Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Anomaly Detection Under Multiplicative Noise Model Uncertainty

Renganathan, Venkatraman LU (2022) In IEEE Control Systems Letters 6. p.1873-1878
Abstract
State estimators are crucial components of anomaly detectors that are used to monitor cyber-physical systems. Many frequently-used state estimators are suscepti- ble to model risk as they rely critically on the availability of an accurate state-space model. Modeling errors make it more difficult to distinguish whether deviations from expected behavior are due to anomalies or simply a lack of knowledge about the system dynamics. In this research, we account for model uncertainty through a multiplicative noise framework. Specifically, we propose to use the multiplicative noise LQG based compensator in this setting to hedge against the model uncertainty risk. The size of the residual from the estimator can then be compared against a threshold... (More)
State estimators are crucial components of anomaly detectors that are used to monitor cyber-physical systems. Many frequently-used state estimators are suscepti- ble to model risk as they rely critically on the availability of an accurate state-space model. Modeling errors make it more difficult to distinguish whether deviations from expected behavior are due to anomalies or simply a lack of knowledge about the system dynamics. In this research, we account for model uncertainty through a multiplicative noise framework. Specifically, we propose to use the multiplicative noise LQG based compensator in this setting to hedge against the model uncertainty risk. The size of the residual from the estimator can then be compared against a threshold to detect anomalies. Finally, the proposed detector is validated using numerical simulations. Extension of state-of-the-art anomaly detection in cyber-physical systems to handle model uncertainty represents the main novel contribution of the present work. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
IEEE Control Systems Letters
volume
6
pages
6 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:85122143839
ISSN
2475-1456
DOI
10.1109/LCSYS.2021.3134944
project
Artificial intelligence techniques for guidance, navigation, and control
language
English
LU publication?
yes
id
7987c2ca-0333-454e-bb63-9bfccd089c22
date added to LUP
2022-01-17 14:42:23
date last changed
2022-06-28 10:13:55
@article{7987c2ca-0333-454e-bb63-9bfccd089c22,
  abstract     = {{State estimators are crucial components of anomaly detectors that are used to monitor cyber-physical systems. Many frequently-used state estimators are suscepti- ble to model risk as they rely critically on the availability of an accurate state-space model. Modeling errors make it more difficult to distinguish whether deviations from expected behavior are due to anomalies or simply a lack of knowledge about the system dynamics. In this research, we account for model uncertainty through a multiplicative noise framework. Specifically, we propose to use the multiplicative noise LQG based compensator in this setting to hedge against the model uncertainty risk. The size of the residual from the estimator can then be compared against a threshold to detect anomalies. Finally, the proposed detector is validated using numerical simulations. Extension of state-of-the-art anomaly detection in cyber-physical systems to handle model uncertainty represents the main novel contribution of the present work.}},
  author       = {{Renganathan, Venkatraman}},
  issn         = {{2475-1456}},
  language     = {{eng}},
  pages        = {{1873--1878}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Control Systems Letters}},
  title        = {{Anomaly Detection Under Multiplicative Noise Model Uncertainty}},
  url          = {{http://dx.doi.org/10.1109/LCSYS.2021.3134944}},
  doi          = {{10.1109/LCSYS.2021.3134944}},
  volume       = {{6}},
  year         = {{2022}},
}