Dynamic Authentication Method Selection Based on Risk Assessment
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Solution Overview
Problem
Existing authentication methods for user devices accessing services do not effectively adapt to changing security risks, potentially leading to vulnerabilities and compromised security.
Innovation Solution
A system that analyzes device security behavior and network intelligence data using machine learning to dynamically determine and recommend a more secure authentication method based on identified risk levels, allowing for real-time adjustments to ensure secure access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If third-party user credentials are used for authentication to provide user convenience and lower barrier for acquisition, then ease of operation is improved, but security risk increases
Solution Approach 1:
The system dynamically adjusts authentication methods based on real-time risk assessment. Instead of using a static authentication approach, the system evaluates device security behavior, network intelligence data, and threat intelligence to determine the appropriate authentication method for each access attempt, thereby adapting security measures to current conditions while maintaining user convenience.
Solution Approach 2:
The system changes authentication parameters dynamically by switching between different authentication methods (e.g., from third-party credentials to more secure methods) based on identified risk levels. This parameter change allows the system to respond to varying security conditions while preserving ease of operation when risks are low.
2Reliability
If dynamic risk assessment and authentication method determination are implemented, then security is improved, but device complexity increases
Solution Approach 1:
The system introduces an intermediary authentication management component that sits between the user device and service providers. This intermediary handles the complex tasks of risk assessment, machine learning analysis, and authentication method determination, thereby isolating the complexity from both the user device and service providers while improving overall security.
Solution Approach 2:
The system implements feedback loops where authentication outcomes, security events, and risk assessments are continuously monitored and fed back into the machine learning models. This feedback mechanism enables the system to automatically learn and improve its security decisions over time, reducing the need for manual configuration and managing complexity through self-optimization.
3Object-affected harmful factors
If real-time security monitoring and machine learning analysis are performed, then security risk mitigation is improved, but use of energy increases
Solution Approach 1:
The system applies partial monitoring and analysis by focusing computational resources on specific high-risk scenarios, device behaviors, or authentication attempts rather than continuously analyzing all traffic. Machine learning models evaluate only the necessary parameters for risk assessment, performing excessive analysis only when initial indicators suggest potential threats, thereby reducing overall energy consumption while maintaining effective security monitoring.
Data Source
AI summary
A device, method or executable instructions that include receiving, over a network, an authentication request from a user device for performing a function utilizing a first authentication method, obtaining network intelligence data for a mobile network over the network, and identifying a risk for each of multiple authentication methods in response to analyzing device security behavior and the network intelligence data. Further embodiments include identifying a first risk for the first authentication method and identifying a second risk for the function, determining the first risk is higher than the second risk, and identifying a second authentication method that is associated with the second risk. Additional embodiments include notifying the user device of the second risk for the function, and providing a recommendation to the user device to utilize the second authentication method to perform the function. Other embodiments are disclosed.