Cybersecurity Risk Analysis Using Cyber-Physical Graph Reconnaissance
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Solution Overview
Problem
Existing cybersecurity rating methods, such as CVSS, fail to adequately profile and rate the cybersecurity profiles of organizations due to insufficient information incorporation, making it difficult to monitor critical infrastructure entities, identify anomalous network events, and assess associated risks.
Innovation Solution
A system and method for cybersecurity risk analysis and anomaly detection using active and passive external reconnaissance, which identifies critical network entities within a cyber-physical graph, determines the risk of identified anomalies based on entity values, and determines an effectiveness score for the network's cybersecurity implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive data gathering about organization infrastructure and operations is performed, then cybersecurity profile accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the cybersecurity assessment into distinct modules: data gathering module that collects infrastructure and operations data, cyber-physical graph construction module that models relationships, reconnaissance module that identifies vulnerabilities, and scoring module that generates ratings. This segmentation allows comprehensive data collection while managing system complexity through modular architecture.
Solution Approach 2:
The patent introduces a cyber-physical graph as an intermediary data structure that mediates between raw organizational data and security assessment results. The graph transforms complex infrastructure and operations data into a structured representation of entities and relationships, enabling accurate cybersecurity profiling without overwhelming system complexity.
2Reliability
If active and passive reconnaissance searches are performed on the cyber-physical graph, then cybersecurity vulnerability detection is improved, but processing time increases
Solution Approach 1:
The system performs preliminary construction of the cyber-physical graph before conducting reconnaissance searches. By pre-processing organizational data into a structured graph format with defined entities, relationships, and attributes, the system prepares the data structure in advance, enabling faster and more efficient vulnerability detection during the reconnaissance phase without sacrificing detection comprehensiveness.
3Measurement precision
If criticality scores are assigned to multiple nodes in the cyber-physical graph, then risk assessment accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies local quality by assigning criticality scores to individual nodes based on their specific characteristics, relationships, and importance to organizational operations. Each node receives a customized criticality assessment considering its local context within the cyber-physical graph, enabling precise risk assessment at the node level while managing computational complexity through localized evaluation rather than global analysis.
Data Source
AI summary
A system and method for cybersecurity risk analysis and anomaly detection using active and passive external reconnaissance, that identifies critical network entities within a cyber-physical graph, identifies anomalous events within the network, determines the risk of identified anomalies based on the value of the entities involved, and determines an effectiveness score for the network based on the identified risks.