Social Network Analysis of File Access Logs for Permission Management
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Solution Overview
Problem
Network administrators face challenges in managing and securing large network file systems with complex hierarchies and numerous users, as they struggle to determine appropriate file access permissions and identify anomalous activity due to the sheer volume of data and user interactions.
Innovation Solution
A social network analysis method that identifies relationships between users based on file access logs, generating a graph to represent collaboration information, with weighted values indicating the level of collaboration, and hierarchical clustering to group users and detect outliers, facilitating improved permission management and anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual management of file access permissions is implemented in a large network file system, then administrators can directly control and adjust permissions, but the complexity and time required to manage and secure the system increases significantly
Solution Approach 1:
The patent introduces an intermediary system that automatically analyzes file access logs and generates permission recommendations. This intermediary processing layer between raw log data and administrator decisions reduces the direct burden on administrators while maintaining reliable permission management through data-driven insights.
Solution Approach 2:
The system enables self-service by automatically analyzing access patterns and generating permission recommendations without requiring manual administrator intervention for each decision. The automated analysis of file access logs allows the system to serve itself in identifying appropriate permission configurations.
2Reliability
If comprehensive monitoring of all user file access is implemented, then anomalous or unauthorized activity can be detected, but the difficulty of analyzing and identifying patterns increases due to the volume of data
Solution Approach 1:
The patent extracts relevant patterns and anomalies from the vast amount of file access log data by focusing on specific metrics and behaviors. Instead of analyzing all raw data, the system extracts meaningful insights such as access frequency, user relationships, and deviation from normal patterns, making anomaly detection feasible.
Solution Approach 2:
The system transforms raw file access log data into meaningful parameters and metrics for analysis. By changing the parameters from raw log entries to aggregated statistics and behavioral patterns, the system makes the data more manageable and easier to analyze for anomalies.
3Loss of information
If detailed analysis of file access logs is performed to identify user relationships, then collaboration information can be determined, but the time and computational resources required increase
Solution Approach 1:
The patent performs preliminary analysis by pre-processing file access logs and establishing user relationship patterns in advance. This preliminary action creates a foundation of known collaboration patterns that can be quickly queried and updated, reducing the time required for detailed analysis when needed.
Data Source
AI summary
An analyzer module may identify a plurality of users and a plurality of files that have been accessed by at least one of the users. Pairs of users may be identified where each user of a pair has accessed at least one same file. A weight value may be calculated for each of the identified pairs. The weight value may be calculated based on a number of same files that each of the users of an identified pair have accessed. Collaboration information associated with the users may be determined based on the weight values.