An on-line design of physical watermarks
This paper considers the problem of designing physical watermark signals to protect a control system against replay attacks. We first introduce the replay attack model, where an adversary replays the previous sensory data in order to fool the controller to believe the system is still operating norma...
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sg-ntu-dr.10356-1429732020-07-17T00:54:11Z An on-line design of physical watermarks Liu, Hanxiao Yan, Jiaqi Mo, Yilin Johansson, Karl Henrik School of Electrical and Electronic Engineering 2018 IEEE Conference on Decision and Control (CDC 2018) Engineering::Electrical and electronic engineering Watermarking Optimization This paper considers the problem of designing physical watermark signals to protect a control system against replay attacks. We first introduce the replay attack model, where an adversary replays the previous sensory data in order to fool the controller to believe the system is still operating normally. The physical watermarking scheme, which leverages a random control input as a watermark to detect the replay attack is introduced. The optimal watermark signal design problem is then proposed as an optimization problem, which achieves the optimal trade-off between the control performance and attack detection performance. For the system with unknown parameters, we provide a procedure to asymptotically derive the optimal watermarking signal. Numerical examples are provided to illustrate the effectiveness of the proposed strategy. ASTAR (Agency for Sci., Tech. and Research, S’pore) Accepted version 2020-07-17T00:54:11Z 2020-07-17T00:54:11Z 2019 Conference Paper Liu, H., Yan, J., Mo, Y., & Johansson, K. H. (2018). An on-line design of physical watermarks. Proceedings of 2018 IEEE Conference on Decision and Control (CDC 2018), 440-445. doi:10.1109/CDC.2018.8619632 978-1-5386-1396-2 https://hdl.handle.net/10356/142973 10.1109/CDC.2018.8619632 2-s2.0-85062166469 440 445 en © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/CDC.2018.8619632. application/pdf |
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Engineering::Electrical and electronic engineering Watermarking Optimization Liu, Hanxiao Yan, Jiaqi Mo, Yilin Johansson, Karl Henrik An on-line design of physical watermarks |
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This paper considers the problem of designing physical watermark signals to protect a control system against replay attacks. We first introduce the replay attack model, where an adversary replays the previous sensory data in order to fool the controller to believe the system is still operating normally. The physical watermarking scheme, which leverages a random control input as a watermark to detect the replay attack is introduced. The optimal watermark signal design problem is then proposed as an optimization problem, which achieves the optimal trade-off between the control performance and attack detection performance. For the system with unknown parameters, we provide a procedure to asymptotically derive the optimal watermarking signal. Numerical examples are provided to illustrate the effectiveness of the proposed strategy. |
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School of Electrical and Electronic Engineering |
author_facet |
School of Electrical and Electronic Engineering Liu, Hanxiao Yan, Jiaqi Mo, Yilin Johansson, Karl Henrik |
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Conference or Workshop Item |
author |
Liu, Hanxiao Yan, Jiaqi Mo, Yilin Johansson, Karl Henrik |
author_sort |
Liu, Hanxiao |
title |
An on-line design of physical watermarks |
title_short |
An on-line design of physical watermarks |
title_full |
An on-line design of physical watermarks |
title_fullStr |
An on-line design of physical watermarks |
title_full_unstemmed |
An on-line design of physical watermarks |
title_sort |
on-line design of physical watermarks |
publishDate |
2020 |
url |
https://hdl.handle.net/10356/142973 |
_version_ |
1681058948901240832 |