Graph Analysis for Network Node Significance Assessment
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Solution Overview
Problem
Conventional approaches to identifying critical assets in organization networks rely on human inputs, which are subjective and inefficient, making it difficult to effectively manage vulnerabilities and protect against cyber threats.
Innovation Solution
Implementing a graph analysis-based assessment using network traffic data to calculate centrality values for nodes, identifying significant nodes with high disruption potential, and automatically updating rulesets for security management without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional self-assessment inventory approaches are used to identify important assets, then human inputs and stakeholder interviews can determine what stakeholders consider important, but the process becomes subjective, time-consuming, and inefficient
Solution Approach 1:
The patent replaces the mechanical system of human interviews and self-assessment inventories with an automated computational system that performs graph analysis on network traffic data. This substitution eliminates subjective human input and manual processes, providing objective, precise, and rapid identification of critical assets through algorithmic analysis of network flow patterns and node centrality metrics
Solution Approach 2:
The system enables the network infrastructure itself to identify its own critical assets by analyzing its own traffic data and topological structure. The graph analysis engine processes network traffic data automatically without requiring external human assessment, allowing the system to self-determine which nodes are most critical based on their actual network behavior and connectivity patterns
2Measurement precision
If graph analysis-based assessment is implemented to identify critical nodes, then objective and accurate identification of significant nodes is achieved, but the complexity of network analysis and calculation increases
Solution Approach 1:
The patent segments the complex network analysis task into distinct modular components: a graph construction module that builds the network topology from traffic data, a centrality calculation module that computes node importance metrics, and a critical node identification module that ranks and selects significant nodes. This segmentation reduces overall system complexity by breaking down the monolithic analysis process into manageable, independent functional units
Solution Approach 2:
The patent introduces a graph representation as an intermediary data structure between raw network traffic data and the final critical node identification results. The graph serves as a mediator that organizes complex network relationships into a structured format with nodes and edges, enabling efficient centrality calculations while simplifying the interface between data input and analysis output
Data Source
AI summary
Various embodiments include systems and methods to implement a graph analysis-based assessment to determine relative node significance. Network traffic data associated with a network may be obtained. A graph analysis-based assessment of the network may be performed to determine network traffic paths between a plurality of nodes in the network based at least in part on the network traffic data and to calculate, for each node and based at least in part on the network traffic paths, a respective centrality value. The respective centrality value may be indicative of a respective node being a potential source of disruption to the network relative to other nodes. At least one significant node in the network may be identified based at least in part on the centrality values, and a particular action to be performed with respect to the at least one significant node may be determined.