TY - GEN
T1 - On the effectiveness of recurrent neural networks for live modeling of cyber-physical systems
AU - Yoginath, Srikanth
AU - Tansakul, Varisara
AU - Chinthavali, Supriya
AU - Taylor, Curtis
AU - Hambrick, Joshua
AU - Irminger, Philip
AU - Perumalla, Kalyan
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - 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.
AB - 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.
KW - CPS cyber security
KW - CPS live modeling
KW - RNN based Digital Twin
UR - http://www.scopus.com/inward/record.url?scp=85085943263&partnerID=8YFLogxK
U2 - 10.1109/ICII.2019.00062
DO - 10.1109/ICII.2019.00062
M3 - Conference contribution
AN - SCOPUS:85085943263
T3 - Proceedings - IEEE International Conference on Industrial Internet Cloud, ICII 2019
SP - 309
EP - 317
BT - Proceedings - IEEE International Conference on Industrial Internet Cloud, ICII 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Conference on Industrial Internet Cloud, ICII 2019
Y2 - 10 November 2019 through 12 November 2019
ER -