ML-Based VM Security Configuration Adaptation
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Solution Overview
Problem
Security configurations for virtual machines (VMs) in virtualized computing environments are often fixed and infrequently updated, making them ineffective against evolving security threats and vulnerabilities.
Innovation Solution
A computer-implemented method using a machine learning algorithm to determine a security configuration for a target VM by training on vectors of configuration characteristics and associated vulnerability data, selecting appropriate security configurations based on generated vulnerability vectors, and considering current security threats to weight and prioritize vulnerability indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If security configurations are fixed and infrequently updated, then system stability is maintained, but security effectiveness deteriorates against evolving threats
Solution Approach 1:
The patent implements dynamic security configurations that automatically adapt to evolving threats through machine learning analysis. The system continuously updates vulnerability assessments and security policies based on real-time data, transforming static security configurations into dynamic, responsive ones that maintain effectiveness against changing threat landscapes.
Solution Approach 2:
The system employs self-service mechanisms where the machine learning model automatically analyzes vulnerability data, identifies security gaps, and generates updated security configurations without manual intervention. This autonomous adaptation enables the system to respond to emerging threats in real-time while maintaining operational stability.
2Adaptability or versatility
If machine learning algorithms are used to dynamically determine security configurations, then security adaptability improves, but system complexity increases
Solution Approach 1:
The patent introduces a machine learning model as an intermediary component that bridges the gap between raw vulnerability data and security configuration decisions. This intermediary automatically processes complex vulnerability assessments and translates them into actionable security policies, managing system complexity while enabling dynamic adaptation.
Solution Approach 2:
The system dynamically changes security configuration parameters based on machine learning analysis of vulnerability data. By automatically adjusting security parameters such as firewall rules, access controls, and encryption settings based on assessed risk levels, the system achieves adaptability without requiring complex manual configuration management.
3Productivity
If security configurations are frequently updated to address current threats, then security responsiveness improves, but system stability may deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where the machine learning model continuously monitors security event data, vulnerability assessments, and threat intelligence. This feedback loop enables the system to make informed, incremental configuration updates that respond to current threats while maintaining stability through data-driven decision-making rather than reactive changes.
Solution Approach 2:
The system performs preliminary vulnerability assessments and risk analyses before implementing security configuration changes. By pre-evaluating potential updates through machine learning models and testing their impact in simulated environments, the system can deploy security updates with confidence, improving responsiveness while minimizing disruption to system stability.
Data Source
AI summary
A computer implemented method to determine a security configuration for a target virtual machine (VM) in a virtualized computing environment, the method including training a machine learning algorithm to determine a vector of security vulnerabilities for the target VM based on a vector of configuration characteristics for the target VM, the machine learning algorithm being trained using training examples each including a configuration for a training VM and an associated vulnerability vector based on an observed security occurrence at the training VM, wherein each training example further includes an identification of one of set of security configurations for the training VM; selecting at least a subset of the set of security configurations and, for each security configuration in the subset, executing the machine learning algorithm with the vector of configuration characteristics for the target VM and an identification of the security configuration, so as to generate a set of vulnerability vectors including a vulnerability vector for each security configuration in the selected subset; and selecting a security configuration for the target VM based on the set of vulnerability vectors.