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Enhanced IoT Based IDS Driven by Binary Snake Optimizer for Feature Selection

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Advances in Model and Data Engineering in the Digitalization Era (MEDI 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1751))

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Abstract

Connected devices have extended the borders of the traditional Internet into the new Internet of Things (IoT). IoT holds a significant role in several fields such as industry, transportation, smart homes, cities, and others. However, protecting IoT environments and preventing intrusions is one of the critical problems in IoT. An intrusion detection system (IDS) aims to identify malicious patterns and threats that traditional security countermeasures cannot detect. This paper presents an effective feature selection (FS) approach driven by a binary variant of the newly proposed Snake Optimizer (SO) for enhancing intrusion detection systems. Two variants of FS are developed, and the best one that is based on a V-shaped transfer function is compared with several optimization algorithms to confirm its efficiency in boosting IDSs. In addition, five datasets that represent real IoT traffic are employed for evaluation purposes. The experimental results show that SO based on V-shaped transfer is superior to the S-shaped transfer function and outperforms other optimizers in particular based on the obtained average accuracy and convergence rates. Hence, it can conclude that the proposed approach can be efficiently employed in IoT intrusion detection systems.

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Acknowledgements

The research leading to these results has received funding from the Research Council (TRC) of the Sultanate of Oman under the Block Funding Program with agreement no. TRC/BFP/ASU/01/2019.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, A’Sharqiyah University, Ibra, 400, Oman

    Ayman A. El-Saleh

  2. Department of Computer Systems Engineering, Arab American University, Jenin, Palestine

    Thaer Thaher

  3. Information Technology Engineering, Al-Quds University, Jerusalem, Palestine

    Thaer Thaher

  4. Faculty of Information Technology, Sebha University, Sebha, Libya

    Hamouda Chantar

  5. Department of Computer Science, Birzeit University, Birzeit, Palestine

    Majdi Mafarja

Authors
  1. Ayman A. El-Saleh
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  2. Thaer Thaher
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  3. Hamouda Chantar
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  4. Majdi Mafarja
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Corresponding author

Correspondence to Thaer Thaher .

Editor information

Editors and Affiliations

  1. Shenzhen University, Shenzhen, China

    Philippe Fournier-Viger

  2. Nile University, Giza, Egypt

    Ahmed Hassan

  3. ISAE-ENSMA, Poitiers, France

    Ladjel Bellatreche

  4. University of Tartu, Tartu, Estonia

    Ahmed Awad

  5. Mohammed V University, Rabat, Morocco

    Abderrahim Ait Wakrime

  6. LIAS/ISAE-ENSMA, Futuroscope, France

    Yassine Ouhammou

  7. University of Paris-Saclay, Gif-sur-Yvette, France

    Idir Ait Sadoune

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El-Saleh, A.A., Thaher, T., Chantar, H., Mafarja, M. (2022). Enhanced IoT Based IDS Driven by Binary Snake Optimizer for Feature Selection. In: Fournier-Viger, P., et al. Advances in Model and Data Engineering in the Digitalization Era. MEDI 2022. Communications in Computer and Information Science, vol 1751. Springer, Cham. https://doi.org/10.1007/978-3-031-23119-3_3

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  • DOI: https://doi.org/10.1007/978-3-031-23119-3_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23118-6

  • Online ISBN: 978-3-031-23119-3

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