A comparative study of time series anomaly detection models for industrial control systems

HIGHLIGHTS

• who: Bedeuro Kim and collaborators from the Department of Electrical and Computer Engineering, Sungkyunkwan University, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, Republic of Korea have published the paper: A Comparative Study of Time Series Anomaly Detection Models for Industrial Control Systems, in the Journal: Sensors 2023, 23, 1310. of /2023/
• what: The authors develop a framework to evaluate the performance of anomaly detection models with two public ICS datasets and standard evaluation criteria. The authors provide a comparative evaluation of five promising unsupervised anomaly detection models: InterFusion , RANSynCoder , GDN , LSTM-ED , and USAD . . .
  

   

  

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