Performance Evaluation of Sequential Minimal Optimization and K* Algorithms for Predicting Burst Header Packet Flooding Attacks on Optical Burst Switching Networks

Abstract

Optical burst switching networks are vulnerable to various threats including Burst Header Packet Flooding attack, Circulating Burst Header attack, Address Spoofing, and Replay attack. Therefore, detecting such threats play a key role in taking appropriate security measures. One of the major challenges in identifying the risks of Burst Header Packet flooding attacks is the lack or insufficiency of reliable historical data. In this paper, firstly, Burst Header Packet flooding attacks are classified into four categories, Misbehaving-Block, Behaving-No Block, Misbehaving-No Block and Misbehaving-Wait, using Naive Bayes and K-Nearest Neighbor algorithms. Using performance metrics obtained both after testing on the same set and after applying 10-fold cross validation, the performance of Naive Bayes and K-Nearest Neighbor algorithms is compared based on commonly used performance metrics. As the results show, compared to Naive Bayes, K-Nearest Neighbor algorithm is more suitable for predicting Burst Header Packet Flooding attacks.

Keywords

• Burst header packet flooding
• Classification algorithms
• Optical burst switching
• Performance evaluation
• Security threats

References

1. [1] M. Sliti, M. Hamdi, and N. Boudriga, "A novel optical firewall architecture for burst switched networks," in 2010 12th International Conference on Transparent Optical Networks, 2010: IEEE, pp. 1-5. doi: 10.1109/ICTON.2010.5549054
2. [2] T. Battestilli and H. Perros, "An introduction to optical burst switching," IEEE communications magazine, vol. 41, no. 8, pp. S10-S15, 2003. doi: 10.1109/MCOM.2003.1222715
3. [3] M. Maier, Optical switching networks. Cambridge University Press, 2008. doi:10.1017/CBO9780511619731.011
4. [4] V. M. Vokkarane, J. P. Jue, and S. Sitaraman, "Burst segmentation: an approach for reducing packet loss in optical burst switched networks," in 2002 IEEE International Conference on Communications. Conference Proceedings. ICC 2002 (Cat. No. 02CH37333), 2002, vol. 5: IEEE, pp. 2673-2677. doi: 10.1109/ICC.2002.997328
5. [5] P. K. Chandra, A. K. Turuk, and B. Sahoo, "Survey on optical burst switching in WDM networks," in 2009 International Conference on Industrial and Information Systems (ICIIS), 2009: IEEE, pp. 83-88. doi: 10.1109/ICIINFS.2009.5429885
6. [6] S. Chakraborty, A. K. Turuk, and B. Sahoo, "OBS network blocking probability prediction using ensemble technique," in 2020 IEEE International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC), 2020: IEEE, pp. 1-6. doi: 10.1109/iSSSC50941.2020.9358862
7. [7] Y. Wang, T. Chen, and N. Zhou, "Space-based Optical Burst Switching Assembly Algorithm Based on QoS Adaption," in 2019 IEEE 11th International Conference on Communication Software and Networks (ICCSN), 2019: IEEE, pp. 101-105. doi: 10.1109/ICCSN.2019.8905264
8. [8] B. T. F. Fernandez and C. Sreenath, "Burstification threat in optical burst switched networks," in IEEE proceeding of International Conference on Communication and Signal Processing, 2014, pp. 1666-1670. doi: 10.1109/ICCSP.2014.6949804

9. [9] Y. Chen and P. K. Verma, "Secure optical burst switching: Framework and research directions," IEEE Communications magazine, vol. 46, no. 8, pp. 40-45, 2008. doi: 10.1109/MCOM.2008.4597102
10. [10] Y. Coulibaly, A. A. I. Al-Kilany, M. S. Abd Latiff, G. Rouskas, S. Mandala, and M. A. Razzaque, "Secure burst control packet scheme for Optical Burst Switching networks," in 2015 IEEE International Broadband and Photonics Conference (IBP), 2015: IEEE, pp. 86-91. doi: 10.1109/IBP.2015.7230771
11. [11] M. P. Fok, Z. Wang, Y. Deng, and P. R. Prucnal, "Optical layer security in fiber-optic networks," IEEE Transactions on Information Forensics and Security, vol. 6, no. 3, pp. 725-736, 2011. doi: 10.1109/TIFS.2011.2141990
12. [12] M. Z. Hasan, K. M. Hasan, and A. Sattar, "Burst Header Packet Flood Detection in Optical Burst Switching Network Using Deep Learning Model," Procedia Computer Science, vol. 143, pp. 970-977, 2018. doi: 10.1016/j.procs.2018.10.337
13. [13] M. Hola, L. Scholtz, L. Ladanyi, and J. Mullerova, "Modeling of optical burst switching networks," Proc. SPIE 10976, 21st Czech-Polish-Slovak Optical Conference on Wave and Quantum Aspects of Contemporary Optics, 2018, 1097610. doi: 10.1117/12.2518407