Privatized distributed anomaly detection for large-scale nonlinear uncertain systems

Privatized distributed anomaly detection for large-scale nonlinear uncertain systems

Rostampour, Vahab (Rijksuniversiteit Groningen)
Ferrari, Riccardo M.G. (TU Delft Team Jan-Willem van Wingerden)
Teixeira, Andre M.H. (Uppsala University)
Keviczky, T. (TU Delft Team Tamas Keviczky)

2020

In this article two limitations in current distributed model based approaches for anomaly detection in large-scale uncertain nonlinear systems are addressed. The first limitation regards the high conservativeness of deterministic detection thresholds, against which a novel family of set-based thresholds is proposed. Such set-based thresholds are defined in a way to guarantee robustness in a user-defined probabilistic sense, rather than a deterministic sense. They are obtained by solving a chance-constrained optimization problem, thanks to a randomization technique based on the Scenario Approach. The second limitation regards the requirement, in distributed anomaly detection architectures, for different parties to regularly communicate local measurements. In settings where these parties want to preserve their privacy, communication may be undesirable. In order to preserve privacy and still allow for distributed detection to be implemented, a novel privacy-preserving mechanism is proposed and a so-called privatized communication protocol is introduced. Theoretical guarantees on the achievable level of privacy, along with a characterization of the robustness properties of the proposed distributed threshold set design, taking into account the privatized communication scheme, are provided. Finally, simulation studies are included to illustrate our theoretical developments.

Anomaly detection
Integrated circuit interconnections
Measurement uncertainty
Monitoring
Privacy
Probabilistic logic
Uncertainty

http://resolver.tudelft.nl/uuid:fa4c14bb-9951-41df-8fae-0b2e44ef68f8

https://doi.org/10.1109/TAC.2020.3040251

2021-05-24

0018-9286

IEEE Transactions on Automatic Control, 66 (2021) (11), 5299-5313

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2020 Vahab Rostampour, Riccardo M.G. Ferrari, Andre M.H. Teixeira, T. Keviczky