Skip to main content
SpringerLink
Log in

Utilizing hybrid computing models for network monitoring and security analysis through optical network modeling and data analytics

  • Published:
Optical and Quantum Electronics Aims and scope Submit manuscript

Abstract

Safe and reliable data transport over optical networks is essential for a high-speed Internet. Optical fibres are the backbone of the Internet, allowing billions of people around the world to connect and exchange data. However, these cables can be compromised by both deliberate attacks and accidental damage. The disruption caused by these abnormalities may result in significant monetary and data losses, undermine the security of optical networks by enabling unauthorised parties to view the sent data, or progressively lower network performance. Based on an examination of data from optical networks, this research proposes an original strategy for hybrid machine learning-based network monitoring and security improvement. Here, an anomaly detection method called convolutional principal component network is utilised to keep an eye on the optical infrastructure. Then, we apply hybrid cloud vector Bayesian graph networks to fortify the system. Anomaly detection rate, accuracy, precision rate, and quality of service are all experimentally evaluated across a variety of security analysis datasets. We validate our research using data from operational networks and simulated data created in a realistic-looking simulator. The findings verify the efficacy of our approach for optimising routing decisions in real time and designing networks for the future.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

All the data’s available in the manuscript.

References

  • Abdelli, K., Cho, J.Y., Azendorf, F., Griesser, H., Tropschug, C., Pachnicke, S.: Machine-learning-based anomaly detection in optical fiber monitoring. J. Opt. Commun. Netw. 14(5), 365–375 (2022)

    Article  Google Scholar 

  • Abdelli, K., Tropschug, C., Griesser, H., Pachnicke, S.: Fault monitoring in passive optical networks using machine learning techniques. In: 2023 23rd International Conference on Transparent Optical Networks (ICTON), pp. 1–5 (2023). IEEE.

  • Aibin, M., Chung, N., Gordon, T., Lyford, L., Vinchoff, C.: On short-and long-term traffic prediction in optical networks using machine learning. In: 2021 International Conference on Optical Network Design and Modeling (ONDM), pp. 1–6 (2021). IEEE.

  • Alwabisi, S., Ouni, R., Saleem, K.: Using machine learning and software-defined networking to detect and mitigate ddos attacks in fiber-optic networks. Electron 11(23), 4065 (2022). https://doi.org/10.3390/electronics11234065

    Article  Google Scholar 

  • Chen, X., Liu, C.Y., Proietti, R., Li, Z., Yoo, S.B.: Automating optical network fault management with machine learning. IEEE Commun. Mag. 60(12), 88–94 (2022)

    Article  Google Scholar 

  • Cho, J.Y., Pedreno-Manresa, J.J., Patri, S., Abdelli, K., Tropschug, C., Zou, J., Rydlichowski, P.: DeepALM: holistic optical network monitoring based on machine learning. In: 2022 Optical Fiber Communications Conference and Exhibition (OFC), pp. 1–3 (2022). IEEE.

  • Furdek, M., Natalino, C., Lipp, F., Hock, D., Di Giglio, A., Schiano, M.: Machine learning for optical network security monitoring: a practical perspective. J. Lightwave Technol. 38(11), 2860–2871 (2020). https://doi.org/10.1109/JLT.2020.2987032.

    Google Scholar 

  • Furdek, M., Natalino, C., Lipp, F., Hock, D., Aerts, N., Schiano, M., Wosinska, L.: Demonstration of machine-learning-assisted security monitoring in optical networks. In: Proceedings ECOC (No. 1) (2019) ISBN: 978-1-83953-185-9, https://doi.org/10.1049/cp.2019.1189

  • Khadr, M.H., Elgala, H., Rahaim, M., Khreishah, A., Ayyash, M., Little, T.: Machine learning-based security-aware spatial modulation for heterogeneous radio-optical networks. Proc. R. Soc. A 477(2248), 20200889 (2021)

    Article  MathSciNet  ADS  Google Scholar 

  • Panayiotou, T., Michalopoulou, M., Ellinas, G.: Survey on machine learning for traffic-driven service provisioning in optical networks. IEEE Commun. Surv. Tutor. 25(2), 1412–1443 (2023). https://doi.org/10.1109/COMST.2023.3247842.

  • Pandey, N.K., Mishra, A.K., Tripathi, N., Bagla, P., Sharma, R.: Implementation and monitoring of network traffic security using machine learning. In: 2023 2nd international conference on smart technologies and systems for next generation computing (ICSTSN), pp. 1–5 (2023). IEEE

  • Rengachary Gopalan, S., Chandran, H., Vijayan, N., Yadav, V., Mishra, S.: A machine learning assisted optical multistage interconnection network: performance analysis and hardware demonstration. ETRI J. 45(1), 60–74 (2023)

    Article  Google Scholar 

  • Silva, M.F., Sgambelluri, A., Pacini, A., Paolucci, F., Green, A., Mascarenas, D., Valcarenghi, L.: Confidentiality-preserving machine learning algorithms for soft-failure detection in optical communication networks. J. Opt. Commun. Netw. 15(8), C212–C222 (2023)

    Article  Google Scholar 

  • Song, H., Li, Y., Liu, M., Wang, K., Li, J., Zhang, M., ... & Zhang, J.: Experimental study of machine-learning-based detection and location of eavesdropping in end-to-end optical fiber communications. Optic. Fiber Technol. 68, 102669 (2022). https://doi.org/10.1016/j.yofte.2021.102669.

    Article  Google Scholar 

  • Szostak, D., Włodarczyk, A., Walkowiak, K.: Machine learning classification and regression approaches for optical network traffic prediction. Electron. 10(13), 1578 (2021)

    Article  Google Scholar 

  • Szostak, D.: Machine Learning Ensemble Methods for Optical Network Traffic Prediction. In: Gude Prego, J.J., de la Puerta, J.G., García Bringas, P., Quintián, H., Corchado, E. (eds) 14th International Conference on Computational Intelligence in Security for Information Systems and 12th International Conference on European Transnational Educational (CISIS 2021 and ICEUTE 2021). CISIS - ICEUTE 2021. Adv. Intell. Syst. Comput. 1400, (2022). https://doi.org/10.1007/978-3-030-87872-6_11

  • Wang, D., Zhang, C., Chen, W.: A review of machine learning-based failure management in optical networks. Sci. China Inf. Sci. 65, 211302 (2022). https://doi.org/10.1007/s11432-022-3557-9

    Article  Google Scholar 

  • Yan, B., Zhao, Y., Wang, W., Yan, L., Wang, Y., Liu, J., Zhang, J. First demonstration of machine-learning-based self-optimizing optical networks (SOON) running on commercial equipment. In: 2018 European Conference on Optical Communication (ECOC), pp. 1–3 (2018). IEEE.

  • Yang, H., Wan, Y., Yao, Q., Bao, B., Li, C., Sun, Z., Cheriet, M.: Anomaly prediction with hybrid supervised/unsupervised deep learning for elastic optical networks: a multi-index correlative approach. J. Lightwave Technol. 40(14), 4502–4513 (2022)

    Article  ADS  Google Scholar 

  • Zhang, L., Li, X., Tang, Y., Xin, J., & Huang, S.: A survey on QoT prediction using machine learning in optical networks. Opt. Fiber Technol. 68, 102804 (2022). https://doi.org/10.1016/j.yofte.2021.102804.

    Article  Google Scholar 

  • Zhang, C., Wang, D., Jia, J., et al.: Potential failure cause identification for optical networks using deep learning with an attention mechanism. J. Opt. Commun. Netw. 14(2), A122–A133 (2022b)

    Article  Google Scholar 

Download references

Funding

No funding.

Author information

Authors and Affiliations

  1. Department of Physics, School of Sciences, JAIN (Deemed-to-Be University), Bangalore, Karnataka, 562112, India

    Sudhakar Reddy

  2. College of Computing Science and Information Technology, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India

    Ajay Rastogi

  3. Department of Electrical Engineering, Vivekananda Global University, Jaipur, India

    Mukur Gupta

  4. Department of Computer Science and Engineering, Sanskriti University, Mathura, Uttar Pradesh, India

    Vikas Sharma

  5. UGDX School of Technology, ATLAS SkillTech University, Mumbai, India

    Dakshinamurthy V. Kolluru

  6. Department of Electronics and Communication Engineering, Presidency University, Bangalore, India

    Amrutha V. Nair

Authors
  1. Sudhakar Reddy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ajay Rastogi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mukur Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vikas Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dakshinamurthy V. Kolluru
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Amrutha V. Nair
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

SR Conceived and design the analysis. AR Writing—Original draft preparation. MG Collecting the Data, VS Contributed data and analysis stools. DVK Performed and analysis, Wrote the Paper. AVN Editing and Figure Design.

Corresponding author

Correspondence to Sudhakar Reddy.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethical approval

This article does not contain any studies with animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Reddy, S., Rastogi, A., Gupta, M. et al. Utilizing hybrid computing models for network monitoring and security analysis through optical network modeling and data analytics. Opt Quant Electron 56, 180 (2024). https://doi.org/10.1007/s11082-023-05718-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11082-023-05718-4

Keywords

Search

Navigation