MACHINE LEARNING-BASED INTRUSION DETECTION SYSTEMS FOR SDN: AN EMPIRICAL STUDY USING KNIME

Abstract

Software-Defined Networking (SDN) is a revolutionary approach to designing and managing networks that simplifies the process by separating the control and data planes. Nevertheless, these attributes make SDN susceptible to security risks. As a result, it is critical to include a Network Intrusion Detection System (NIDS) as a response. This study suggests utilizing machine learning models to improve the efficiency of NIDS in SDN systems. More specifically, we use two benchmark datasets—NSL KDD and UNSW-NB15—to create and test machine learning models that aim to improve SDN network security and reduce potential threats.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Lamiae Boukraa

Lamiae Boukraa (Laboratory MASI, Multidisciplinary Faculty of Nador, Mohammed Premier University, Oujda, Morocco) received her master’s degree in Networks and Systems from Hassan 1st University, Settat, Morocco, in 2013. Since 2015, she has been a mathematics teacher at a secondary school in the Nador region. Since 2021, she has been a PhD student at the MASI laboratory. Her research interests focus on SDN security-based machine learning. She has published three papers at international conferences.

Siham Essahraui

Siham Essahraui (Laboratory MASI, Multidisciplinary Faculty of Nador, Mohammed Premier University, Oujda, Morocco) received her Master of Science degree in Computer Science from the Multidisciplinary Faculty of Nador at the University Mohammed Premier, Oujda, Morocco. She is currently pursuing her Ph.D. in computer science at the same prestigious institution. As an artificial intelligence scientist, her research primarily focuses on analyzing human behavior using advanced AI techniques. Her research primarily focuses on the analysis of human behavior using artificial intelligence techniques. Her contributions highlight a commitment to pushing the boundaries of artificial intelligence, particularly in the fields of machine learning, deep learning, computer vision, and data science, and its applications in real-world educational settings.

Yassine Maleh

Yassine Maleh (Laboratory LaSTI, ENSAK, University Sultan Moulay Slimane, Morocco) is a professor of cybersecurity and IT governance at Sultan Moulay Slimane University, Morocco. He is the founding chair of IEEE Consultant Network Morocco and founding president of the African Research Center of Information Technology & Cybersecurity. He is a senior member of IEEE and a member of the International Association of Engineers IAENG and The Machine Intelligence Research Labs. Dr Maleh has made contributions in the fields of information security and privacy, Internet of things security, wireless and constrained networks security. His research interests include information security and privacy, Internet of things, networks security, information system, and IT governance. He has published over than 100 papers (book chapters, international journals, and conferences/workshops), 17 edited books, and 3 authored books. He is the editor-in-chief of the International Journal of Information Security and Privacy, and the International Journal of Smart Security Technologies (IJSST). He serves as an associate editor for IEEE Access (2019 Impact Factor 4.098), the International Journal of Digital Crime and Forensics (IJDCF), and the International Journal of Information Security and Privacy (IJISP). He is a series editor of Advances in Cybersecurity Management, by CRC Taylor & Francis. He was also a guest editor of a special issue on Recent Advances on Cyber Security and Privacy for Cloud-of-Things of the International Journal of Digital Crime and Forensics (IJDCF), Volume 10, Issue 3, July–September 2019. He has served and continues to serve on executive and technical program committees and as a reviewer of numerous international conferences and journals such as Elsevier Ad Hoc Networks, IEEE Network Magazine, IEEE Sensor Journal, ICT Express, and Springer Cluster Computing. He was the Publicity chair of BCCA 2019 and the General Chair of the MLBDACP 19 symposium and ICI2C’21 Conference. He received Publons Top 1% reviewer award for the years 2018 and 2019.

Khalid El Makkaoui

Khalid El Makkaoui (Multidisciplinary Faculty of Nador, Mohammed Premier University, Oujda, Morocco) received his master’s degree in networks and systems and a Ph.D. in computer science from Hassan 1st University, Settat, Morocco, in 2014 and 2018. Since 2019, he has been an Associate Professor with the Department of Computer Science at the Multidisciplinary Faculty of Nador, University Mohammed Premier, Oujda, Morocco. His research interests focus on cybersecurity and artificial intelligence. He has published over 40 papers (book chapters, international journals, and conferences).

Ibrahim Ouahbi

Ibrahim Ouahbi (Multidisciplinary Faculty of Nador, Mohammed Premier University, Oujda, Morocco) is a professor of computer science at the Multidisciplinary Faculty of Nador, University Mohammed Premier, Oujda, Morocco. He received his Ph.D. in didactics of informatics in 2018 from Sidi Mohammed Ben Abdellah University, Fez, Morocco. He was a professor of educational technologies at the Faculty of Educational Sciences, Mohammed V University of Rabat, in 2019. His research interests include artificial intelligence, cybersecurity, and ICT integration in science education and learning.

Redouane Esbai

Redouane Esbai (Laboratory MASI, Multidisciplinary Faculty of Nador, Mohammed Premier University, Oujda) Redouane Esbai teaches the concept of Information System at Mohammed Premier University. He got his thesis of national doctorate in 2012. He got a degree of an engineer in Computer Sciences from the National School of Applied Sciences at Oujda. His activities of research focusing on MDA integrating new technologies DSL, JEE, Spring, and LLM.