Mandatory Access Control Policy Generation via Machine Learning
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Solution Overview
Problem
Existing operating systems lack the ability to automatically generate mandatory access control policies for new applications, leading to potential security threats and resource wastage due to the execution of unnecessary files.
Innovation Solution
A method utilizing heuristic and machine learning models to identify required and unnecessary files within an application, generating rules to block unnecessary files and create a mandatory access control policy, which is then implemented by the operating system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional operating systems are used without automated access control policy generation, then device complexity remains low and ease of operation is maintained, but security reliability is compromised and computing resources are wasted due to execution of unnecessary files
Solution Approach 1:
The system enables self-service by automatically generating mandatory access control policies through machine learning models that analyze application behavior and file requirements, eliminating the need for manual security policy configuration while enhancing security reliability
Solution Approach 2:
The system changes the parameter of policy generation from static manual configuration to dynamic automated generation using heuristic and machine learning models, transforming how access control policies are created and applied
2Extent of automation
If manual access control policy generation is used, then automation extent is low, but manufacturing precision of security policies can be maintained through expert analysis, whereas automated generation without ML models would reduce precision and increase errors
Solution Approach 1:
The system replaces manual expert analysis (mechanical human process) with automated machine learning models that process application behavior data, achieving both high automation extent and maintained precision through algorithmic analysis of file execution patterns
Solution Approach 2:
The system implements feedback mechanisms where the machine learning models continuously learn from observed application behavior and file execution patterns, improving policy generation accuracy through iterative optimization based on actual system performance
3Productivity
If all files in an application are allowed to execute, then ease of operation is maintained and security policy configuration is simple, but computing resources are wasted and security threats increase due to execution of unnecessary files
Solution Approach 1:
The system extracts and identifies unnecessary files from applications using machine learning analysis of file execution patterns, separating required files from unnecessary ones to enable selective blocking while maintaining application functionality and improving resource efficiency
Data Source
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AI summary
A device processes, with a model, an application to identify a set of file paths with process identifiers. The device identifies patterns associated with the set of file paths with process identifiers, and determines positions of random elements in each file path of the set of file paths with process identifiers. The device processes the patterns and the positions of the random elements to train a machine learning model, and utilizes the machine learning model to generate a first set of rules to identify files required for execution of the application, and a second set of rules to identify files not required for execution of the application. The device generates a mandatory access control policy based on the first set of rules and the second set of rules, and provides the mandatory access control policy to be implemented by an operating system of a client device.