On the effectiveness of recurrent neural networks for live modeling of cyber-physical systems

Srikanth Yoginath, Varisara Tansakul, Supriya Chinthavali, Curtis Taylor, Joshua Hambrick, Philip Irminger, Kalyan Perumalla

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Attention to cyber security of cyber-physical systems (CPS) has led to the development of innovative cyber-resilient methodologies to ensure early detection and mitigation of cyber anomalies and threats. The concept of Digital Twin (DT) has recently emerged as one of the approaches to achieve the objective of resilience. In the approach using DT, a software-based live model of a target CPS is used to continuously monitor, surveil and verify the correctness of the target CPS operation. In this paper, we empirically study the effectiveness of Recurrent Neural Network (RNN)-based models as the basis of DT-based resilience. We uncover the important characteristics of an RNN-based solution with experimentation on a lab-scale Canal Lock CPS emulator with live validations and attack scenarios. For the first time, we demonstrate actual, real-time use of a RNN-based model as a DT for performing live analysis on an operational CPS. Based on the observed results, we highlight the importance of a DT model's training interval, prediction interval and CPS polling interval in the process of anomaly detection. We uncover the limitations in anomaly detection due to real-time synchronization needs of the RNN-based DT. We highlight this uncovered tug of war between synchronization and anomaly detection is inherent in any complex CPS that is monitored and synchronized by relying on the same sensor streams of ground truth for both synchronization as well as anomaly detection.

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Industrial Internet Cloud, ICII 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages309-317
Number of pages9
ISBN (Electronic)9781728129778
DOIs
StatePublished - Nov 2019
Event2nd IEEE International Conference on Industrial Internet Cloud, ICII 2019 - Orlando, United States
Duration: Nov 10 2019Nov 12 2019

Publication series

NameProceedings - IEEE International Conference on Industrial Internet Cloud, ICII 2019

Conference

Conference2nd IEEE International Conference on Industrial Internet Cloud, ICII 2019
Country/TerritoryUnited States
CityOrlando
Period11/10/1911/12/19

Keywords

  • CPS cyber security
  • CPS live modeling
  • RNN based Digital Twin

Fingerprint

Dive into the research topics of 'On the effectiveness of recurrent neural networks for live modeling of cyber-physical systems'. Together they form a unique fingerprint.

Cite this