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Blockchain-Assisted Hybrid Harris Hawks Optimization Based Deep DDoS Attack Detection in the IoT Environment

Author

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  • Iyad Katib

    (Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Mahmoud Ragab

    (Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

Abstract

The Internet of Things (IoT) is developing as a novel phenomenon that is applied in the growth of several crucial applications. However, these applications continue to function on a centralized storage structure, which leads to several major problems, such as security, privacy, and a single point of failure. In recent years, blockchain (BC) technology has become a pillar for the progression of IoT-based applications. The BC technique is utilized to resolve the security, privacy, and single point of failure (third-part dependency) issues encountered in IoT applications. Conversely, the distributed denial of service (DDoS) attacks on mining pools revealed the existence of vital fault lines amongst the BC-assisted IoT networks. Therefore, the current study designs a hybrid Harris Hawks with sine cosine and a deep learning-based intrusion detection system (H3SC-DLIDS) for a BC-supported IoT environment. The aim of the presented H3SC-DLIDS approach is to recognize the presence of DDoS attacks in the BC-assisted IoT environment. To enable secure communication in the IoT networks, BC technology is used. The proposed H3SC-DLIDS technique designs a H3SC technique by integrating the concepts of Harris Hawks optimization (HHO) and sine cosine algorithm (SCA) for feature selection. For the intrusion detection process, a long short-term memory auto-encoder (LSTM-AE) model is utilized in this study. Finally, the arithmetic optimization algorithm (AOA) is implemented for hyperparameter tuning of the LSTM-AE technique. The proposed H3SC-DLIDS method was experimentally validated using the BoT-IoT database, and the results indicate the superior performance of the proposed H3SC-DLIDS technique over other existing methods, with a maximum accuracy of 99.05%.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:8:p:1887-:d:1124744
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    References listed on IDEAS

    as
    1. Ahmed. H. A. Elkasem & Salah Kamel & Mohamed H. Hassan & Mohamed Khamies & Emad M. Ahmed, 2022. "An Eagle Strategy Arithmetic Optimization Algorithm for Frequency Stability Enhancement Considering High Renewable Power Penetration and Time-Varying Load," Mathematics, MDPI, vol. 10(6), pages 1-38, March.
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    Cited by:

    1. 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.
    2. Rayed AlGhamdi, 2023. "Design of Network Intrusion Detection System Using Lion Optimization-Based Feature Selection with Deep Learning Model," Mathematics, MDPI, vol. 11(22), pages 1-17, November.
    3. Fatmah Y. Assiri & Mahmoud Ragab, 2023. "Optimal Deep-Learning-Based Cyberattack Detection in a Blockchain-Assisted IoT Environment," Mathematics, MDPI, vol. 11(19), pages 1-16, September.

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