Machine Learning Based Physical Layer Resilience for Optical Networks Against Jamming Attack

We propose a machine learning-based recovery scheme for optical networks against physical layer jamming attacks. The results show that the proposed models can achieve high recovery rates without consuming additional optical path resources.

Authors

Jiahao Pang (ORCID: https://orcid.org/0000-0002-8857-1152)
Xiaoxue Gong (ORCID: https://orcid.org/0000-0002-7440-4003)
Weigang Hou (ORCID: https://orcid.org/0000-0002-9136-279X)
Xu Zhang (ORCID: https://orcid.org/0000-0001-9080-8027)
Lei Guo (ORCID: https://orcid.org/0000-0003-3547-3070)
J. Fei

Institutions

Chongqing University of Posts and Telecommunications (CN)

Publication Details

Published: 2024-11-02
DOI: https://doi.org/10.1109/acp/ipoc63121.2024.10809541
Primary Topic: Ocular and Laser Science Research
Type: article
Field-Weighted Citation Impact: 0.00

Funders

Chongqing Municipal Education Commission Foundation
National Natural Science Foundation of China

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article

Machine Learning Based Physical Layer Resilience for Optical Networks Against Jamming Attack

Jiahao Pang, Xiaoxue Gong, Weigang Hou, Xu Zhang et al.

Nov 2, 2024

Ocular and Laser Science Research

article

Machine Learning Based Physical Layer Resilience for Optical Networks Against Jamming Attack

Jiahao Pang, Xiaoxue Gong, Weigang Hou, Xu Zhang, Lei Guo, J. Fei

November 2, 2024article en

Abstract

We propose a machine learning-based recovery scheme for optical networks against physical layer jamming attacks. The results show that the proposed models can achieve high recovery rates without consuming additional optical path resources.

Chongqing University of Posts and Telecommunications (CN)

Chongqing Municipal Education Commission Foundation, National Natural Science Foundation of China

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Ocular and Laser Science Research

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