Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

On Graph Theory vs. Time-Domain Discrete Event Simulation for Topology-Informed Assessment of Power Grid Cyber Risk

Khandaker Akramul Haque, Leen Al Homoud, Xin Zhuang, Mariam Elnour, Ana Goulart, Katherine Davis

TL;DR

Results for each case study show that combining graph theory and simulation provides a topology-informed security assessment, and allow us to identify critical network nodes and evaluate their performance and reliability under a cyber threat such as denial of service threats.

Abstract

The shift toward more renewable energy sources and distributed generation in smart grids has underscored the significance of modeling and analyzing modern power systems as cyber-physical systems (CPS). This transformation has highlighted the importance of cyber and cyber-physical properties of modern power systems for their reliable operation. Graph theory emerges as a pivotal tool for understanding the complex interactions within these systems, providing a framework for representation and analysis. The challenge is vetting these graph theoretic methods and other estimates of system behavior from mathematical models against reality. High-fidelity emulation and/or simulation can help answer this question, but the comparisons have been understudied. This paper employs graph-theoretic metrics to assess node risk and criticality in three distinct case studies, using a Python-based discrete-event simulation called SimPy. Results for each case study show that combining graph theory and simulation provides a topology-informed security assessment. These tools allow us to identify critical network nodes and evaluate their performance and reliability under a cyber threat such as denial of service threats.

On Graph Theory vs. Time-Domain Discrete Event Simulation for Topology-Informed Assessment of Power Grid Cyber Risk

TL;DR

Results for each case study show that combining graph theory and simulation provides a topology-informed security assessment, and allow us to identify critical network nodes and evaluate their performance and reliability under a cyber threat such as denial of service threats.

Abstract

The shift toward more renewable energy sources and distributed generation in smart grids has underscored the significance of modeling and analyzing modern power systems as cyber-physical systems (CPS). This transformation has highlighted the importance of cyber and cyber-physical properties of modern power systems for their reliable operation. Graph theory emerges as a pivotal tool for understanding the complex interactions within these systems, providing a framework for representation and analysis. The challenge is vetting these graph theoretic methods and other estimates of system behavior from mathematical models against reality. High-fidelity emulation and/or simulation can help answer this question, but the comparisons have been understudied. This paper employs graph-theoretic metrics to assess node risk and criticality in three distinct case studies, using a Python-based discrete-event simulation called SimPy. Results for each case study show that combining graph theory and simulation provides a topology-informed security assessment. These tools allow us to identify critical network nodes and evaluate their performance and reliability under a cyber threat such as denial of service threats.
Paper Structure (19 sections, 5 equations, 5 figures, 3 tables)

This paper contains 19 sections, 5 equations, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Motivation, Contribution, and Literature Review
  3. Methodology
  4. Graph Metrics
  5. Case Studies
  6. Case 1: IEEE 123 Bus Case
  7. Case 2: Transmission and Distribution Radial Network
  8. Case 3: Ring Substation Topology
  9. Simulation Setup Overview
  10. Results and Discussions
  11. Critical Routers Based on Graph Theory Metrics
  12. Case 1
  13. Case 2
  14. Case 3
  15. Simulation of Use Case Results
  16. ...and 4 more sections

Figures (5)

  • Figure 1: Methodology of power system cyber network topologies using simulation and graph theory analysis.
  • Figure 2: Case 1: The cyber network defined in sahu2023reinforcement based on the seven zones of the IEEE 123-bus system defined in zhang2018dynamic. Each source of traffic (packet generators in red) is a zone.
  • Figure 3: Case 2: A combined transmission and distribution radial communication network le2020peer.
  • Figure 4: Case 3: A ring substation topology, similar to topology in nivethan2014modeling.
  • Figure 5: Delays in the critical routers for each case with SimPy simulation. The 'Stable' label denotes the base case (no attack) results for each case, and the horizontal axes showcase the different delays for each of the DoS/DDoS threats ran on each case (i.e., DoS at 5 in Figure \ref{['fig:first']} means a DoS threat on Router 5 in Case 1).