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Optimal Deep-Learning-Based Cyberattack Detection in a Blockchain-Assisted IoT Environment

Author

Listed:
  • Fatmah Y. Assiri

    (Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah 21493, 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 the most extensively utilized technology nowadays that is simple and has the advantage of replacing the data with other devices by employing cloud or wireless networks. However, cyber-threats and cyber-attacks significantly affect smart applications on these IoT platforms. The effects of these intrusions lead to economic and physical damage. The conventional IoT security approaches are unable to handle the current security problems since the threats and attacks are continuously evolving. In this background, employing Artificial Intelligence (AI) knowledge, particularly Machine Learning (ML) and Deep Learning (DL) solutions, remains the key to delivering a dynamically improved and modern security system for next-generation IoT systems. Therefore, the current manuscript designs the Honey Badger Algorithm with an Optimal Hybrid Deep Belief Network (HBA-OHDBN) technique for cyberattack detection in a blockchain (BC)-assisted IoT environment. The purpose of the proposed HBA-OHDBN algorithm lies in its accurate recognition and classification of cyberattacks in the BC-assisted IoT platform. In the proposed HBA-OHDBN technique, feature selection using the HBA is implemented to choose an optimal set of features. For intrusion detection, the HBA-OHDBN technique applies the HDBN model. In order to adjust the hyperparameter values of the HDBN model, the Dung Beetle Optimization (DBO) algorithm is utilized. Moreover, BC technology is also applied to improve network security. The performance of the HBA-OHDBN algorithm was validated using the benchmark NSLKDD dataset. The extensive results indicate that the HBA-OHDBN model outperforms recent models, with a maximum accuracy of 99.21%.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:19:p:4080-:d:1248186
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    References listed on IDEAS

    as
    1. Hasan Alkahtani & Theyazn H. H. Aldhyani & M. Irfan Uddin, 2021. "Intrusion Detection System to Advance Internet of Things Infrastructure-Based Deep Learning Algorithms," Complexity, Hindawi, vol. 2021, pages 1-18, July.
    2. Hashim, Fatma A. & Houssein, Essam H. & Hussain, Kashif & Mabrouk, Mai S. & Al-Atabany, Walid, 2022. "Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 192(C), pages 84-110.
    3. 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.
    Full references (including those not matched with items on IDEAS)

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