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On the Effectiveness of Recurrent Neural Networks for Live Modeling of Cyber-Physical Systems

Conference ·
 [1];  [1];  [1];  [1];  [1];  [1];  [1]
  1. ORNL
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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.
Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE
DOE Contract Number:
AC05-00OR22725
OSTI ID:
1649632
Country of Publication:
United States
Language:
English

References (9)

Towards Security-Aware Virtual Environments for Digital Twins conference May 2018
Anomaly Detection in Cyber Physical Systems Using Recurrent Neural Networks conference January 2017
National Cyber Range Overview conference October 2014
The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles
  • Glaessgen, Edward; Stargel, David
  • 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
    20th AIAA/ASME/AHS Adaptive Structures Conference
    14th AIAA     https://doi.org/10.2514/6.2012-1818
conference June 2012
Hardware-in-the-Loop Testing of Modern On-Board Power Systems Using Digital Twins conference June 2018
Product Lifecycle Management and the Quest for Sustainable Space Exploration conference August 2010
A Review of the Roles of Digital Twin in CPS-based Production Systems journal January 2017
Reengineering Aircraft Structural Life Prediction Using a Digital Twin journal January 2011
Real-Time Identification of Cyber-Physical Attacks on Water Distribution Systems via Machine Learning–Based Anomaly Detection Techniques journal January 2019

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