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Intrusion Detection in Software-Defined Networking Using Machine Learning Models

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Artificial Intelligence, Data Science and Applications (ICAISE 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 838))

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Abstract

Software-defined networking (SDN) is a new networking paradigm developed to reduce network complexity via control and management of the network from a centralized location. Nevertheless, the dynamic nature of SDN can lead to many vulnerabilities and threats, including denial of service (DOS) and Distributed Denial of service (DDoS) attacks. Thus, deploying Intrusion Detection Systems (IDSs) based on machine learning (ML) is a crucial part of the network architecture to monitor malevolent activities. This paper compares three ML models for intelligent intrusion detection in SDN: support vector machines, K-Nearest Neighbors, and Naive Bayes Networks. To evaluate and measure the performance of ML models, we used the DDoS-SDN dataset and compared their evaluation metrics, such as accuracy.

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Author information

Authors and Affiliations

  1. Multidisciplinary Faculty of Nador, University Mohammed Premier, Oujda, Morocco

    Lamiae Boukraa, Siham Essahraui, Khalid El Makkaoui, Ibrahim Ouahbi & Redouane Esbai

Authors
  1. Lamiae Boukraa
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  2. Siham Essahraui
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  3. Khalid El Makkaoui
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  4. Ibrahim Ouahbi
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  5. Redouane Esbai
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Corresponding author

Correspondence to Lamiae Boukraa .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Moulay Ismail University, Errachidia, Morocco

    Yousef Farhaoui

  2. Centre of AI and Robotics, Napier University, Edinburgh, UK

    Amir Hussain

  3. Prince Sultan University, Riyadh, Saudi Arabia

    Tanzila Saba

  4. University Canada West, Vancouver, BC, Canada

    Hamed Taherdoost

  5. Institute of Science, Banaras Hindu University, Varanasi, India

    Anshul Verma

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Boukraa, L., Essahraui, S., El Makkaoui, K., Ouahbi, I., Esbai, R. (2024). Intrusion Detection in Software-Defined Networking Using Machine Learning Models. In: Farhaoui, Y., Hussain, A., Saba, T., Taherdoost, H., Verma, A. (eds) Artificial Intelligence, Data Science and Applications. ICAISE 2023. Lecture Notes in Networks and Systems, vol 838. Springer, Cham. https://doi.org/10.1007/978-3-031-48573-2_8

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