Knowledge Graph Attack Path Modeling for Network Risk Prioritization
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Solution Overview
Problem
Current network security solutions fail to comprehensively model attack paths that integrate both vulnerability exploitation and authentication service misconfigurations, neglecting the strategic behavior of attackers and lacking accurate prioritization based on the path of least resistance.
Innovation Solution
A system utilizing a knowledge graph ontology to combine vulnerability exploitation and authentication service misconfigurations with network reachability data, and a machine learning model to predict the path of least resistance using graph neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional vulnerability assessment methods are used to prioritize remediation based on individual endpoint severity ratings, then high severity vulnerabilities (e.g., CVSS 9) are addressed first, but the overall contextual risk and systemic connectivity of vulnerabilities is not captured, leading to potentially suboptimal remediation prioritization
Solution Approach 1:
The patent combines multiple data sources including vulnerability scan data, authentication service scan data, and network traffic data into a unified knowledge graph. This merging allows the system to capture both individual vulnerability severity and the contextual connectivity between systems, resolving the contradiction by integrating localized severity measurements with global contextual awareness.
Solution Approach 2:
The patent adds a new dimension of analysis by modeling network systems as a knowledge graph where nodes represent systems and edges represent potential attack paths. This dimensional transformation from isolated vulnerability ratings to connected attack path analysis enables simultaneous consideration of both severity and contextual connectivity.
2Ease of manufacture
If attack paths are modeled using only vulnerability exploitation data, then the model captures technical vulnerability connections, but it fails to account for authentication service misconfigurations and network reachability constraints, reducing accuracy
Solution Approach 1:
The patent merges three distinct data types: vulnerability exploitation data, authentication service misconfiguration data, and network traffic reachability data. Each data type is processed through dedicated ontology modules that then feed into a unified attack path model, capturing the full spectrum of attack vectors while maintaining the simplicity of modular construction.
Solution Approach 2:
The knowledge graph structure serves multiple functions simultaneously: it stores vulnerability data, authentication service data, network topology information, and computed attack paths. This multi-functionality allows the same infrastructure to support comprehensive attack path analysis without requiring separate systems for each data type.
3Measurement precision
If comprehensive network data collection and analysis is performed to accurately model attack paths, then attack path accuracy is improved, but system complexity and computational resources required increase significantly
Solution Approach 1:
The patent segments the complex analysis task into distinct modular components: a vulnerability scan ontology module, an authentication service scan ontology module, and a network traffic ontology module. Each module processes specific data types independently and feeds results to the attack path computation engine, reducing overall system complexity while maintaining comprehensive analysis capability.
Solution Approach 2:
The knowledge graph serves as an intermediary layer between raw data collection and attack path computation. It structures and stores data from multiple sources in a standardized format, enabling the attack path algorithm to operate on pre-processed, organized data rather than raw inputs, thereby simplifying the computational complexity.
4Ease of operation
If traditional scanning methods are used to infer network connections based on passive monitoring, then implementation is straightforward, but the model fails to capture the full scope of realizable network accesses and authentication service configurations
Solution Approach 1:
The patent combines passive scanning data with active authentication service scanning and network traffic flow analysis. This multi-source approach compensates for the limitations of any single method, capturing both the simplicity of passive monitoring and the comprehensiveness of active probing without requiring exhaustive manual configuration.
Data Source
AI summary
An attack path modeling and discovery system and process of the present combines comprehensive network data with machine learning to determine the greatest risk to a network environment by exposing the path of least resistance an attacker would likely take. This system and process considers both the likelihood of a threat agent to exploit a vulnerability, and the potential for loss when that threat occurs. The system utilizes two key models to accomplish this goal. First, Knowledge Graphs (KG) are leveraged to comprehensively model relationships across an environment, and second, Graph Neural Networks (GNNs) are used to predict the path of least resistance to the network's user-defined most valuable assets.