Wireless Networks

, Volume 25, Issue 6, pp 3513–3529 | Cite as

Analysis of vulnerability propagation for the all-optical network based on Bio-PEPA

  • Zhong-Nan ZhaoEmail author
  • Jian Wang
  • Hong-Wei Guo


Aiming at the vulnerability propagation of all-optical networks, combining with the characteristics of the all-optical network, a formal modeling and analysis method for vulnerability propagation based on Bio-PEPA is proposed in this paper. First of all, the main characteristics that influence the vulnerability propagation are analyzed. Then, the optical fibers, optical amplifiers and optical switches are abstract into three different clusters, and the propagation behavior of vulnerability in intra-cluster and inter-cluster are described accurately. In addition, Ordinary Differential Equations is used for model parsing. Finally, through testing the main factors of vulnerability propagation, such as the number of nodes with potential vulnerability, system detection and repair rate, it is shown that the model constructed in this paper can reflect the vulnerability propagation trend of all-optical networks reasonably. At the same time, the proposed method can avoid the state space explosion problem of traditional modeling methods.


All-optical network Formal modeling Vulnerability propagation Bio-PEPA 



This work is supported by the National Natural Science Foundation of China (61403109), the Specialized Research Fund for the Doctoral Program of Higher Education of China (20112303120007) and the Scientific Research Fund of Heilongjiang Provincial Education Department (12541169).


  1. 1.
    Deng, N., & Jia, W. (2016). Technology evolution of all optical switching. In Asia Communications and Photonics Conference (pp. 2–5).Google Scholar
  2. 2.
    Ebrahimzadeh, A., Rahbar, A. G., & Alizadeh, B. (2017). Online bandwidth provisioning in all optical interconnection networks of data centers: Throughput maximizing approach. Computers & Electrical Engineering, 57, 15–27.CrossRefGoogle Scholar
  3. 3.
    Cheng, M. X., Crow, M., & Ye, Q. (2016). A game theory approach to vulnerability analysis: Integrating power flows with topological analysis. International Journal of Electrical Power with Energy Systems, 82, 29–36.CrossRefGoogle Scholar
  4. 4.
    Silva, C., Batista, R., Queiroz, R., et al. (2016) Towards a taxonomy for security threats on the web ecosystem. In Noms IEEE/IFIP Network Operations and Management Symposium, IEEE (pp. 584–590).Google Scholar
  5. 5.
    Orojloo, H., & Azgomi, M. A. (2017). A method for evaluating the consequence propagation of security attacks in cyber-physical systems. Future Generation Computer Systems, 67, 57–71.CrossRefGoogle Scholar
  6. 6.
    Morris-King, J., & Cam, H. (2015). Ecology-inspired cyber risk model for propagation of vulnerability exploitation in tactical edge. In Military Communications Conference, IEEE (Vol. 23(3), pp. 336–341).Google Scholar
  7. 7.
    Lazzez, A. (2015). All-optical networks: Security issues analysis. Journal of Optical Communications and Networking, 7(3), 136–145.CrossRefGoogle Scholar
  8. 8.
    Furdek, M., & Skorin-Kapov, N. (2011). Physical-layer attacks in all-optical WDM networks. In 2011 Proceedings of the 34th International Convention MIPRO, IEEE (pp. 446–451).Google Scholar
  9. 9.
    Furdek, M., & Skorin-Kapov, N. (2012). Physical-layer attacks in transparent optical networks. In N. Das (Ed.), Optical Communications Systems. Available from:
  10. 10.
    Ma, J., Zhang, L., Zhang, S., et al. (2013). Vulnerability analysis of the optical network NMS. In Second International Conference on Instrumentation, IEEE. Google Scholar
  11. 11.
    Patel, D. S., & Pancholi, P. N. (2013) Security issues and attack management in AON—A review. In International Conference on Emerging Technology Trends in Electronics, IEEE.Google Scholar
  12. 12.
    Habib, M. F., Tornatore, M., Dikbiyik, F., et al. (2013). Disaster survivability in optical communication networks. Computer Communications, 36(6), 630–644.CrossRefGoogle Scholar
  13. 13.
    Wang, J., Zhang, Y., et al. (2012). Research on assessing the vulnerability of optical network from the network geography distribution. In Fourth International Symposium on Information Science and Engineering, Shanghai (pp. 7–11).Google Scholar
  14. 14.
    Ruiz, M., & Velasco, L. (2014). Vulnerability modelling for periodical flexgrid network planning. In International Conference on Transparent Optical Networks, IEEE.Google Scholar
  15. 15.
    Meixner, C. C., Dikbiyik, F., Tornatore, M., et al. (2013). Disaster-resilient virtual-network mapping and adaptation in optical networks. In 2013 17th International Conference on Optical Networking Design and Modeling, IEEE.Google Scholar
  16. 16.
    Omer, M., Nilchiani, R., & Mostashari, A. (2009). Measuring the resilience of the trans-oceanic telecommunication cable system. IEEE Systems Journal, 3(3), 295–303.CrossRefGoogle Scholar
  17. 17.
    Agarwal, P. K., Efrat, A., Ganjugunte, S., et al. (2013). The resilience of WDM networks to probabilistic geographical failures. IEEE/ACM Transactions on Networking, 21(5), 1525–1538.CrossRefGoogle Scholar
  18. 18.
    Jirattigalachote, A., Skorinkapov, N., Furdek, M., et al. (2011). Sparse power equalization placement for limiting jamming attack propagation in transparent optical networks. Optical Switching and Networking, 8(4), 249–258.CrossRefGoogle Scholar
  19. 19.
    Ebrahimzadeh, A., Ghaffarpour Rahbar, A., & Alizadeh, B. (2016). Cost efficient amplifier placement and gain adjustment in all-optical networks: A noise-minimizing approach. Optik-International Journal for Light and Electron Optics, 127(23), 11191–11210.CrossRefGoogle Scholar
  20. 20.
    Manousakis, K., & Ellinas, G. (2016). Attack-aware planning of transparent optical networks. Optical Switching and Networking, 19(2), 97–109.CrossRefGoogle Scholar
  21. 21.
    Zhao, H., Mamoori, S. A., Jaekel, A. (2016). Attack-aware RWA using knowledge of demand holding times. In Electrical and Computer Engineering, IEEE.Google Scholar
  22. 22.
    Xiao, Y., Shao, A., Dou, Q., et al. (2013). Dedicated-path protection algorithm for preventing high-powered optical crosstalk in Transparent Optical Networks. In Wireless and Optical Communication Conference (pp. 523–526).Google Scholar
  23. 23.
    Skorin-Kapov, N., Furdek, M., Pardo, R. A., et al. (2012). Wavelength assignment for reducing in-band crosstalk attack propagation in optical networks: ILP formulations and heuristic algorithms. European Journal of Operational Research, 222(3), 418–429.CrossRefGoogle Scholar
  24. 24.
    Sun, Z., Peng, Y., & Long, K. (2011). Propagation effect of high-powered jamming attack in transparent optical networks. Proceedings of SPIE—The International Society for Optical Engineering, 8310(1), 1–6.Google Scholar
  25. 25.
    Ciocchetta, F., & Hillston, J. (2009). Bio-PEPA: A framework for the modelling and analysis of biological systems. Theoretical Computer Science, 410(33), 3065–3084.MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Ciocchetta, F., Duguid, A., Gilmore, S., et al. (2009). The Bio-PEPA tool suite. In The 2009 Sixth International Conference on the Quantitative Evaluation of Systems (pp. 309–310).Google Scholar
  27. 27.
    Agrawal, A., & Khan, R. A. (2012). Role of coupling in vulnerability propagation. Software Engineering, 2(1), 60–68.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyHarbin University of Science and TechnologyHarbinChina
  2. 2.School of Computer Science and TechnologyHarbin Engineering UniversityHarbinChina
  3. 3.Department of MathematicsHeilongjiang Institute of TechnologyHarbinChina

Personalised recommendations