English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

As one of the most sophisticated attacks against power grids, coordinatedcyber-physical attacks (CCPAs) damage the power grid's physical infrastructureand use a simultaneous cyber attack to mask its effect. This work proposes anovel approach to detect such attacks and identify the location of the lineoutages (due to the physical attack). The proposed approach consists of threeparts. Firstly, moving target defense (MTD) is applied to expose the physicalattack by actively perturbing transmission line reactance via distributedflexible AC transmission system (D-FACTS) devices. MTD invalidates theattackers' knowledge required to mask their physical attack. Secondly,convolution neural networks (CNNs) are applied to localize line outage positionfrom the compromised measurements. Finally, model agnostic meta-learning (MAML)is used to accelerate the training speed of CNN following the topologyreconfigurations (due to MTD) and reduce the data/retraining time requirements.Simulations are carried out using IEEE test systems. The experimental resultsdemonstrate that the proposed approach can effectively localize line outages instealthy CCPAs.

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