Dynamic Session Timeout Prediction Engine
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Solution Overview
Problem
Conventional access management systems use static timeout parameters, which pose security risks due to their non-user-specific nature, leading to potential session hijacking and poor user experience, as well as increased administrative burdens from stale sessions.
Innovation Solution
Implementing a machine learning-based prediction engine to determine user-specific timeout parameters based on observed user behavior, using models like neural networks or Bayesian networks trained on user activity history to set session and idle timeouts dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a long timeout parameter is used, then user convenience is improved, but security risk increases due to session hijacking opportunities
Solution Approach 1:
The patent applies dynamics by transitioning from static timeout parameters to dynamic timeout parameters that automatically adjust based on user behavior patterns. The system learns user activity patterns and adapts timeout values in real-time, making the timeout both user-friendly (long enough for legitimate use) and secure (short enough to prevent hijacking).
Solution Approach 2:
The patent changes the timeout parameter from a fixed value to a variable value that changes based on observed user behavior. The system monitors user activity and adjusts the timeout parameter dynamically, allowing it to extend during active use and shorten during inactivity, thus resolving the contradiction between convenience and security.
2Object-affected harmful factors
If a short timeout parameter is used, then security risk is reduced, but user experience deteriorates due to frequent logouts
Solution Approach 1:
The system uses dynamics to adjust timeout values based on real-time user behavior. When user activity is detected, the timeout is extended, preventing frequent logouts and improving user experience. When inactivity is detected, the timeout is shortened, maintaining security. This dynamic adjustment resolves the contradiction between security and user experience.
Solution Approach 2:
The patent implements feedback by continuously monitoring user behavior and using this information to adjust timeout parameters. The system receives feedback from user activity patterns and modifies timeout values accordingly, creating a closed-loop system that balances security and user experience based on actual usage.
3Device complexity
If static timeout parameters are used for all users, then system complexity is reduced, but security risk increases due to one-size-fits-all approach
Solution Approach 1:
The patent applies self-service by enabling the system to automatically learn user behavior patterns and generate personalized timeout parameters without requiring manual configuration for each user. The system serves itself by autonomously adapting to individual user needs, reducing administrative complexity while improving security through customization.
Solution Approach 2:
The system performs preliminary action by pre-learning user behavior patterns during initial usage and preparing personalized timeout parameters before security incidents can occur. This proactive approach allows the system to have customized security parameters ready in advance, reducing complexity while enhancing security.
Data Source
AI summary
Techniques for described for generating session-related timeout parameters that are user-specific in value. A user-specific timeout parameter offers several advantages over a static timeout parameter, including minimized the risk of session hijacking, fewer stale sessions to manage, and timeout parameters that more closely match the user's actual behavior. A value for a timeout parameter can therefore depend on information stored for a specific user. The stored information can indicate user behavior observed over a period of time encompassing multiple sessions and/or multiple accesses to the same or different resources. In certain embodiments, a value for a timeout parameter is determined by a prediction engine implemented using a machine learning (ML) model. The ML model may determine the timeout parameter based on information obtained records associated with the user for whom the timeout parameter value is being determined, as well as information from records associated with other users.