Localization of Coordinated Cyber-Physical Attacks in Power Grids Using Moving Target Defense and Deep Learning

TL;DR

This paper introduces a novel method combining moving target defense, deep learning, and meta-learning to detect and locate coordinated cyber-physical attacks in power grids, enhancing security and response speed.

Contribution

It presents an integrated approach using MTD, CNN, and MAML to improve detection and localization of attacks in power grids with faster training and adaptation.

Findings

Effective localization of line outages during stealthy attacks

MTD invalidates attackers' knowledge and exposes physical attacks

Meta-learning accelerates CNN training after topology changes

Abstract

As one of the most sophisticated attacks against power grids, coordinated cyber-physical attacks (CCPAs) damage the power grid's physical infrastructure and use a simultaneous cyber attack to mask its effect. This work proposes a novel approach to detect such attacks and identify the location of the line outages (due to the physical attack). The proposed approach consists of three parts. Firstly, moving target defense (MTD) is applied to expose the physical attack by actively perturbing transmission line reactance via distributed flexible AC transmission system (D-FACTS) devices. MTD invalidates the attackers' knowledge required to mask their physical attack. Secondly, convolution neural networks (CNNs) are applied to localize line outage position from the compromised measurements. Finally, model agnostic meta-learning (MAML) is used to accelerate the training speed of CNN following the topology reconfigurations (due to MTD) and reduce the data/retraining time requirements. Simulations are carried out using IEEE test systems. The experimental results demonstrate that the proposed approach can effectively localize line outages in stealthy CCPAs.