Automated Security Scope Definition via TF-IDF Feature Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional security systems for computing environments, including machine learning environments, rely heavily on manual registration and administration by skilled IT administrators, which is inefficient and prone to errors, especially in complex and dynamically changing environments, leading to potential gaps in protection and improper assignment of security scopes and services.
Innovation Solution
The implementation of a Term Frequency-Inverse Document Frequency (TF-IDF) Model that automatically determines the importance and classification of machines or components within a computing environment, allowing for automated feature selection and security protection without the need for manual intervention, focusing on monitoring intended states and raising alarms for anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual registration of machines or components is performed by IT administrators, then security protection can be provided to registered components, but the process is highly dependent on administrator knowledge and experience, leading to potential gaps in protection and improper assignment of security scopes
Solution Approach 1:
The system automatically discovers machines and components within the computing environment and registers them with the security system without requiring manual intervention. The security system autonomously identifies important features and determines appropriate security scopes, eliminating dependency on administrator knowledge and experience while ensuring comprehensive protection coverage.
2Quantity of substance
If the number of machines or components in the computing environment increases, then the computing environment becomes more complex and valuable, but the likelihood of administrators missing or forgetting to register certain components increases
Solution Approach 1:
The system continuously monitors the computing environment for changes, automatically detecting newly added machines and components. This feedback mechanism ensures that as the environment grows in complexity and size, the security system adapts by automatically registering new components and adjusting security scopes, maintaining complete protection coverage without increasing administrative burden.
3Adaptability or versatility
If conventional security systems require constant upgrading of agents for discovery and monitoring, then the system can adapt to new threats, but the complexity and maintenance burden increase significantly
Solution Approach 1:
The system employs a universal discovery and monitoring approach that automatically adapts to different machines and components without requiring specialized agents for each type. The security system performs multiple functions including discovery, classification, and registration through a single integrated mechanism, eliminating the need for constant agent upgrading while maintaining high adaptability to new threats and environment changes.
Data Source
AI summary
A feature selection methodology is disclosed. In a computer-implemented method, components of a computing environment are automatically monitored, and have a feature selection analysis performed thereon. Provided the feature selection analysis determines that features of the components are well defined, a classification of the features is performed. Provided the feature selection analysis determines that features of the components are not well defined, a similarity analysis of the features is performed. Results of the feature selection methodology are generated.