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2020 | OriginalPaper | Chapter

Network-Wide Localization of Optical-Layer Attacks

Authors : Marija Furdek, Vincent W. S. Chan, Carlos Natalino, Lena Wosinska

Published in: Optical Network Design and Modeling

Publisher: Springer International Publishing

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Abstract

Optical networks are vulnerable to a range of attacks targeting service disruption at the physical layer, such as the insertion of harmful signals that can propagate through the network and affect co-propagating channels. Detection of such attacks and localization of their source, a prerequisite for secure network operation, is a challenging task due to the limitations in optical performance monitoring, as well as the scalability and cost issues. In this paper, we propose an approach for localizing the source of a jamming attack by modeling the worst-case scope of each connection as a potential carrier of a harmful signal. We define binary words called attack syndromes to model the health of each connection at the receiver which, when unique, unambiguously identify the harmful connection. To ensure attack syndrome uniqueness, we propose an optimization approach to design attack monitoring trails such that their number and length is minimal. This allows us to use the optical network as a sensor for physical-layer attacks. Numerical simulation results indicate that our approach obtains network-wide attack source localization at only 5.8% average resource overhead for the attack monitoring trails.

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Literature
7.
go back to reference Pederzolli, F., et al.: Towards secure optical networks: a framework to aid localization of harmful connections. In: Proceedings of OFC, p. Th2A.42, March 2018. IEEEXplore Pederzolli, F., et al.: Towards secure optical networks: a framework to aid localization of harmful connections. In: Proceedings of OFC, p. Th2A.42, March 2018. IEEEXplore
Metadata
Title
Network-Wide Localization of Optical-Layer Attacks
Authors
Marija Furdek
Vincent W. S. Chan
Carlos Natalino
Lena Wosinska
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-38085-4_27

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