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International Journal of Computer Engineering in Research Trends. Scholarly, Peer-Reviewed,Open Access and Multidisciplinary

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Machine Learning-Based DDoS Saturation Attack Detection and analysis in SDN Environment

P Sandeep Kumar Reddy, M SriRaghavendra, K Sreenivasulu, T N Balakrishna,
Department of CSE, G.Pullaiah College of Engineering and Technology, Kurnool

To create dynamic, adaptable, manageable, and cost-effective computer networks, Software Defined Network (SDN) has been developed as a new methodology. As a result, the security of SDN is essential. Switches in SDN can match incoming packets to flow tables but not process anything. To identify SDN, DDoS, and saturation attacks, different Machine Learning-based detection methods have recently been presented. This method detects and analyses DDoS saturation attacks using Machine Learning in an SDN environment. The presented model utilizes a variety of Machine Learning (ML) methods, including AdaBoost, Decision Tree (DT), and Support Vector Machine (SVM). Experimental results clearly express that the described Machine Learning model provides more Accuracy, Precision, Recall and F1-Score compared to simple Machine Learning models. The combined Machine Learning (SVM+ DT+ AdaBoost) accuracy is 97.6%, precision, recall, F1-score values are 96.6%, 97.4%, 98% respectively.

P Sandeep Kumar Reddy,MSriRaghavendra,K Sreenivasulu,T N Balakrishna."Machine Learning-Based DDoS Saturation Attack Detection and analysis in SDN Environment". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.9, Issue 12,pp.269-274, December- 2022, URL :,

Keywords : Software Defined Network (SDN), Distributed Denial of Service Attacks (DDoS), Machine Learning, SVM, DT and AdaBoost.

[1] Shengli Du, Qiushuo Yan, Lijing Dong, JunfeiQiao, “Secure Consensus of Multiagent Systems With Input Saturation and Distributed Multiple DoS Attacks”, IEEE Transactions on Circuits and Systems II: Express Briefs, Volume: 69, Issue: 4, Year: 2022 
[2] Hamza Chahed, Andreas J. Kassler, “Software-Defined Time Sensitive  Networks Configuration and Management”, 2021 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), Year: 2021 
[3] Adel Alshamrani, “Reconnaissance Attack in SDN based Environments”, 2020 27th International Conference on Telecommunications (ICT), Year: 2020
[4] Nitin Varyani, Zhi-Li Zhang, David Dai, “QROUTE: An Efficient Quality of  Service (QoS) Routing Scheme for Software-Defined Overlay Networks”, IEEE Access, Volume: 8, Year: 2020 
[5] Jesús Arturo Pérez-Díaz, Ismael AmezcuaValdovinos, Kim-Kwang Raymond Choo, Dakai Zhu, “A Flexible SDN-Based Architecture for Identifying and Mitigating Low-Rate DDoS Attacks Using Machine Learning”, IEEE Access, Volume: 8, Year: 2020  
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[10] H. Lin and P. Wang, ‘‘Implementation of an SDN-based security defense mechanism against DDoS attacks,’’ in Proc. Joint Int. Conf. Econ. Manage. Eng. (ICEME), Int. Conf. Econ. Bus. Manage. (EBM), Philadelphia, PA, USA, 2016
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[13] N. F. Haq, A. R. Onik, M. A. K. Hridoy, M. Rafni, F. M. Shah, and D.M. Farid, “Application of machine learning Approaches in Intrusion Detection system: A survey”, international journal of advanced Research in Artificial Intelligence, vol. 4, no. 3, pp. 9 18, 2015
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