Detecting Traffic Diversion Using Metaheuristic Algorithm in SDN

Authors

  • Mona Afr Alshammari College of computer and information sciences , Jouf University, KSA
  • A. A. Abd El-Aziz College of computer and information sciences , Jouf University, KSA and Faculty of Graduate studies for Statistical Research, Cairo University, Egypt
  • Hedi Hamdi University of Manouba, Tunisia

Keywords:

SDN, Traffic Diversion, Metaheuristic Algorithm, GA, Anomaly Detection, Network Security

Abstract

With the increasing prevalence of Software-Defined Networking (SDN) and the growing demand for network resources, the threat of traffic diversion attacks in SDN environments poses a significant risk to network security and performance. Conventional methods for detecting these attacks often fall short of identifying sophisticated and dynamic diversion tactics. In response to this challenge, we present a novel approach to tackle traffic diversion attacks in SDN. Our proposed technique leverages metaheuristic algorithms, specifically a Genetic Algorithm (GA), to improve traffic diversion detection's precision and effectiveness. The primary objective is to provide network administrators with a robust and adaptive tool for identifying and mitigating diversion attacks. Through rigorous testing and evaluation, our proposed algorithm demonstrates exceptional performance. It achieved a high level of accuracy, exceeding 70 %, a precision of 94%, a recall of 92%, and a F1-score of 93%.  in identifying diversion attacks while maintaining a low false positive rate. The algorithm's adaptability ensures it can respond effectively to evolving diversion tactics, making it well-suited for dynamic SDN environments. The proposed algorithm is scalable as it can be adapted to the changing of network conditions, such as traffic levels. The proposed algorithm contributes to the enhancement of SDN security, safeguarding network integrity and reliability in the face of evolving threats.

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References

Shakil, M., Fuad Yousif Mohammed, A., Arul, R., Bashir, A. K., & Choi, J. K. (2022). A novel dynamic framework to detect DDoS in SDN using metaheuristic clustering. Transactions on Emerging Telecommunications Technologies, 33)3(e3622).

Foukas, X., Marina, M. K., & Kontovasilis, K. (2015). Software-defined networking concepts. Software Defined Mobile Networks (SDMN) Beyond LTE Network Architecture, 21-44.

Wu, Q., Zhang, X., Xu, X., & Yan, J. (2021). A traffic diversion detection system based on particle swarm optimization in software-defined networking. IEEE Access, 9, 58851-58861.

He, Z., Wang, Q., Liu, X., & Li, L. (2019). An intrusion detection system for SDN based on hybrid GA and SVM algorithm. Journal of Ambient Intelligence and Humanized Computing, 10(1), 273-284.

Chen, Q., Yu, X., Zhou, L., & Li, Z. (2017). A detection mechanism for traffic diversion attacks in SDN based on ant colony optimization. International Journal of Distributed Sensor Networks, 13(1), 1-11

Tally, Mushtaq Talb, and Haleh Amintoosi. "A hybrid method of genetic algorithm and support vector machine for intrusion detection." International Journal of Electrical & Computer Engineering (2088-8708) 11.1 (2021).

Peng, Huijun, et al. "A detection method for anomaly flow in software-defined network." IEEE Access 6 (2018): 27809-27817

Salas-Fernández, Agustín, et al. "Metaheuristic techniques in attack and defense strategies for cybersecurity: a systematic review." Artificial Intelligence for Cyber Security: Methods, Issues and Possible Horizons or Opportunities (2021): 449-467.‏

Kan, Xiu, et al. "A novel IoT network intrusion detection approach based on adaptive particle swarm optimization convolutional neural network." Information Sciences 568 (2021): 147-162.‏

Champagne, S., Makanju, T., Yao, C., Zincir-Heywood, N., & Heywood, M. (2018, July). A genetic algorithm for dynamic controller placement in software defined networking. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1632-1639).

Shin, S., Xu, L., Hong, S., & Gu, G. (2016, August). Enhancing network security through software defined networking (SDN). In 2016 25th international conference on computer communication and networks (ICCCN) (pp. 1-9). IEEE.

Li, W., Meng, W., & Kwok, L. F. (2016). A survey on OpenFlow-based Software Defined Networks: Security challenges and countermeasures. Journal of Network and Computer Applications, 68, 126-139.

François, J., Dolberg, L., Festor, O., & Engel, T. (2014, October). Network security through software defined networking: a survey. In Proceedings of the Conference on Principles, Systems and Applications of IP Telecommunications (pp. 1-8).

Maheshwari, A., Mehraj, B., Khan, M. S., & Idrisi, M. S. (2022). An optimized weighted voting-based ensemble model for DDoS attack detection and mitigation in SDN environment. Microprocessors and Microsystems, 89, 104412.

Benzekki, K., El Fergougui, A., & Elbelrhiti Elalaoui, A. (2016). Software‐defined networking (SDN): a survey. Security and communication networks, 9(18), 5803-5833.

Haleplidis, E., Pentikousis, K., Denazis, S., Salim, J. H., Meyer, D., & Koufopavlou, O. (2015). Software-defined networking (SDN): Layers and architecture terminology (No. rfc7426).

Karakus, M., & Durresi, A. (2017). A survey: Control plane scalability issues and approaches in software-defined networking (SDN). Computer Networks, 112, 279-293.

Barrett, R., Facey, A., Nxumalo, W., Rogers, J., Vatcher, P., & St-Hilaire, M. (2017, January). Dynamic traffic diversion in SDN: testbed vs mininet. In 2017 International Conference on Computing, Networking, and Communications (ICNC) (pp. 167-171). IEEE.

Rego, A., Garcia, L., Sendra, S., & Lloret, J. (2018). Software Defined Network-based control system for an efficient traffic management for emergency situations in smart cities. Future Generation Computer Systems, 88, 243-253.

Nazar, M. J., Iqbal, S., Altaf, S., Qureshi, K. N., Usmani, K. H., & Wassan, S. (2022). Software-Defined Networking (SDN) Security Concerns. In Information Security Handbook (pp. 19-38). CRC Press.

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Published

27.12.2023

How to Cite

Alshammari, M. A. ., Abd El-Aziz, A. A. ., & Hamdi, H. . (2023). Detecting Traffic Diversion Using Metaheuristic Algorithm in SDN. International Journal of Intelligent Systems and Applications in Engineering, 12(9s), 369–379. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4327

Issue

Vol. 12 No. 9s (2024)

Section

Research Article

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