Machine Learning Graph Analysis for Security Misconfiguration Prediction
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Solution Overview
Problem
Security misconfigurations and non-compliant cloud deployments are major reasons for compromised computing systems and security vulnerabilities, with traditional threat modeling processes being time-consuming and error-prone.
Innovation Solution
The use of machine learning to predict misconfigurations in computing systems by labeling graph nodes and links with security vulnerabilities and training algorithms to identify potential misconfigurations and suggest modifications to mitigate them.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional threat modeling processes are used to identify security misconfigurations, then security vulnerabilities can be detected, but the process is time-consuming and error-prone
Solution Approach 1:
The patent replaces manual threat modeling processes with an automated machine learning system. The system uses graph neural networks to automatically analyze computing system configurations, identify security misconfigurations, and generate remediation recommendations, eliminating the need for manual threat modeling while improving both speed and accuracy
Solution Approach 2:
The system performs self-service by automatically analyzing computing system configurations without requiring manual intervention. The machine learning model autonomously processes graph data, identifies vulnerabilities, and generates remediation strategies, allowing the system to serve itself rather than requiring human analysts to perform threat modeling
2Measurement precision
If manual security configuration review is performed, then misconfigurations can be identified, but the process is error-prone and time-consuming
Solution Approach 1:
The patent replaces manual security review processes with an automated machine learning system that uses graph neural networks to analyze configurations. The system automatically processes computing system graphs, identifies misconfigurations with high precision, and generates remediation recommendations, significantly improving both accuracy and speed compared to manual review
3Reliability
If conventional security design practices are used, then security vulnerabilities can be addressed, but the process is error-prone
Solution Approach 1:
The patent replaces complex manual security design processes with an automated machine learning system. The graph neural network automatically analyzes system configurations, identifies security issues, and generates remediation recommendations, simplifying the overall process while improving reliability and reducing errors associated with manual security design
Data Source
AI summary
Methods, apparatus, and program products that can predict misconfigurations in a computing system using machine learning are disclosed herein. One method includes labeling one or more graph nodes or link nodes of a data graph of a computing system that includes one or more security vulnerabilities with a node label or link label, respectively, in which each node label represents the first security vulnerabilities associated with a particular graph node and each link label represents the second security vulnerabilities associated with a particular link node. The method further includes utilizing the graph node(s) or the link node(s) to train a machine learning algorithm to predict one or more misconfigurations in the computing system based on the security vulnerabilities and determining one or more modifications to the computing system for mitigating the one or more misconfigurations. Apparatus and program products that include and/or perform the methods are also disclosed herein.