Interaction Graph Security for Dynamic Access Control
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Solution Overview
Problem
Traditional methods for securing files and records rely on user-set passwords and permission levels, which are inadequate in preventing unauthorized access and malicious activities, as they do not dynamically adapt to user interaction patterns.
Innovation Solution
A computer-implemented method and system that creates an interaction graph based on user interactions with files and records, computing interaction weights to flag and block access attempts by users with low interaction weights, thereby filtering security alerts and preventing unauthorized access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional permission levels and passwords are used for securing files, then access control is implemented, but the system cannot dynamically adapt to user interaction patterns and cannot effectively identify malicious activities
Solution Approach 1:
The system dynamically computes interaction weights between users and files based on observed interaction patterns, rather than using static permission levels. The interaction graph is continuously updated to reflect current user behavior, enabling the security system to adapt to changing interaction patterns and identify anomalies in real-time.
Solution Approach 2:
The system monitors user interactions with files and uses this feedback to compute interaction weights that inform access control decisions. By continuously observing interaction patterns and adjusting security responses based on this feedback, the system can distinguish between legitimate and malicious activities more effectively.
2Ease of operation
If static permission levels are assigned to users, then access control is simplified, but the system generates false security alerts and cannot distinguish between legitimate and malicious access attempts
Solution Approach 1:
Instead of using fixed permission levels, the system changes the parameter used for access control decisions from static role-based permissions to dynamic interaction weights. These weights are computed based on observed interaction patterns, allowing the system to maintain simplicity while reducing false alerts through context-aware security decisions.
3Reliability
If interaction graphs are computed and analyzed, then dynamic access control and malicious activity detection are achieved, but system complexity increases
Solution Approach 1:
The system automatically computes interaction graphs and interaction weights by observing user interactions without requiring manual configuration or intervention. The security system serves itself by autonomously learning interaction patterns and making access control decisions based on computed weights, reducing the operational complexity despite the advanced algorithms involved.
Data Source
AI summary
There is provided a data security method, comprising: creating an interaction graph, by: analyzing collected interaction events between users and between users and files and/or records, wherein a respective node of the interaction graph represents a specific one of a user, a record, and a file, wherein a respective edge indicates an interaction between respective users or between a respective user and a respective file and/or record, wherein an interaction weight assigned to the respective edge indicates an amount of the interaction, monitoring an attempt by a target user to access a target file and/or record, computing a target interaction weight between the target user and the target file and/or record from the interaction graph, and in response to the target interaction weight being below a target threshold, at least one of: filtering security alerts, and blocking access by the target user to the target file and/or record.