VM Security via Restricted Boltzmann Machine Graph Analysis
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Solution Overview
Problem
Virtualized computer systems in cloud environments are vulnerable to cyber-attacks, including malware and botnet infections, due to insufficient security measures and default configurations, which can lead to compromised VMs being used for coordinated attacks.
Innovation Solution
A computer-implemented method using a machine learning algorithm, specifically a restricted Boltzmann machine, to identify susceptible VM configurations by analyzing relationships between VM parameters and attack characteristics, generating a directed graph to determine vulnerable parameters, and implementing protective measures to mitigate attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security measures and default configurations are used to protect VMs, then implementation simplicity is maintained, but security reliability is insufficient leading to vulnerable configurations
Solution Approach 1:
The system performs self-diagnosis by automatically analyzing VM configurations against the directed graph model to identify vulnerable parameters, eliminating the need for manual security audits and enabling automated protective measure implementation
Solution Approach 2:
The directed graph data structure is pre-computed from training data to encode attack sequences and vulnerable configuration relationships, enabling rapid vulnerability assessment without performing complex analysis during actual security evaluation
2Reliability
If comprehensive security analysis is performed to identify all vulnerable VM configurations, then security coverage is improved, but analysis time and computational resources increase
Solution Approach 1:
The patent replaces traditional mechanical security analysis methods with machine learning-based automated analysis using directed graphs, enabling comprehensive security coverage to be achieved through algorithmic processing rather than manual or traditional systematic analysis
Solution Approach 2:
The system changes the analysis approach by representing security configurations as graph structures with nodes and edges, transforming the problem from exhaustive configuration checking to graph traversal and pattern matching, which significantly reduces computational complexity
3Measurement precision
If detailed VM configuration parameters are analyzed to identify attack vulnerabilities, then detection precision is improved, but data processing complexity increases
Solution Approach 1:
The system extracts only the critical configuration parameters that are relevant to security vulnerabilities from the complete VM configuration set, focusing analysis on high-risk parameters identified through training data rather than processing all configuration details
Data Source
AI summary
A computer implemented method to identify one or more parameters of a configuration of a target virtual machine (VM) in a virtualized computing environment used in a security attack against the target VM, the security attack exhibiting a particular attack characteristic, is disclosed.