Moving Target Defense for Robust Monitoring of Electric Grid Transformers in Adversarial Environments

TL;DR

This paper proposes a dynamic, game-theoretic monitoring strategy inspired by Moving Target Defense to enhance the robustness of electric grid transformer monitoring against cyberattacks, ensuring reliable detection of component failures.

Contribution

It introduces a novel differential immunity framework and algorithms for MTD configuration in power grids, applying Stackelberg game models for optimal movement strategies.

Findings

Effective in identifying robust configurations for MTD.

Scalable algorithms demonstrated on IEEE test cases.

Enhanced resilience of transformer monitoring systems.

Abstract

Electric power grid components, such as high voltage transformers (HVTs), generating stations, substations, etc. are expensive to maintain and, in the event of failure, replace. Thus, regularly monitoring the behavior of such components is of utmost importance. Furthermore, the recent increase in the number of cyberattacks on such systems demands that such monitoring strategies should be robust. In this paper, we draw inspiration from work in Moving Target Defense (MTD) and consider a dynamic monitoring strategy that makes it difficult for an attacker to prevent unique identification of behavioral signals that indicate the status of HVTs. We first formulate the problem of finding a differentially immune configuration set for an MTD in the context of power grids and then propose algorithms to compute it. To find the optimal movement strategy, we model the MTD as a two-player game and consider the Stackelberg strategy. With the help of IEEE test cases, we show the efficacy and scalability of our proposed approaches.