Weighted Attack Graphs for Cybersecurity Threat Prioritization
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Solution Overview
Problem
Cybersecurity attacks are increasing in complexity, overwhelming security resources, and there is a need for efficient prioritization of threats to allocate resources effectively.
Innovation Solution
A method and apparatus that treat cybersecurity attacks as nodes connected by edges with variable weights, allowing for system-independent prioritization and visualization in an interactive 3D environment using data visualization software, enabling customization of edge weights to quantify risk and allocate resources efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If security personnel monitor all cybersecurity attacks manually, then detection completeness is improved, but resource consumption and time requirements increase significantly
Solution Approach 1:
The patent introduces an automated attack graph generation system as an intermediary between raw security data and human analysis. The system automatically processes security events, generates attack graphs, calculates risk scores, and produces prioritized reports, serving as a mediator that handles the time-consuming analysis work while providing comprehensive detection to security personnel.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with automated computational systems. Instead of security personnel manually analyzing security logs and generating reports, the system uses automated algorithms to generate attack graphs, calculate edge weights, determine risk scores, and produce prioritized security assessments, significantly reducing time requirements while maintaining detection completeness.
2Reliability
If security resources are allocated to address all security requirements equally, then comprehensive coverage is improved, but resource efficiency deteriorates due to overwhelming number of attacks
Solution Approach 1:
The patent applies local quality by differentiating the treatment of different security requirements based on their specific risk characteristics. Instead of uniform resource allocation, the system calculates unique risk scores for each attack path based on local factors such as asset criticality, vulnerability severity, and attack likelihood, enabling targeted resource allocation to high-risk areas while maintaining adequate coverage elsewhere.
Solution Approach 2:
The patent changes the parameter of resource allocation from equal distribution to risk-proportional distribution. By introducing risk scores as a new parameter that combines multiple factors (asset value, vulnerability severity, attack likelihood), the system transforms resource allocation decisions from uniform to differentiated, improving resource efficiency while maintaining comprehensive coverage through prioritized addressing of high-risk requirements.
3Ease of operation
If attack graphs are visualized in traditional 2D environment, then simplicity is improved, but analytical depth and insight quality deteriorate
Solution Approach 1:
The patent transitions from traditional 2D attack graph visualization to a multi-dimensional visualization approach. The system incorporates additional dimensions such as risk score gradients, asset criticality levels, and attack probability metrics into the visual representation, enabling security personnel to perceive complex relationships and prioritize threats more effectively while maintaining visual accessibility.
Data Source
AI summary
A computer-implemented method and system for weighing and prioritizing cybersecurity attacks are disclosed. The method includes obtaining data regarding one or more cyber-attacks, generating nodes representing the attacks, calculating edge weights between adjacent nodes using a custom risk function, and outputting a weighted attack graph. Each node is defined by attributes including severity, likelihood, protection level, and layer in a threat model. Edges between nodes are assigned weights that quantify risk based on severity, likelihood of success, and system protection. The resulting weighted attack graph provides an interactive visual representation of potential attack paths, enabling cybersecurity professionals to allocate defensive resources more efficiently and respond to critical threats.