Dynamic Authentication Frequency and Challenge Type Adjustment
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Solution Overview
Problem
Existing authentication systems for data-processing systems lack dynamic adjustment of authentication frequency and challenge type based on environmental and physiological properties, leading to potential security vulnerabilities, especially in situations where access may be compromised.
Innovation Solution
The system determines authentication frequency and challenge type by utilizing environmental sensors (e.g., noise level, luminosity) and physiological sensors (e.g., heart rate, blood pressure) to adapt security measures, such as increasing frequency and strengthening challenge types in risky situations, and selecting appropriate challenge methods based on current and historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If authentication frequency is increased to enhance security, then security is improved, but user convenience deteriorates
Solution Approach 1:
The authentication frequency and challenge type are made dynamic by continuously monitoring environmental properties (noise level, luminosity, temperature) and physiological properties (heart rate, blood pressure, respiration rate). The system adjusts authentication parameters in real-time based on detected conditions, transitioning between high-security modes in risky environments and low-security modes in safe environments, thereby resolving the contradiction between security and convenience
Solution Approach 2:
The system changes authentication parameters (frequency and challenge type) based on detected environmental and physiological parameters. When environmental properties indicate high risk (e.g., noisy environment, dark location) or physiological properties show signs of distress, the system increases authentication frequency and strengthens challenge types. Conversely, when conditions are safe, authentication is reduced, balancing security with user convenience
2Reliability
If stronger challenge types are used in high-risk situations, then security is improved, but system complexity increases
Solution Approach 1:
The challenge type is dynamically selected based on current environmental and physiological conditions. The system maintains a pool of available challenge types (e.g., password entry, fingerprint scan, voice recognition, retinal scan) and selects the most appropriate one based on real-time detections. This dynamic selection allows the system to use strong authentication methods only when necessary, reducing overall system complexity while maintaining high security when needed
Solution Approach 2:
The system changes challenge type parameters based on detected conditions. In high-risk environments or when physiological distress is detected, the system selects more challenging authentication methods. The parameter changes are managed through a structured decision-making framework that maps environmental/physiological conditions to appropriate challenge types, preventing uncontrolled complexity expansion
3Measurement precision
If authentication is based on multiple sensor types, then accuracy is improved, but device complexity increases
Solution Approach 1:
The system implements a multi-functional authentication framework that can utilize various sensor types (environmental sensors for noise level, luminosity, temperature; physiological sensors for heart rate, blood pressure, respiration rate) depending on the detection needs. This universal approach allows the system to achieve high authentication accuracy by combining multiple data sources while managing complexity through a unified processing architecture that selects and integrates relevant sensors based on current conditions
Data Source
AI summary
An apparatus and method are disclosed for determining authentication frequency (i.e., the length of time between authenticating and re-authenticating a user) and challenge type (e.g., username/password, fingerprint recognition, voice recognition, etc.) based on one or more environmental properties (e.g., ambient noise level, ambient luminosity, temperature, etc.), or one or more physiological properties of a user (e.g., heart rate, blood pressure, etc.), or both. Advantageously, the illustrative embodiment enables authentication frequency and challenge type to be adjusted based on the likelihood of malicious activity, as inferred from these properties. In addition, the illustrative embodiment enables the authentication challenge type to be tailored to particular environmental conditions (e.g., noisy environments, dark environments, etc.).