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An XGBoost-Based Approach for an Efficient RPL Routing Attack Detection

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Computational Collective Intelligence (ICCCI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13501))

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Abstract

Routing Protocol for Low Power and Lossy Networks (RPL) is characterized by a reliable routing functionality compared with traditional protocols in IoT domains. However, it has basic security functionalities; therefore, many hackers exploit this characteristic to make various attacks. Extending RPL security presents a challenge, mainly due to the constrained devices and connectivity to unsecured Internet. In this paper, several routing attacks in RPL such as hello flood, decreased rank, and version number modification have been analyzed in different scenarios. In addition, an anomaly-based intrusion detection system using the XGBoost algorithm has been proposed. Several simulations have been conducted to generate normal and attack data. Results demonstrate a high detection accuracy of the XGBoost for the three considered types of attacks compared to Naive Bayes, Stochastic Gradient Descent, Multilayer Perceptron, and Support Vector Machines.

This work is supported by Prince Sultan University in Saudi Arabia.

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Author information

Authors and Affiliations

  1. SERCOM Lab, Polytechnic School of Tunisia, University of Carthage, Carthage, Tunisia

    Faicel Yaakoubi, Aymen Yahyaoui & Rabah Attia

  2. Military Academy of Fondouk Jedid, Nabeul, Tunisia

    Aymen Yahyaoui

  3. RIOTU Lab, Prince Sultan University, Riyadh, Saudi Arabia

    Wadii Boulila

  4. RIADI Laboratory, National School of Computer Sciences, University of Manouba, Manouba, Tunisia

    Wadii Boulila

Authors
  1. Faicel Yaakoubi
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  2. Aymen Yahyaoui
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  3. Wadii Boulila
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  4. Rabah Attia
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Corresponding author

Correspondence to Wadii Boulila .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Open University of Cyprus, Nicosia, Cyprus

    Yannis Manolopoulos

  3. University of Pau and Pays de l'Adour, Anglet, France

    Richard Chbeir

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Adrianna Kozierkiewicz

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Yaakoubi, F., Yahyaoui, A., Boulila, W., Attia, R. (2022). An XGBoost-Based Approach for an Efficient RPL Routing Attack Detection. In: Nguyen, N.T., Manolopoulos, Y., Chbeir, R., Kozierkiewicz, A., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2022. Lecture Notes in Computer Science(), vol 13501. Springer, Cham. https://doi.org/10.1007/978-3-031-16014-1_48

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  • DOI: https://doi.org/10.1007/978-3-031-16014-1_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16013-4

  • Online ISBN: 978-3-031-16014-1

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