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

An RNN-based forecasting approach is used to early detect anomalies inindustrial multivariate time series data from a simulated Tennessee EastmanProcess (TEP) with many cyber-attacks. This work continues a previouslyproposed LSTM-based approach to the fault detection in simpler data. It isconsidered necessary to adapt the RNN network to deal with data containingstochastic, stationary, transitive and a rich variety of anomalous behaviours.There is particular focus on early detection with special NAB-metric. Acomparison with the DPCA approach is provided. The generated data set is madepublicly available.

RNN-based Early Cyber-Attack Detection for the Tennessee Eastman Process