Cyber-Physical System Vulnerability Assessment via Graph Metrics
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Solution Overview
Problem
Conventional approaches to preventing sabotage of cyber-physical systems are inadequate as they focus on software hacking and ignore downstream effects, and lack coordination among different human entities and vendors, resulting in an incomplete picture of vulnerabilities.
Innovation Solution
A method that uses dynamical model data to generate vulnerability metrics for links between nodes in a graph representing cyber-physical systems, identifying and ranking vulnerable links and nodes based on these metrics to facilitate mitigation analysis and reduce vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional approaches focus on software hacking prevention, then software security is improved, but downstream effects and system-wide vulnerabilities are not detected
Solution Approach 1:
The system segments the cyber-physical system into discrete nodes and links, analyzing vulnerability at each component level while maintaining system-wide context. This allows detailed software security analysis at individual nodes while aggregating results to provide a complete system-wide vulnerability picture.
Solution Approach 2:
The patent combines multiple analysis perspectives (software hacking, downstream effects, system destabilization) into a unified vulnerability assessment framework. By merging these previously separate concerns into a single graph-based model, the system provides comprehensive security analysis without losing any dimension of vulnerability.
2Measurement precision
If vulnerability analysis is performed manually by human experts, then detailed assessment is possible, but the process is time-consuming and incomplete
Solution Approach 1:
The system performs automated vulnerability analysis using graph-based models and computational algorithms. The computer system independently calculates vulnerability metrics, identifies critical links, and generates assessments without requiring manual human analysis, thereby providing detailed results rapidly and efficiently.
Solution Approach 2:
The patent replaces manual human expert analysis with automated computational mechanisms. Graph theory algorithms and vulnerability metric calculations substitute for human reasoning processes, enabling rapid, consistent, and comprehensive vulnerability assessment across the entire system.
3Reliability
If comprehensive vulnerability assessment of all links is performed, then complete security picture is achieved, but computational complexity increases
Solution Approach 1:
The system calculates vulnerability metrics locally at each link in the graph rather than performing global analysis of the entire system at once. By computing vulnerability measures for individual links between nodes and then aggregating these local assessments, the system achieves comprehensive coverage while managing computational complexity through divide-and-conquer.
Solution Approach 2:
The patent transforms the vulnerability assessment problem by changing parameters from traditional security metrics to graph-theoretic measures (link vulnerability, node vulnerability, critical link identification). This parameter transformation enables efficient computational algorithms that scale better with system size while maintaining assessment completeness.
Data Source
AI summary
Techniques of preventing sabotage attacks in cyber-physical systems involve automatically identifying links between nodes of a graph representing cyber-physical systems as vulnerable to sabotage attacks according to a vulnerability metric for each link. The vulnerability metric used depends on the particular sabotage attack used to determine the vulnerable links. Once a computer configured to design cyber-physical systems based on vulnerability to sabotage attacks receives data representing the graph, the computer enumerates the possible links between nodes of the graph along which a sabotage attack may be performed. For each of those links, the computer computes the vulnerability metric. The computer then ranks the links according to respective values of the vulnerability metric. Based on the ranking, a designer may then perform a mitigation analysis that results in changes to the graph to reduce the vulnerability at each node accessible to a malicious actor.