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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Existing coordinated cyber-attack detection methods have low detectionaccuracy and efficiency and poor generalization ability due to difficultiesdealing with unbalanced attack data samples, high data dimensionality, andnoisy data sets. This paper proposes a model for cyber and physical data fusionusing a data link for detecting attacks on a Cyber-Physical Power System(CPPS). Two-step principal component analysis (PCA) is used for classifying thesystem's operating status. An adaptive synthetic sampling algorithm is used toreduce the imbalance in the categories' samples. The loss function is improvedaccording to the feature intensity difference of the attack event, and anintegrated classifier is established using a classification algorithm based onthe cost-sensitive gradient boosting decision tree (CS-GBDT). The simulationresults show that the proposed method provides higher accuracy, recall, andF-Score than comparable algorithms.

Coordinated Cyber-Attack Detection Model of Cyber-Physical Power System Based on the Operating State Data Link