Digital Twins for Moving Target Defense Validation in AC Microgrids

TL;DR

This paper introduces a digital twin-based moving target defense strategy for AC microgrids, enhancing resilience against stealth cyber-attacks by dynamically altering control parameters and validating stability through a digital twin.

Contribution

It presents a novel hybrid MTD approach using digital twins and neural networks to improve microgrid security and stability against coordinated stealth attacks.

Findings

Reduces attack success probability by making system dynamics less predictable

Identifies and removes malicious injections through control weight modifications

Uses digital twin to validate stability and optimize defense strategies

Abstract

Cyber-physical microgrids are vulnerable to stealth attacks that can degrade their stability and operability by performing low-magnitude manipulations in a coordinated manner. This paper formulates the interactions between CSAs and microgrid defenders as a non-cooperative, zero-sum game. Additionally, it presents a hybrid Moving Target Defense (MTD) strategy for distributed microgrids that can dynamically alter local control gains to achieve resiliency against Coordinated Stealth Attacks (CSAs). The proposed strategy reduces the success probability of attack(s) by making system dynamics less predictable. The framework also identifies and removes malicious injections by modifying secondary control weights assigned to them. The manipulated signals are reconstructed using an Artificial Neural Network (ANN)-based Digital Twin (DT) to preserve stability. To guarantee additional immunity against instability arising from gain alterations, MTD decisions are also validated (via utility and best response computations) using the DT before actual implementation. The DT is also used to find the minimum perturbation that defenders must achieve to invalidate an attacker's knowledge effectively.