Exploring the potential of longitudinal power monitoring for detecting physical-layer attacks [Invited]

Abstract

The recurring cases of suspicious incidents involving optical fiber cables in recent years have exposed the vulnerabilities of modern communication networks. Whether driven by geopolitical tensions, sabotage, or urban vandalism, these disruptions can cause Internet blackouts, compromise user privacy, and, most critically, challenge operators’ reliability in delivering secure connectivity. Moreover, the emergence of such incidents raises key concerns about how effectively network operators can secure thousands of kilometers of deployed fiber without incurring additional costs from expensive monitoring solutions. In this context, the rise of receiver (Rx)-based digital signal processing (DSP) monitoring schemes can serve as a valuable ally. Originally designed for optical performance monitoring—providing insights such as the estimation of the longitudinal power monitoring (LPM) in optical fiber links—these approaches can also play a crucial role in detecting fiber-related attacks, as any attempt to leak or degrade information leaves distinctive optical power signatures that can be revealed by the Rx-DSP. Therefore, this work investigates the effectiveness of LPM in detecting physical-layer attacks. A detailed simulative analysis is conducted for fiber tapping, addressing aspects such as monitoring implementation, security vulnerabilities, and signature recognition. Other attacks, such as quality-of-service degradation and out-of-band jamming via gain competition, are explored qualitatively, offering insights and identifying opportunities for future research.

Full text

C30 Vol. 17, No. 7 / July 2025 / Journal of Optical Communications and Networking Research Article
Exploring the potential of longitudinal power
monitoring for detecting physical-layer attacks
[Invited]
Matheus Sena,1,* Abdelrahmane Moawad,2Robert Emmerich,2
Behnam Shariati,2Marc Geitz,1Ralf-Peter Braun,3Johannes Fischer,2
AND Ronald Freund2
1Deutsche Telekom AG, Winterfeldtstraße 21, Berlin 10781, Germany
2Fraunhofer Heinrich Hertz Institute, Einsteinufer 37, Berlin 10587, Germany
3Orbit Gesellschaft für Applikations- und Informationssysteme mbH, Mildred-Scheel-Str. 1, Bonn 53175, Germany
*matheus.ribeiro-sena@telekom.de
Received 3 January 2025; revised 17 February 2025; accepted 21 February 2025; published 20 March 2025
The recurring cases of suspicious incidents involving optical fiber cables in recent years have exposed the vul-
nerabilities of modern communication networks. Whether driven by geopolitical tensions, sabotage, or urban
vandalism, these disruptions can cause Internet blackouts, compromise user privacy, and, most critically, chal-
lenge operators’ reliability in delivering secure connectivity. Moreover, the emergence of such incidents raises key
concerns about how effectively network operators can secure thousands of kilometers of deployed fiber without
incurring additional costs from expensive monitoring solutions. In this context, the rise of receiver (Rx)-based
digital signal processing (DSP) monitoring schemes can serve as a valuable ally. Originally designed for opti-
cal performance monitoring—providing insights such as the estimation of the longitudinal power monitoring
(LPM) in optical fiber links—these approaches can also play a crucial role in detecting fiber-related attacks, as
any attempt to leak or degrade information leaves distinctive optical power signatures that can be revealed by
the Rx-DSP. Therefore, this work investigates the effectiveness of LPM in detecting physical-layer attacks. A
detailed simulative analysis is conducted for fiber tapping, addressing aspects such as monitoring implementa-
tion, security vulnerabilities, and signature recognition. Other attacks, such as quality-of-service degradation and
out-of-band jamming via gain competition, are explored qualitatively, offering insights and identifying oppor-
tunities for future research. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial
Intelligence (AI) training, and similar technologies, are reserved.
https://doi.org/10.1364/JOCN.554766
1. INTRODUCTION
In light of recent events, such as conflicts in Europe and the
Middle East, a growing sense of global geopolitical instability
has emerged [1]. These uncertainties make communication
networks vulnerable targets for “hybrid” warfare, and reports of
sabotage involving submarine optical fiber cables have become
increasingly frequent in the news [2,3]. Given this growing
number of suspicious activities on optical fiber infrastructure
around the world, network operators must adapt to a new
reality and enhance their ability to predict and respond swiftly
to potential threats.
Traditionally, operators rely on monitoring tools such as
optical time-domain reflectometers (OTDRs) to detect power
disturbances along fiber links, exposing potential intrusions
like tappings or breaks [4]. Alternative methods, using phase-
sensitive OTDRs, can identify subtle mechanical vibrations
near the fiber, offering early warnings of suspicious activities
that may signal an attack [5]. However, the high cost and com-
plexity of deploying these solutions at scale across nationwide
or even transcontinental networks pose significant challenges.
Other advanced techniques leverage machine learning (ML)
algorithms to analyze polarization rotations [6] or optical per-
formance monitoring (OPM) indicators, such as bit-error ratio
(BER), chromatic dispersion, and optical signal-to-noise ratio
(OSNR) [7], enabling the identification of attacker signatures
with enhanced precision. Yet, such ML-based models require
computationally intensive training processes and, in security
scenarios, generating sufficient and representative training data
may be complex, as physical-layer attacks are short-lived and
highly variable [8], making it difficult to create datasets that
accurately reflect the diverse nature of potential threats [9].
One promising and more scalable solution lies in harness-
ing the monitoring capabilities of receiver (Rx) digital signal
1943-0620/25/070C30-11 Journal © 2025 Optica Publishing Group