Dynamic Authentication Threshold Adjustment
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Solution Overview
Problem
Conventional authentication attempt thresholds are arbitrary and do not adapt to individual user behavior, potentially compromising usability and security, as they do not account for the probability of successful authentication based on recentness and frequency of successful attempts.
Innovation Solution
A system and method that determine an authentication attempt threshold by analyzing the recentness and frequency of successful authentication attempts, allowing for a dynamic adjustment of the number of attempts based on the user's authentication history, thereby providing a personalized threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed authentication attempt threshold is used for all users, then security is simplified and easier to implement, but security effectiveness deteriorates because it does not adapt to individual user behavior patterns
Solution Approach 1:
The patent implements dynamic authentication thresholds that automatically adjust based on user behavior patterns. The system monitors authentication history, success rates, and temporal patterns to continuously adapt the threshold for each user, transforming the static security parameter into a dynamic one that responds to actual user characteristics and risk profiles.
Solution Approach 2:
The system changes the authentication attempt threshold parameter based on analyzed user behavior data. By calculating probability scores from authentication patterns and using these to adjust the threshold parameter dynamically, the system optimizes security effectiveness without requiring complex manual configuration or intervention.
2Reliability
If a low authentication attempt threshold is set, then security against brute force attacks is improved, but usability deteriorates because frequent users may be locked out due to temporary glitches or forgotten credentials
Solution Approach 1:
The patent applies different authentication thresholds to different users based on their individual behavior patterns. Instead of a uniform threshold, each user receives a personalized threshold tailored to their authentication history, success rates, and temporal patterns, allowing frequent users to benefit from higher thresholds while maintaining strict security for users with suspicious patterns.
Solution Approach 2:
The system continuously monitors authentication attempts and outcomes, using this feedback to adjust individual user thresholds dynamically. By analyzing authentication success rates, temporal patterns, and behavior changes, the system adapts thresholds in real-time, providing higher limits to trusted users while maintaining low limits for potentially compromised accounts.
3Ease of operation
If a high authentication attempt threshold is set, then usability is improved for infrequent users who may forget credentials, but security deteriorates because it allows more opportunities for brute force attacks
Solution Approach 1:
The system implements location-specific (user-specific) authentication thresholds based on individual behavior patterns. Each user's threshold is customized according to their authentication history, frequency, success rates, and temporal patterns, allowing infrequent users to receive higher thresholds while frequent users receive appropriately lower thresholds for enhanced security.
Solution Approach 2:
The authentication threshold parameter is dynamically changed based on analyzed user behavior data. The system calculates probability scores from authentication patterns and uses these to adjust the threshold parameter for each user, optimizing the balance between security and usability for every individual user rather than applying a static universal threshold.
4Ease of manufacture
If arbitrary fixed thresholds are used, then implementation is simpler, but security optimization deteriorates because thresholds do not account for user investment or visit frequency
Solution Approach 1:
The system automatically analyzes user authentication patterns and self-adjusts thresholds without requiring manual configuration or administrator intervention. The authentication system serves itself by monitoring its own data, calculating probability scores, and dynamically adjusting thresholds based on observed user behavior, eliminating the need for complex manual setup while achieving optimized security.
Data Source
AI summary
Systems and methods are provided for determining an authentication attempt threshold. Authentication systems often have predetermined authentication attempt thresholds that may not be sufficient for some users and do not necessarily provide any increased security. Systems and methods provided for determining an authentication thresholds described herein may determine the authentication threshold based on certain factors in a user's authentication attempt history that may provide information about a user's probability of a successful authentication to provide additional security for users more likely to successfully authenticate while providing additional assistance to users who may be less likely to successfully authenticate.