Network-Wide Localization of Optical-Layer Attacks
Name
_ONDM_19__Attack_probe_design_IEEE-authors-version.pdf
Description
Accepted version
Size
562.96 KB
Format
Adobe PDF
Checksum (MD5)
8e3b07aa501f9516cd203a843b5fd945
Author(s)
Chan, Vincent W. S.
Date Issued
May 2019
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher
Springer International Publishing
Citation
Furdek, 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)
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/978-3-030-38085-4_27
