A Comparative Study for SDN Security Based on Machine Learning

A Comparative Study for SDN Security Based on Machine Learning

Software Defined Network (SDN); Deep Neural Network (DNN); Machine Learning (ML); NSL-KDD;

In the past decade, traditional networks have been utilized to transfer data between more than one node. The primary problem related to formal networks is their stable essence, which makes them incapable of meeting the requirements of nodes recently inserted into the network. Thus, formal networks are substituted by a Software Defined Network (SDN). The latter can be utilized to construct a structure for intensive data applications like big data. In this paper, a comparative investigation of Deep Neural Network (DNN) and Machine Learning (ML) techniques that uses various feature selection techniques is undertaken. The ML techniques employed in this approach are decision tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM). The proposed approach is tested experimentally and evaluated using an available NSL–KDD dataset. This dataset includes 41 features and 148,517 samples. To evaluate the techniques, several estimation measurements are calculated. The results prove that DT is the most accurate and effective approach. Furthermore, the evaluation measurements indicate the efficacy of the presented approach compared to earlier studies.

Khattab M. Ali Alheeti

Abdulkareem Alzahrani

Maha Alamri

Aythem Khairi Kareem

Duaa Al_Dosary

International Journal of Interactive Mobile Technologies (iJIM); Vol. 17 No. 11 (2023); pp. 131-140

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2023-06-07

Copyright (c) 2023 Haider TH.Salim ALRikabi, Khattab M. Ali Alheeti, Abdulkareem Alzahrani, Maha Alamri, Aythem Khairi , Duaa Al_Dosary

https://creativecommons.org/licenses/by/4.0

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eng

info:eu-repo/semantics/article

info:eu-repo/semantics/publishedVersion

Peer-reviewed Article