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Long Short Term Memory and Gated Recurrent Unit Predictive Models for Industrial Control Systems

Shraddha Suratkar, Mrunmayee Dhapre, Apurva Vaje


As Operational Technologies (OT) and Internet of Things (IoT) grew popular, Industrial Control Systems (ICS) which are frequently managed through a Supervisory Control and Data Acquisition (SCADA) systems gained importance but simultaneously anomalies in ICS became a security concern. This paper presents a data-driven approach of predictive modeling for Energy Management System logs that exploits the relationship between data elements in the logs and the predictable aspect of communication patterns between devices in ICS networks using the time series structure of their logs. Specifically, two Recurrent Neural Networks - Stacked Long Short Term Memory (LSTM) and Stacked Gated Recurrent Unit (GRU) models - have been employed to model the behavior of these logs and comparison between these models is demonstrated. Various measures like accuracy, loss, memory usage and testing time are implemented to compare the performance of the models.


Industrial Control System; Energy Management System; Data-driven Modeling; Deep Learning; Recurrent Neural Network; Long Short Term Memory; Gated Recurrent Unit.

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