A Hybrid Mechanism to Detect DDoS Attacks in Software Defined Networks

  • َAfsaneh Banitalebi Dehkordi Department of Computer Science,Payame Noor University(PNU),P.OBOX,19395-4697 ,Tehran,Iran
  • MohammadReza Soltanaghaei Department of Computer Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran.
  • Farsad Zamani Boroujeni Department of Computer Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
Keywords: ∅-Entropy, WrapperSubsetEval,second order Correlation Coefficient ,SDN, DDoS attack


DDoS (Distributed Denial-of-Service) attacks are among the cyberattacks that are increasing day by day and have caused problems for computer network servers. With the advent of SDN networks, they are not immune to these attacks, and due to the software-centric nature of these networks, this type of attack can be much more difficult for them, ignoring effective parameters such as port and Source IP in detecting attacks, providing costly solutions which are effective in increasing CPU load, and low accuracy in detecting attacks are of the problems of previously presented methods in detecting DDoS attacks. Given the importance of this issue,the purpose of this paper is to increase the accuracy of DDoS attack detection using the second order correlation coefficient technique based on ∅-entropy according to source IP and selection of optimal features.To select the best features, by examining the types of feature selection algorithms and search methods, the WrapperSubsetEval feature selection algorithm, the BestFirst search method, and the best effective features were selected. This study was performed on CTU-13 and ISOT datasets and the results were compared with other methods. The accuracy of the detection in this work indicates the high efficiency of the proposed approach compared to other similar methods.  


[1] Yadav, A., et al., SDN Control Plan Security in Cloud Computing Against DDoS Attack. IJARIIE, 2016. 2(3): p. 426-430.
[2] Dayal, N., et al., Research Trends in Security and DDOS in SDN. Security and Communucation Networks, 2017. 9(18): p. 6386-6411.
[3] Kupreev, O., E. Badovskaya, and A. Gutnikov. DDoS attacks in Q1 2020. 2020; Available from: https://securelist.com/ddos-attacks-in-q1-2020/96837/.
[4] Morgan, S. CyberCrime Magazine. 2020; Available from: https://cybersecurityventures.com.
[5] Mirvaziri, H., A new method to reduce the effects of HTTP-Get Flood attack. Future Computing and Informatics Journal, 2017: p. 87-93.
[6] Kirubavathi, G. and R. Anitha, Botnet detection via mining of traffic flowcharacteristics. Computers and Electrical Engineering, 2016.
[7] Anbarsu, S., A.X. Annie Rayan, and V. Vetrian, Software-Defined Networking for the Internet of Things: Securing home networks using SDN, ed. R.-T.D.A.f.L.S.S. Data. 2020.
[8] Singh, K., K. Dhindsa, and D. Nehra, T-CAD: A threshold based collaborative DDoS attack detection in multiple autonomous systems. Journal of Information Security and Applications, 2020. 51.
[9] Bouyeddou, B., et al., DDOS-attacks detection using an efficient measurement-based statistical mechanism. Engineering Science and Technology, an international Journal 2020.
[10] Abdulqadder, I., et al., Multi-layered Intrusion Detection and Prevention in the SDN/NFV Enabled Cloud of 5G Networks using AI-based Defense Mechanisms. Computer Networks, 2020.
[11] Pradhan, A. and R. Mathew. Solution to Vulnerabilities and Threts in Software Defined Networking(SDN). in Third International Conference on Computing and Network Communications(CoCoNet'19). 2020.
[12] Velliangiri, S. and H.M. Pandey, Fuzzy-Taylor-elephant herd optimization inspired Deep Belief Network for DDoS attack detection and comparison with state-of-the-arts algorithms. Future Generation Computer Systems, 2020. 110: p. 80-90.
[13] Virupakshar, K., et al., Distributed Denial of Service (DDoS) Attacks Detection System for OpenStack-based Private Cloud. Procedia Computer Science, 2020.
[14] Fuente, D., A. Romero, and P. Torres, Existence and extendibility of rotationally symmetric graphs with a prescribed higher mean curvature function in Euclidean and Minkowski spaces. Journal of Mathematical Analysis and Applications, 2017. 446(1): p. 1046-1059.
[15] LIU, H., A collaborative defense framework against DDOS Attacks in networks. 2013, WASHINGTON STATE University.
[16] Xu, Z., et al., Software defect prediction based on kernel PCA and weighted extreme learning machine. Information and Software Technology, 2019. 106: p. 182-200.
[17] Bolly, F. and I. Gentil, ∅-entropy inequalities for diffusion semigroups. Journal de Mathématiques Pures et Appliqués. 93(5): p. 449-473.
[18] Song, Y., et al., Divergence-based cross entropy and uncertainty measures of Atanassov’s intuitionistic fuzzy sets with their application in decision making. Applied Soft Computing, 2019. 84.
[19] Hoque, N., et al., Network attacks: Taxonomy, tools and systems. Journal of Network and Computer Applications, , 2014. 40.
[20] Yavanoglu, O. and M. Aydos A review on cyber security datasets for machine learning algorithms in IEEE International Conference on Big Data (Big Data) 2017. Boston, MA, USA
[21] Bhamare, D., et al., Feasibility of Supervised Machine Learning for Cloud Security in International Conference on Information Science and Security (ICISS) 2016: Pattaya, Thailand
How to Cite
Banitalebi Dehkordiَ., Soltanaghaei, M., & Zamani Boroujeni, F. (2021). A Hybrid Mechanism to Detect DDoS Attacks in Software Defined Networks. Majlesi Journal of Electrical Engineering, 15(1), 1-8. https://doi.org/https://doi.org/10.29252/mjee.15.1.1

Most read articles by the same author(s)