Cloud Permission Anomaly Detection Using Similarity Grouping
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Solution Overview
Problem
The challenge of managing and identifying abnormal permissions in cloud environments, where enterprise users have numerous accounts with varying permissions, is exacerbated by the difficulty in manually managing each account and the increased risk due to unconsumed or anomalous permissions.
Innovation Solution
A method involving data partitioning, similarity calculations, and genetic algorithms to identify abnormal permissions by grouping users based on their permissions, using asymmetrical similarity scores to detect and remediate abnormal permissions, reducing runtime and memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional account management approaches are used (removing unused permissions based on login activity), then security risks from unconsumed permissions are reduced, but user experience deteriorates due to unnecessary account removal and churn
Solution Approach 1:
The system changes the parameter for identifying abnormal permissions from simple login activity thresholds to asymmetrical similarity scores that compare permission sets between user groups. This allows the system to distinguish between legitimately occasional users and those with abnormal permission patterns, thereby maintaining security while preserving user experience.
Solution Approach 2:
The patent replaces the mechanical approach of removing accounts based on login frequency with an automated analytical system that calculates similarity scores and identifies abnormal permissions through algorithmic comparison. This substitution enables more nuanced decision-making that balances security requirements with user experience considerations.
2Measurement precision
If manual management of each user account is performed, then permission accuracy is improved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system segments the large set of user accounts into smaller groups based on similarity in permission patterns. By analyzing groups rather than individual accounts in isolation, the system achieves comprehensive permission management at scale, reducing the time required while maintaining accuracy through systematic comparison within segments.
Solution Approach 2:
The patent creates a universal system that handles permission management for all user accounts through a single automated process. The asymmetrical similarity calculation methodology serves multiple functions: identifying abnormal permissions, grouping users, and flagging accounts for review, thereby eliminating the need for separate manual management procedures.
3Measurement precision
If comprehensive permission analysis is performed across all user accounts, then detection accuracy of abnormal permissions is improved, but computational load and processing time increase
Solution Approach 1:
The system divides the computational task of permission analysis into segments by grouping users with similar permission patterns. Instead of performing exhaustive comparisons across all user pairs, the system only needs to analyze within groups and between group representatives, significantly reducing computational complexity while maintaining detection accuracy.
Solution Approach 2:
The patent merges the analysis of multiple user accounts by identifying and treating users with identical or similar permission sets as a single analytical unit. This combining approach reduces redundant calculations and lowers computational resource usage while preserving the ability to detect abnormal permissions that differ from the merged group pattern.
Data Source
AI summary
Systems and methods for detecting abnormal permissions in a cloud environment include obtaining data associated with a cloud environment; partitioning the data into a plurality of groups and windows, wherein each of the windows includes one or more groups; determining one or more groups within a window having a similarity; and identifying one or more groups as having abnormal permissions based on the similarity. Based on a similarity score being above a threshold, the systems can be adapted to identify the differences, i.e., the one or more extra permissions in one of the groups, and automatically remove these permissions.