Predictive Access Rights for Load Management
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Solution Overview
Problem
Load management systems face challenges in efficiently detecting and managing unauthorized access, particularly at large scales, as existing methods are inefficient and burdensome, leading to overloaded servers and queues during peak access periods.
Innovation Solution
A computer-implemented method using machine-learning models to predict user affinities for resources, generating resource-affinity parameters to dynamically assign access rights, thereby reducing the likelihood of unauthorized access and optimizing network resource usage by pre-assigning access rights to likely users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If access rights are assigned on a first-come-first-served basis, then the system is simple to operate, but unauthorized activity increases and system security deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-assigning access rights to user devices based on predicted resource affinity before actual resource access occurs. The load management system analyzes user behavior patterns and pre-grants access rights to legitimate users, so that when resource access is needed, authorized users can access immediately without waiting in queues, while unauthorized users (like bots) are blocked by throttle factors. This resolves the contradiction by maintaining security through predictive analysis while preserving ease of operation for legitimate users.
2Measurement precision
If the system processes all access requests in real-time, then access control accuracy is improved, but server load increases and processing efficiency decreases
Solution Approach 1:
The system performs preliminary analysis of user behavior patterns and pre-determines resource affinity scores before the actual resource access requests occur. By analyzing user device characteristics, access patterns, and resource usage history in advance, the system pre-identifies legitimate users and assigns them access rights proactively. This shifts the computational burden from real-time request processing to offline or near-offline pattern analysis, maintaining high detection accuracy while improving real-time processing efficiency.
Solution Approach 2:
The system implements periodic throttle factor updates and access right reassignments based on evolving user behavior patterns. Instead of continuously analyzing every request in real-time, the system periodically refreshes its understanding of user affinities and adjusts access rights accordingly. This periodic action maintains security accuracy while reducing the continuous computational load on servers, thereby improving overall processing efficiency.
3Productivity
If access rights are pre-assigned to reduce server load, then processing efficiency is improved, but the risk of granting access to unauthorized users increases
Solution Approach 1:
The system performs preliminary risk assessment and user verification before pre-assigning access rights. By analyzing user device characteristics, access patterns, and resource affinity in advance, the system identifies legitimate users with high confidence and pre-grants them access rights. This preliminary action maintains security by verifying user legitimacy before automation, reducing the risk of unauthorized access while improving processing efficiency for confirmed legitimate users.
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor resource access patterns and user behavior after access rights are pre-assigned. When unusual patterns or potential unauthorized access attempts are detected, the system adjusts throttle factors and can revoke or modify access rights dynamically. This feedback loop maintains reliability by detecting and preventing unauthorized access while preserving the efficiency benefits of pre-assignment for legitimate users.
Data Source
AI summary
Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.