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

This research proposes a machine learning-based attack detection model forpower systems, specifically targeting smart grids. By utilizing data and logscollected from Phasor Measuring Devices (PMUs), the model aims to learn systembehaviors and effectively identify potential security boundaries. The proposedapproach involves crucial stages including dataset pre-processing, featureselection, model creation, and evaluation. To validate our approach, we used adataset used, consist of 15 separate datasets obtained from different PMUs,relay snort alarms and logs. Three machine learning models: Random Forest,Logistic Regression, and K-Nearest Neighbour were built and evaluated usingvarious performance metrics. The findings indicate that the Random Forest modelachieves the highest performance with an accuracy of 90.56% in detecting powersystem disturbances and has the potential in assisting operators indecision-making processes.

Machine Learning to detect cyber-attacks and discriminating the types of power system disturbances