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dc.contributor.authorChan, Vincent W. S.
dc.date.accessioned2021-03-03T12:44:02Z
dc.date.available2021-03-03T12:44:02Z
dc.date.issued2019-05
dc.identifier.isbn9783030380854
dc.identifier.issn0302-9743
dc.identifier.urihttps://hdl.handle.net/1721.1/130059
dc.description.abstractOptical 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.en_US
dc.language.isoen
dc.publisherSpringer International Publishingen_US
dc.relation.isversionof10.1007/978-3-030-38085-4_27en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceOther repositoryen_US
dc.titleNetwork-Wide Localization of Optical-Layer Attacksen_US
dc.typeArticleen_US
dc.identifier.citationFurdek, Marija et al. “Network-Wide Localization of Optical-Layer Attacks.” Paper in the Lecture Notes in Computer Science, 11616 LNCS, ONDM 2019, Athens, Greece, May 13-16, 2019, Springer International Publishing: 310-322 © 2019 The Author(s)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.relation.journalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2020-12-04T16:45:03Z
dspace.orderedauthorsFurdek, M; Chan, VWS; Natalino, C; Wosinska, Len_US
dspace.date.submission2020-12-04T16:45:07Z
mit.journal.volume11616 LNCSen_US
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusComplete


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