Dynamic Access Control via Standard Deviation Thresholds
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Solution Overview
Problem
Existing systems lock out users from online accounts inappropriately due to static attempt limits, wasting time and network resources as users try to access accounts, without considering the normal distribution of access attempts.
Innovation Solution
A system that determines whether the distribution of access attempts is normal, calculates the standard deviation, and performs control operations based on a threshold of standard deviation to manage access attempts more accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a static account lockout policy is implemented, then security against brute force attacks is improved, but false positive lockouts occur and user access is blocked inappropriately
Solution Approach 1:
The patent implements a dynamic account lockout policy that adjusts the number of allowed access attempts based on real-time analysis of access patterns. Instead of a fixed static threshold, the system continuously monitors access frequency and dynamically determines when to trigger lockout, allowing legitimate users with unusual access patterns to avoid false positive lockouts while maintaining security against coordinated attacks.
Solution Approach 2:
The system changes the parameter of lockout threshold from a static value to a dynamic value that adapts based on observed access patterns. By analyzing historical access data and current request rates, the system adjusts the effective threshold adaptively, transforming the rigid parameter into a flexible one that responds to actual usage conditions.
2Reliability
If a static account lockout policy is implemented, then security is improved, but network resources are wasted due to repeated failed access attempts
Solution Approach 1:
The system implements feedback mechanisms that continuously monitor access patterns and use this information to adjust lockout decisions in real-time. By analyzing the rate and pattern of access attempts, the system can identify coordinated attack patterns and adjust the effective threshold dynamically, preventing resource exhaustion from automated attacks while avoiding unnecessary lockouts that would waste resources.
Solution Approach 2:
The dynamic adjustment of lockout thresholds allows the system to respond adaptively to varying attack intensities. When attack patterns intensify, the system can lower the effective threshold to block further waste of network resources, while during normal operation it maintains higher thresholds to avoid false positives.
3Reliability
If a static account lockout policy is implemented, then security is improved, but user time is wasted when legitimate users are inappropriately locked out
Solution Approach 1:
The system transforms the static lockout threshold parameter into a dynamic one that adapts to individual user behavior patterns. By learning and adapting to each user's typical access frequency and patterns, the system adjusts thresholds personalized to each user, preventing legitimate users from triggering lockouts due to their normal but unusual access patterns.
Solution Approach 2:
The system enables users to effectively set their own lockout thresholds through their usage patterns. By continuously learning from each user's behavior, the system adapts to individual needs and preferences, allowing users to maintain access during their normal usage patterns while the system automatically protects against attacks targeting their accounts.
Data Source
AI summary
Provided are computer-implemented methods that may include receiving, via a communication network, a request to perform an online action from a user device; retrieving data associated with a number of times the user device performed the online action within at least one time interval; determining whether a dataset associated with a number of times a plurality of user devices have performed the online action within the at least one time interval is normally distributed; and determining a standard deviation associated with the number of times the user device performed the online action within the at least one time interval in response to determining that the dataset is normally distributed, and performing a control operation associated with the request to conduct the online action based on a threshold of standard deviation. Systems and computer program products are also provided.