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Integrated Cyber-Physical Resiliency for Power Grids under IoT-Enabled Dynamic Botnet Attacks

Yuhan Zhao, Juntao Chen, Quanyan Zhu

TL;DR

This work addresses the vulnerability of power grids to IoT-enabled botnet attacks by coupling a mean-field SIS epidemic model for cyber risk with cross-layer game-theoretic defenses and a dynamic physical controller. The authors present a tractable epidemic framework to quantify cyber risk $\bar{I}$, analyze cyber defense NE and its dependence on attacker and defender efforts ($\gamma(u_d),\zeta(u_a)$), and design a dynamic min-max control at the physical layer to counteract attacks. The approach is validated on the IEEE-39 bus system, showing that cyber-physical coordination yields explicit NE strategies and improved stability under strategic load-altering attacks. The results demonstrate that integrated cyber-physical resilience can significantly enhance grid operation in the presence of IoT botnets, with practical implications for real-time defense and policy planning.

Abstract

The wide adoption of Internet of Things (IoT)-enabled energy devices improves the quality of life, but simultaneously, it enlarges the attack surface of the power grid system. The adversary can gain illegitimate control of a large number of these devices and use them as a means to compromise the physical grid operation, a mechanism known as the IoT botnet attack. This paper aims to improve the resiliency of cyber-physical power grids to such attacks. Specifically, we use an epidemic model to understand the dynamic botnet formation, which facilitates the assessment of the cyber layer vulnerability of the grid. The attacker aims to exploit this vulnerability to enable a successful physical compromise, while the system operator's goal is to ensure a normal operation of the grid by mitigating cyber risks. We develop a cross-layer game-theoretic framework for strategic decision-making to enhance cyber-physical grid resiliency. The cyber-layer game guides the system operator on how to defend against the botnet attacker as the first layer of defense, while the dynamic game strategy at the physical layer further counteracts the adversarial behavior in real time for improved physical resilience. A number of case studies on the IEEE-39 bus system are used to corroborate the devised approach.

Integrated Cyber-Physical Resiliency for Power Grids under IoT-Enabled Dynamic Botnet Attacks

TL;DR

This work addresses the vulnerability of power grids to IoT-enabled botnet attacks by coupling a mean-field SIS epidemic model for cyber risk with cross-layer game-theoretic defenses and a dynamic physical controller. The authors present a tractable epidemic framework to quantify cyber risk , analyze cyber defense NE and its dependence on attacker and defender efforts (), and design a dynamic min-max control at the physical layer to counteract attacks. The approach is validated on the IEEE-39 bus system, showing that cyber-physical coordination yields explicit NE strategies and improved stability under strategic load-altering attacks. The results demonstrate that integrated cyber-physical resilience can significantly enhance grid operation in the presence of IoT botnets, with practical implications for real-time defense and policy planning.

Abstract

The wide adoption of Internet of Things (IoT)-enabled energy devices improves the quality of life, but simultaneously, it enlarges the attack surface of the power grid system. The adversary can gain illegitimate control of a large number of these devices and use them as a means to compromise the physical grid operation, a mechanism known as the IoT botnet attack. This paper aims to improve the resiliency of cyber-physical power grids to such attacks. Specifically, we use an epidemic model to understand the dynamic botnet formation, which facilitates the assessment of the cyber layer vulnerability of the grid. The attacker aims to exploit this vulnerability to enable a successful physical compromise, while the system operator's goal is to ensure a normal operation of the grid by mitigating cyber risks. We develop a cross-layer game-theoretic framework for strategic decision-making to enhance cyber-physical grid resiliency. The cyber-layer game guides the system operator on how to defend against the botnet attacker as the first layer of defense, while the dynamic game strategy at the physical layer further counteracts the adversarial behavior in real time for improved physical resilience. A number of case studies on the IEEE-39 bus system are used to corroborate the devised approach.
Paper Structure (18 sections, 9 theorems, 50 equations, 8 figures, 3 algorithms)

This paper contains 18 sections, 9 theorems, 50 equations, 8 figures, 3 algorithms.

Table of Contents

  1. Introduction
  2. Organization of the Paper
  3. Related Work
  4. IoT-Enabled Botnet Attack to Power Grids
  5. Power Grid Base Model
  6. Power Grid Model under Botnet Attacks
  7. Epidemic Modeling of Botnet Attacks
  8. Risk Analysis and Cyber-Resilient Design
  9. Cyber Risk Analysis
  10. Cyber Defense Game
  11. Characterization of Cyber Risk $\bar{I}$
  12. Characterization of Nash Equilibrium
  13. Dynamic Game for Physical Resilience
  14. Case Studies
  15. Cyber Risk and Cyber Defense Game Assessment
  16. ...and 3 more sections

Key Result

Proposition 1

For any $\gamma > 0$ and $\zeta > 0$, the equilibrium cyber risk $\bar{I}$ is unique in $(0,1]$ if $\frac{\gamma}{\zeta} < \frac{\langle k^2 \rangle}{\langle k \rangle}$ and is $0$ if $\frac{\gamma}{\zeta} \geq \frac{\langle k^2 \rangle}{\langle k \rangle}$, where $\langle k^2 \rangle = \sum_k k^2 p

Figures (8)

  • Figure 1: The massive IoT-controlled high-power energy devices introduce significant concerns on the cyber-physical power grid. Every load bus contains a considerable number of IoT devices. The adversary can manipulate these IoT devices in a coordinated fashion to launch a botnet attack that disrupts the grid operation.
  • Figure 2: (a) illustrates the percentage of the compromised IoT-controlled energy devices in the grid under the IoT botnet attacks with different attack intensities $\zeta$. (b) shows the resulting cyber risk $\bar{I}$ at the steady state as a function of $\zeta$ for fixed $\gamma$. The approximate function in \ref{['eqn:I_equ_final']} yields satisfactory results for $\bar{I}$.
  • Figure 3: (a) plots $\bar{I}(u_d,u_a)$ as a function of $u_a$ for fixed $u_d$. $\bar{I}$ behaves convex-concave for large $u_d$ and becomes concave as $u_d$ reduces. (b) shows that the attacker's utility $L_a(u_d, u_a)$ admits a unique maximizer $u_a^*$ for fixed $u_d$. $u_a^*$ increases with $u_d$, indicating that more attack effort is required when the defender escalates the cyber defense.
  • Figure 4: (a) depicts the defender and the attacker's optimal response functions. The intersection is the NE. (b) shows that Alg. \ref{['alg:0']} can quickly converge to a NE. The max difference is measured by $\max\{ | u_{d,{i+1}}-u_{d,(i)}|, |u_{a,(i+1)}-u_{a,(i)}| \}$.
  • Figure 5: Generator frequency evolution using a pre-designed PI controller. (a) shows that the PI controller can stabilize the system when there is no attack. (b) implements a load-switching attack and generator frequencies are out of the permissible range, showing a single PI controller is insufficient to stabilize the system under the botnet attack, and it calls for additional resilience enhancement schemes.
  • ...and 3 more figures

Theorems & Definitions (22)

  • Proposition 1
  • proof
  • Corollary 1.1
  • Proposition 2
  • proof
  • Proposition 3
  • proof
  • Corollary 3.1
  • Remark
  • Definition 1: Nash Equilibrium of Cyber Defense Game
  • ...and 12 more