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Detecting and Mitigating DDOS Attacks in SDNs Using Deep Neural Network

Gul Nawaz1, Muhammad Junaid1, Adnan Akhunzada2, Abdullah Gani2,*, Shamyla Nawazish3, Asim Yaqub3, Adeel Ahmed1, Huma Ajab4

1 Department of Information Technology, The University of Haripur, Haripur, 22060, Pakistan
2 Faculty of Computing and Informatics, University Malaysia Sabah, Sabah, 88400, Malaysia
3 Department of Environmental Sciences, COMSATS University Abbottabad Campus, Abbottabad, 22010, Pakistan
4 Department of Chemistry, COMSATS University Abbottabad Campus, Abbottabad, 22010, Pakistan

* Corresponding Author: Abdullah Gani. Email: email

Computers, Materials & Continua 2023, 77(2), 2157-2178.


Distributed denial of service (DDoS) attack is the most common attack that obstructs a network and makes it unavailable for a legitimate user. We proposed a deep neural network (DNN) model for the detection of DDoS attacks in the Software-Defined Networking (SDN) paradigm. SDN centralizes the control plane and separates it from the data plane. It simplifies a network and eliminates vendor specification of a device. Because of this open nature and centralized control, SDN can easily become a victim of DDoS attacks. We proposed a supervised Developed Deep Neural Network (DDNN) model that can classify the DDoS attack traffic and legitimate traffic. Our Developed Deep Neural Network (DDNN) model takes a large number of feature values as compared to previously proposed Machine Learning (ML) models. The proposed DNN model scans the data to find the correlated features and delivers high-quality results. The model enhances the security of SDN and has better accuracy as compared to previously proposed models. We choose the latest state-of-the-art dataset which consists of many novel attacks and overcomes all the shortcomings and limitations of the existing datasets. Our model results in a high accuracy rate of 99.76% with a low false-positive rate and 0.065% low loss rate. The accuracy increases to 99.80% as we increase the number of epochs to 100 rounds. Our proposed model classifies anomalous and normal traffic more accurately as compared to the previously proposed models. It can handle a huge amount of structured and unstructured data and can easily solve complex problems.


Cite This Article

G. Nawaz, M. Junaid, A. Akhunzada, A. Gani, S. Nawazish et al., "Detecting and mitigating ddos attacks in sdns using deep neural network," Computers, Materials & Continua, vol. 77, no.2, pp. 2157–2178, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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