Survey of Moving Target Defense in Power Grids: Design Principles, Tradeoffs, and Future Directions

TL;DR

Power grids face stealthy cyber-physical attacks on state estimation, notably false data injection and coordinated cyber-physical attacks. This survey categorizes moving target defence (MTD) for grids into physics-based, network-based, deception-based, and ML-enhanced strategies, and lays out design principles, performance metrics, and trade-offs. It details physics-based MTD via reactance perturbations with D-FACTS, deployment strategies, and timing (periodic vs event-triggered), as well as extensions to distribution networks and microgrids, and discusses network- and ML-enabled variants. It also highlights open research directions, such as unified cyber-physical MTD design, resilience of ML-based detectors to adversarial attacks, and real-world demonstrations. The work aims to guide operators and researchers in adopting MTD for practical grid security.

Abstract

Moving target defense (MTD) in power grids is an emerging defense technique that has gained prominence in the recent past. It aims to solve the long-standing problem of securing the power grid against stealthy attacks. The key idea behind MTD is to introduce periodic/event-triggered controlled changes to the power grid's SCADA network/physical plant, thereby invalidating the knowledge attackers use for crafting stealthy attacks. In this paper, we provide a comprehensive overview of this topic and classify the different ways in which MTD is implemented in power grids. We further introduce the guiding principles behind the design of MTD, key performance metrics, and the associated trade-offs in MTD and identify the future development of MTD for power grid security.

Figures (3)

• Figure 1: Pictorial depiction of MTD strategies within a cyber-physical power system.
• Figure 2: Smallest principle angle for assessing separation of column space and MTD effectiveness.
• Figure 3: Example of SDN-based cyber defense.