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DC Field | Value | Language |
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dc.contributor.author | Gong, X | - |
dc.contributor.author | Lei, Y | - |
dc.contributor.author | Zhang, Q | - |
dc.contributor.author | Gan, L | - |
dc.contributor.author | Zhang, X | - |
dc.contributor.author | Guo, L | - |
dc.date.accessioned | 2024-01-30T18:12:12Z | - |
dc.date.available | 2024-01-30T18:12:12Z | - |
dc.date.issued | 2024-01-19 | - |
dc.identifier | ORCID iD: Xiaoxue Gong https://orcid.org/0000-0002-7440-4003 | - |
dc.identifier | ORCID iD: Qihan Zhang https://orcid.org/0000-0001-5128-0995 | - |
dc.identifier | ORCID iD: Lu Gan https://orcid.org/0000-0003-1056-7660 | - |
dc.identifier | ORCID iD: Xu Zhang https://orcid.org/0000-0001-9080-8027 | - |
dc.identifier.citation | Gong, X. et al. (2024) 'Machine-learning-based optical spectrum feature analysis for DoS attack detection in IP over optical networks', Optics Express, 32 (3), pp. 3793 - 3803. doi: 10.1364/OE.513504. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/28129 | - |
dc.description | Data availability. Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request. | - |
dc.description.abstract | In this paper, we introduce a novel approach for detecting Denial of Service (DoS) attacks in software-defined IP over optical networks, leveraging machine learning to analyze optical spectrum features. This method employs machine learning to automatically process optical spectrum data, which is indicative of network security status, thereby identifying potential DoS attacks. To validate its effectiveness, we conducted both numerical simulations and experimental trials to collect relevant optical spectrum datasets. We then assessed the performance of three machine learning algorithms XGBoost, LightGBM, and the BP neural network in detecting DoS attacks. Our findings show that all three algorithms demonstrate a detection accuracy exceeding 97%, with the BP neural network achieving the highest accuracy rates of 99.55% and 99.74% in simulations and experiments, respectively. This research not only offers a new avenue for DoS attack detection but also enhances early detection capabilities in the underlying optical network through optical spectrum data analysis. | en_US |
dc.description.sponsorship | National Key Research and Development Program of China (2023YFB2906200); National Natural Science Foundation of China (62075024, 62201105, 62205043, 62025105, 62221005, 62222103, 62331017); Chongqing Municipal Education Commission (CXQT21019, KJQN202100643). | en_US |
dc.format.extent | 3793 - 3803 | - |
dc.format.medium | Electronic | - |
dc.language.iso | en_US | en_US |
dc.publisher | Optica Publishing Group | en_US |
dc.rights | Copyright © 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement (https://doi.org/10.1364/OA_License_v2#VOR-OA). Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reserved. | - |
dc.rights.uri | https://opg.optica.org/library/license_v2.cfm#VOR-OA | - |
dc.title | Machine-learning-based optical spectrum feature analysis for DoS attack detection in IP over optical networks | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1364/OE.513504 | - |
dc.relation.isPartOf | Optics Express | - |
pubs.issue | 3 | - |
pubs.publication-status | Published online | - |
pubs.volume | 32 | - |
dc.identifier.eissn | 1094-4087 | - |
dc.rights.holder | Optica Publishing Group | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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FullText.pdf | Copyright © 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement (https://doi.org/10.1364/OA_License_v2#VOR-OA). Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reserved. | 5.13 MB | Adobe PDF | View/Open |
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