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P4-HLDMC: A Novel Framework for DDoS and ARP Attack Detection and Mitigation in SD-IoT Networks Using Machine Learning, Stateful P4, and Distributed Multi-Controller Architecture

Author

Listed:
  • Walid I. Khedr

    (Department of Information Technology, Zagazig University, Zagazig 44519, Egypt)

  • Ameer E. Gouda

    (Department of Information Technology, Zagazig University, Zagazig 44519, Egypt)

  • Ehab R. Mohamed

    (Department of Information Technology, Zagazig University, Zagazig 44519, Egypt)

Abstract

Distributed Denial of Service (DDoS) and Address Resolution Protocol (ARP) attacks pose significant threats to the security of Software-Defined Internet of Things (SD-IoT) networks. The standard Software-Defined Networking (SDN) architecture faces challenges in effectively detecting, preventing, and mitigating these attacks due to its centralized control and limited intelligence. In this paper, we present P4-HLDMC, a novel collaborative secure framework that combines machine learning (ML), stateful P4, and a hierarchical logically distributed multi-controller architecture. P4-HLDMC overcomes the limitations of the standard SDN architecture, ensuring scalability, performance, and an efficient response to attacks. It comprises four modules: the multi-controller dedicated interface (MCDI) for real-time attack detection through a distributed alert channel (DAC), the MSMPF, a P4-enabled stateful multi-state matching pipeline function for analyzing IoT network traffic using nine state tables, the modified ensemble voting (MEV) algorithm with six classifiers for enhanced detection of anomalies in P4-extracted traffic patterns, and an attack mitigation process distributed among multiple controllers to effectively handle larger-scale attacks. We validate our framework using diverse test cases and real-world IoT network traffic datasets, demonstrating high detection rates, low false-alarm rates, low latency, and short detection times compared to existing methods. Our work introduces the first integrated framework combining ML, stateful P4, and SDN-based multi-controller architecture for DDoS and ARP detection in IoT networks.

Suggested Citation

  • Walid I. Khedr & Ameer E. Gouda & Ehab R. Mohamed, 2023. "P4-HLDMC: A Novel Framework for DDoS and ARP Attack Detection and Mitigation in SD-IoT Networks Using Machine Learning, Stateful P4, and Distributed Multi-Controller Architecture," Mathematics, MDPI, vol. 11(16), pages 1-36, August.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:16:p:3552-:d:1219156
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    References listed on IDEAS

    as
    1. Tariq Ahamed Ahanger & Usman Tariq & Fadl Dahan & Shafique A. Chaudhry & Yasir Malik, 2023. "Securing IoT Devices Running PureOS from Ransomware Attacks: Leveraging Hybrid Machine Learning Techniques," Mathematics, MDPI, vol. 11(11), pages 1-24, May.
    2. Iyad Katib & Mahmoud Ragab, 2023. "Blockchain-Assisted Hybrid Harris Hawks Optimization Based Deep DDoS Attack Detection in the IoT Environment," Mathematics, MDPI, vol. 11(8), pages 1-16, April.
    3. Khalid Mohamed Hosny & Ameer El-Sayed Gouda & Ehab Rushdy Mohamed, 2020. "New Detection Mechanism for Distributed Denial of Service Attacks in Software Defined Networks," International Journal of Sociotechnology and Knowledge Development (IJSKD), IGI Global, vol. 12(2), pages 1-30, April.
    4. Xuejian Zhao & Huiying Su & Zhixin Sun, 2022. "An Intrusion Detection System Based on Genetic Algorithm for Software-Defined Networks," Mathematics, MDPI, vol. 10(21), pages 1-15, October.
    5. Harshit Shah & Dhruvil Shah & Nilesh Kumar Jadav & Rajesh Gupta & Sudeep Tanwar & Osama Alfarraj & Amr Tolba & Maria Simona Raboaca & Verdes Marina, 2023. "Deep Learning-Based Malicious Smart Contract and Intrusion Detection System for IoT Environment," Mathematics, MDPI, vol. 11(2), pages 1-22, January.
    6. Adel A. Ahmed & Sharaf J. Malebary & Waleed Ali & Ahmed A. Alzahrani, 2023. "A Provable Secure Cybersecurity Mechanism Based on Combination of Lightweight Cryptography and Authentication for Internet of Things," Mathematics, MDPI, vol. 11(1), pages 1-24, January.
    7. Chin-Shiuh Shieh & Thanh-Tuan Nguyen & Mong-Fong Horng, 2023. "Detection of Unknown DDoS Attack Using Convolutional Neural Networks Featuring Geometrical Metric," Mathematics, MDPI, vol. 11(9), pages 1-24, May.
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    More about this item

    Keywords

    SD-IoT; DDoS detection; ARP detection; machine learning; stateful P4; multi-controller; traffic monitoring;
    All these keywords.

    JEL classification:

    • P4 - Political Economy and Comparative Economic Systems - - Other Economic Systems

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