Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Journal of Lightwave Technology
  • Vol. 37,
  • Issue 16,
  • pp. 4173-4182
  • (2019)

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

Not Accessible

Your library or personal account may give you access

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.

PDF Article
More Like This
Optical network security management: requirements, architecture, and efficient machine learning models for detection of evolving threats [Invited]

Marija Furdek, Carlos Natalino, Andrea Di Giglio, and Marco Schiano
J. Opt. Commun. Netw. 13(2) A144-A155 (2021)

Machine-learning-based optical spectrum feature analysis for DoS attack detection in IP over optical networks

Xiaoxue Gong, Yang Lei, Qihan Zhang, Lu Gan, Xu Zhang, and Lei Guo
Opt. Express 32(3) 3793-3803 (2024)

Machine-learning attacks on interference-based optical encryption: experimental demonstration

Lina Zhou, Yin Xiao, and Wen Chen
Opt. Express 27(18) 26143-26154 (2019)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved