Dynamic VM Security Policy via ML Vulnerability Classification
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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 generates training data for a machine learning algorithm to determine security vulnerabilities of VMs by using a reverse decay function to associate temporally earlier VM configuration vectors with vulnerability vectors indicating vulnerability to a lesser degree, allowing for dynamic security configuration updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If security configurations for VMs are fixed and infrequently updated, then system stability and management simplicity are maintained, but the security effectiveness deteriorates against evolving threats
Solution Approach 1:
The patent implements dynamic security configurations that automatically adapt to changing threats by using machine learning models to generate updated security policies in real-time, transforming the static security configuration into a dynamic system that evolves with emerging vulnerabilities and attack patterns
Solution Approach 2:
The system employs self-service mechanisms where the machine learning model autonomously generates security configuration updates without requiring manual intervention, automatically detecting vulnerabilities and producing adapted security policies that respond to evolving threats
2Reliability
If security configurations are frequently updated to address new vulnerabilities, then security effectiveness improves, but system complexity and computational resources increase
Solution Approach 1:
The patent introduces a machine learning model as an intermediary that automatically processes vulnerability data and generates security configuration updates, eliminating the need for complex manual configuration management and reducing the burden on system administrators
Solution Approach 2:
The system implements feedback loops where security event data and vulnerability information continuously feed into the machine learning model, which then generates updated security configurations that are applied and monitored, creating a closed-loop system that automatically adapts without increasing operational complexity
3Measurement precision
If comprehensive security monitoring and logging are implemented, then vulnerability detection capability improves, but data processing load and storage requirements increase
Solution Approach 1:
The patent extracts only the most relevant features from comprehensive security event data using the machine learning model, identifying and processing only the critical vulnerability indicators while discarding redundant information, thereby maintaining detection precision without proportionally increasing data processing load
Data Source
AI summary
A computer implemented method to generate training data for a machine learning algorithm for determining security vulnerabilities of a virtual machine (VM) in a virtualized computing environment is disclosed. The machine learning algorithm determines the vulnerabilities based on a vector of configuration characteristics for the VM.