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Cited by 63Optical Network TechnologiesNetwork Security and Intrusion DetectionAdvanced Optical Network Technologies

Experimental Study of Machine-Learning-Based Detection and Identification of Physical-Layer Attacks in Optical Networks

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Natalino, Carlos, Schiano, Marco, Di Giglio, Andrea, Wosinska, Lena, Furdek, Marija·2019·Journal of Lightwave Technology
DOI 10.1109/jlt.2019.2923558
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Abstract

Optical networks are critical infrastructure supporting vital services and are vulnerable to different types of malicious attacks targeting service disruption at the optical layer. Due to the various attack techniques causing diverse physical-layer effects, as well as the limitations and sparse placement of optical performance monitoring devices, such attacks are difficult to detect, and their signatures are unknown. This paper presents an experimental investigation of a machine learning (ML) framework for detection and identification of physical-layer attacks, based on experimental attack traces from an operator field deployed testbed with coherent receivers. We perform in-band and out-of-band jamming signal insertion attacks, as well as polarization scrambling attacks, each with varying intensities. We then evaluate eight different ML classifiers in terms of their accuracy, and scalability in processing experimental data. The optical parameters critical for accurate attack identification are identified and the generalization of the models is validated. Results indicate that artificial neural networks achieve 99.9% accuracy in attack type and intensity classification, and are capable of processing 1 million samples in less than 10 seconds.

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  • Optical fiber networks are susceptible to a range of impairments, including fiber breaks, targeted physical tampering, unauthorized eavesdropping via fiber bending, and ambient mechanical vibrations.

    “Their indispensable function and inherent sensitivity, however, render them susceptible to a range of impairments, including fiber breaks, targeted physical tampering, unauthorized eavesdropping via fiber bending, and ambient mechanical vibrations [12,13].”

    From Resilient Anomaly Detection in Fiber-Optic Networks: A Machine Learning Framework for Multi-Threat Identification Using State-of-Polarization Monitoring (2025) · cites this paper as [12]

    The Introduction (p. 3) discusses physical-layer attacks and bent-fiber couplers, and the experimental setup (p. 6) describes a bent-fiber coupler that could be used in a field attack. These passages support the general vulnerability to physical tampering, but our copy does not establish the full list: fiber breaks, eavesdropping by bending, and ambient mechanical vibrations. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

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Related

Authors
Natalino, C., Schiano, M., Di Giglio, A., Wosinska, L., & Furdek, M.
Affiliations
Chalmers University of Technology (SE)
ORCID
Venue
Journal of Lightwave Technology
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2019-08-15
Volume
37
Issue
16
Pages
4173-4182
ISSN
  • 0733-8724 (ISSN-L)
  • 1558-2213
DOI
10.1109/jlt.2019.2923558
OpenAlex
W2950799921
Semantic Scholar
c4a2e3b1a354153fab5a01904923278205e7f1e3
Cited by
63
References
38
Citations by year
2026: 7 · 2025: 10 · 2024: 6 · 2023: 15 · 2022: 7 · 2021: 7 · 2020: 11
FWCI
2.25
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Lightwave Technology (publishedVersion)
  • Chalmers Research (Chalmers University of Technology) (submittedVersion)
Funders
  • COST Action 15127 RECODIS
  • Celtic-Plus project SENDATE-EXTEND
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Optical Network Technologies
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Optical Network Technologies
Keywords
physical layer attacks, optical networks, attack detection, machine learning, artificial neural networks, coherent optical receivers, polarization scrambling, optical performance monitoring, network security

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