ML System Package Selection via Security Constraints
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Solution Overview
Problem
Current machine learning systems face challenges in managing security vulnerabilities of software packages used in their construction, leading to potential security risks and inefficiencies in production systems due to manual updates and lack of automated handling during model training.
Innovation Solution
A method for generating a machine learning system that selects software packages based on package-specific security vulnerability metadata and security vulnerability constraints, allowing for automated handling of security aspects during training and updates, thereby optimizing security and functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual updates and management of software packages are used in machine learning systems, then flexibility and control are maintained, but security vulnerability management becomes inefficient and time-consuming
Solution Approach 1:
The system performs automated security vulnerability management by self-evaluating package metadata, automatically selecting secure packages, and generating updated machine learning systems without requiring manual intervention for security updates
Solution Approach 2:
Security vulnerability constraints are defined in advance, and package metadata is pre-evaluated for security issues before system generation, allowing secure packages to be selected automatically during system construction rather than requiring post-deployment updates
2Reliability
If security vulnerability constraints are strictly enforced during package selection, then system security is improved, but the available pool of software packages is reduced
Solution Approach 1:
Security vulnerability constraints are defined as adjustable parameters that specify acceptable vulnerability thresholds. The system evaluates package metadata against these parameterized constraints, allowing flexible adjustment of security requirements based on specific needs while maintaining automated enforcement
Solution Approach 2:
Package metadata acts as an intermediary that carries security vulnerability information from software packages to the selection process. This metadata enables automated evaluation and filtering of packages based on security constraints without requiring direct manual inspection
3Measurement precision
If comprehensive security vulnerability metadata is collected and evaluated for all software packages, then security assessment accuracy is improved, but system complexity increases
Solution Approach 1:
Security vulnerability information is extracted from comprehensive package metadata into specific, evaluable constraints. The system extracts relevant security attributes from metadata and evaluates them against defined constraints, simplifying the processing while maintaining assessment accuracy
Data Source
AI summary
Disclosed herein is a computer implemented method for generating a machine learning system from software packages. The software packages comprise package specific security vulnerability metadata. The method comprises receiving a specification of the machine learning system, wherein the specification comprises security vulnerability constraints. The method further comprises selecting the software packages from a collection of software packages using the specification and by comparing the package specific security vulnerability metadata to the security vulnerability constraints. The method further comprises generating the machine learning system using the selected software packages. The method further comprises training the machine learning system using the specification.