Access Control Manager Configuration via Log Mining
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Solution Overview
Problem
Managing user access to resources in a cloud environment is complicated due to the complexity of user permissions, which can lead to vulnerabilities from excessive access or inefficiencies from insufficient access, as existing identity and access management services struggle to maintain proper permissions as users change roles or resources grow.
Innovation Solution
An access control configuration manager generates recommended permission levels by analyzing user and resource interactions, forming clusters through biclustering to create administrator-modifiable security and resource groups, and automatically deploying permission levels into the access control manager.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fine-grained access permissions are implemented for each user and resource, then security is improved and users can perform tasks efficiently, but the complexity of managing user permissions increases significantly
Solution Approach 1:
The patent segments the permission management system into multiple components: access data collection module, matrix generation module, biclustering module, and permission level determination module. Each component handles a specific aspect of the permission management process, making the overall system more manageable despite the fine-grained nature of permissions
Solution Approach 2:
The system automatically generates permission recommendations by analyzing access data and applying biclustering algorithms, eliminating the need for manual permission configuration. The access control manager self-adjusts permissions based on observed access patterns, reducing the burden on administrators while maintaining security
2Adaptability or versatility
If manual permission management is used, then flexibility in configuring access rights is maintained, but the time and effort required to maintain permissions increases as users change roles or resources grow
Solution Approach 1:
The system continuously collects access data from the cloud environment and uses this feedback to automatically update permission recommendations. As users change roles or new resources are added, the system observes the new access patterns and adjusts permissions accordingly, maintaining adaptability without requiring manual intervention
Solution Approach 2:
The system proactively generates permission level recommendations before administrators need to manually configure them. By analyzing access data and predicting appropriate permission levels in advance, the system reduces the time required to maintain permissions while preserving configuration flexibility through administrator review and modification capabilities
Data Source
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AI summary
An access configuration for an access control manager is generated. Access data including users, resources, and actions the users performed on the resources is received into a matrix. Clusters of the matrix are formed to produce ranges of the users and ranges of the resources having selected permission levels based on the actions. Administrator-modifiable security groups are created based on the ranges of users and administrator-modifiable resources groups based on the ranges of resources.