as device characteristics, user behavior patterns, and environmental context, allowing access decisions to adapt as attributes evolve rather than relying on fixed snapshots.
3Measurement precision
If the system collects and analyzes extensive attribute data for prediction, then prediction accuracy improves, but memory and data storage requirements increase
Solution Approach 1:
The system extracts only the most relevant and discriminative features from extensive attribute data for storage and analysis. By identifying and retaining key attributes that most strongly correlate with security risk and access legitimacy, the system reduces storage requirements while maintaining prediction accuracy. Less critical attributes are either discarded or stored in compressed forms.
4Adaptability or versatility
If the system uses multiple attribute tiers and segmentation, then access control granularity improves, but device and algorithm complexity increase
Solution Approach 1:
The patent divides the attribute space into multiple tiers or segments based on security criticality and variability. High-criticality attributes with low variability receive stricter verification, while lower-criticality attributes with high variability receive more flexible handling. This segmented approach allows the system to apply different verification intensities to different attributes, improving overall flexibility without uniformly increasing complexity across all checks.
Data Source
AI summary
A system can generate a risk assessment associated with a target entity. The system can determine an attribute tier for each entity in a set of entities. For each attribute tier, the system can: generate a model configured to predict a percent change in the attribute over a time period for each entity in the respective attribute tier; determine the percent change in the attribute for each entity in the respective attribute tier using the model associated with the respective attribute tier; rank each entity in the attribute tier based on the predicted percent change in the attribute; and assign a score to each entity in the attribute tier based on the rank of the respective entity and on a preconfigured distribution. The system can determine a risk indicator based, in part, on the score.