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MISGUIDE: Security-Aware Attack Analytics for Smart Grid Load Frequency Control

Nur Imtiazul Haque, Prabin Mali, Mohammad Zakaria Haider, Mohammad Ashiqur Rahman, Sumit Paudyal

TL;DR

MISGUIDE is introduced, a novel defense-aware attack analytics designed to extract verifiable multi-time slot-based FDI attack vectors from complex SG load frequency control dynamics and ADMs, utilizing the Gurobi optimizer.

Abstract

Incorporating advanced information and communication technologies into smart grids (SGs) offers substantial operational benefits while increasing vulnerability to cyber threats like false data injection (FDI) attacks. Current SG attack analysis tools predominantly employ formal methods or adversarial machine learning (ML) techniques with rule-based bad data detectors to analyze the attack space. However, these attack analytics either generate simplistic attack vectors detectable by the ML-based anomaly detection models (ADMs) or fail to identify critical attack vectors from complex controller dynamics in a feasible time. This paper introduces MISGUIDE, a novel defense-aware attack analytics designed to extract verifiable multi-time slot-based FDI attack vectors from complex SG load frequency control dynamics and ADMs, utilizing the Gurobi optimizer. MISGUIDE can identify optimal (maliciously triggering under/over frequency relays in minimal time) and stealthy attack vectors. Using real-world load data, we validate the MISGUIDE-identified attack vectors through real-time hardware-in-the-loop (OPALRT) simulations of the IEEE 39-bus system.

MISGUIDE: Security-Aware Attack Analytics for Smart Grid Load Frequency Control

TL;DR

MISGUIDE is introduced, a novel defense-aware attack analytics designed to extract verifiable multi-time slot-based FDI attack vectors from complex SG load frequency control dynamics and ADMs, utilizing the Gurobi optimizer.

Abstract

Incorporating advanced information and communication technologies into smart grids (SGs) offers substantial operational benefits while increasing vulnerability to cyber threats like false data injection (FDI) attacks. Current SG attack analysis tools predominantly employ formal methods or adversarial machine learning (ML) techniques with rule-based bad data detectors to analyze the attack space. However, these attack analytics either generate simplistic attack vectors detectable by the ML-based anomaly detection models (ADMs) or fail to identify critical attack vectors from complex controller dynamics in a feasible time. This paper introduces MISGUIDE, a novel defense-aware attack analytics designed to extract verifiable multi-time slot-based FDI attack vectors from complex SG load frequency control dynamics and ADMs, utilizing the Gurobi optimizer. MISGUIDE can identify optimal (maliciously triggering under/over frequency relays in minimal time) and stealthy attack vectors. Using real-world load data, we validate the MISGUIDE-identified attack vectors through real-time hardware-in-the-loop (OPALRT) simulations of the IEEE 39-bus system.

Paper Structure

This paper contains 33 sections, 18 equations, 8 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Smart Grid Load-Frequency Control System
  3. Grid and Controller Dynamics
  4. Protection Relays
  5. Measurement Validation and Anomaly Detection
  6. Problem Definition and Attack Model
  7. Problem Definition
  8. Attack Model
  9. Attack Strategies
  10. Attack Assumptions
  11. Adversarial Attributes
  12. Attack Goal
  13. Detailed Technical Overview of MISGUIDE Framework
  14. Formal Modeling of Grid Dynamics Constraints
  15. Formal Modeling of FDI Attack Constraints
  16. ...and 18 more sections

Figures (8)

  • Figure 1: Demonstration of generators dispatching process and possible point of attacks in SG.
  • Figure 2: Demonstration of an IEE-3 bus SG system.
  • Figure 3: Demonstrating the benign (a) load measurements (p.u.) of the buses and (b) generated active power (p.u.), (c) reference setpoint (p.u.), and (d) frequency (Hz) of different SG generators.
  • Figure 4: Demonstrating the (a) attacked load measurements (p.u.) from buses and (b) generated active power (p.u.), (c) reference setpoint (p.u.), and (e) frequency (Hz) of different SG generators for UF relay attack in the presence of rules-based BDD.
  • Figure 5: Demonstrating the (a) attacked load measurements (p.u.) from buses and (b) generated active power (p.u.), (c) reference setpoint (p.u.), and (e) frequency (Hz) of different SG generators for UF relay attack in the presence of ML-based ADM.
  • ...and 3 more figures

Theorems & Definitions (2)

  • Definition 1: Indicator Constraint
  • Definition 2: k-Resilency