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A cutting-edge intelligent cyber model for intrusion detection in IoT environments leveraging future generations networks

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Abstract

With 5G technology driving its expansion as the main infrastructure for pervasive connection, the Internet of Things (IoT) symbolises a paradigm-shifting interconnectivity of objects and devices. The increasing integration of IoT devices into our daily lives poses serious security and privacy risks. Every smart object in an urban setting is connected, which increases the vulnerability of IoT-based smart cities to various security risks. It is crucial to guarantee these digital urban settings’ security and resilience, especially as cities become more computerised and have a dense population of linked devices. Ensuring the integrity and functionality of smart cities requires immediate attention to detecting and mitigating potential cyberattacks. This research presents an intrusion detection model derived from data extracted by simulating the SYNFLOOD attack scenario, a prominent form of Denial of Service attack in IoT security. The suggested detection model classifies, trains, and validates the imported data using the k-folds method and creates a unique detection model. The proposed model is fast and effectively enables all IoT networks to communicate information without compromising privacy. The model enhances the detection process by employing data preprocessing and balancing. In this work, the experiments’ accuracy is stable, proving the model’s success for the six used machine learning algorithms resulted in an excellent performance with an accuracy of 92.3% for the Decision Tree and Neural Network.

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Funding

The authors have not disclosed any funding.

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Authors and Affiliations

  1. Department of Information Technology, The Faculty of Prince Al-Hussein bin Abdullah II of Information Technology, The Hashemite University, Zarqa, 13133, Jordan

    Ala Mughaid, Asma Alnajjar, Subhieh M. El-Salhi & Khaled Almakadmeh

  2. Faculty of Science and IT, Al-Zaytoonah University of Jordan, Amman, Jordan

    Shadi AlZu’bi

Authors
  1. Ala Mughaid
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  2. Asma Alnajjar
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  3. Subhieh M. El-Salhi
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  4. Khaled Almakadmeh
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  5. Shadi AlZu’bi
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All authors worked in an equivalent load at all stages to produce this research.

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Correspondence to Ala Mughaid.

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Mughaid, A., Alnajjar, A., El-Salhi, S.M. et al. A cutting-edge intelligent cyber model for intrusion detection in IoT environments leveraging future generations networks. Cluster Comput 27, 10359–10375 (2024). https://doi.org/10.1007/s10586-024-04495-3

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