A Comparative Study for SDN Security Based on Machine Learning

Authors

  • Khattab M. Ali Alheeti College of Computer and Information Technology, Computer Networking Systems De-partment, University of Anbar, Anbar, Iraq
  • Abdulkareem Alzahrani Faculty of Computer Science and Information Technology, Al Baha University, Al Baha, Saudi Arabia
  • Maha Alamri Faculty of Computer Science and Information Technology, Al Baha University, Al Baha, Saudi Arabia
  • Aythem Khairi Kareem Department of Heet Education General Directorate of Education in Anbar, Ministry of Education, Heet, Anbar, Iraq
  • Duaa Al_Dosary College of Computer and Information Technology, Computer Networking Systems Department, University of Anbar, Anbar, Iraq

DOI:

https://doi.org/10.3991/ijim.v17i11.39065

Keywords:

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

Abstract


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.

Cover iJIM Vol. 17 No. 11 (2023)

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Published

2023-06-07

How to Cite

Khattab M. Ali Alheeti, Abdulkareem Alzahrani, Maha Alamri, Aythem Khairi Kareem, & Duaa Al_Dosary. (2023). A Comparative Study for SDN Security Based on Machine Learning. International Journal of Interactive Mobile Technologies (iJIM), 17(11), pp. 131–140. https://doi.org/10.3991/ijim.v17i11.39065

Issue

Vol. 17 No. 11 (2023)

Section

Papers

License

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

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