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Machine Learning-Based Intrusion Detection Approaches for Secured Internet of Things

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The Effect of Information Technology on Business and Marketing Intelligence Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1056))

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Abstract

Nowadays, protecting communication and information for Internet of Things (IOT) has emerged as a critical challenge. Existing systems use firewalls to ensure that they are safe from any unexpected occurrences that may disrupt the desired systems and applications. Intrusion detection systems (IDSs) are an acceptable second line of defence for IOT applications. IDS play a crucial role ensuring that it enhances the IOT security level maintaining sophisticated framework. Attackers have continuously been attempting to determine novel ways to circumnavigate security frameworks that prevent the structures. This paper reviews the security advances, threats and countermeasures for the IOT applications. A state of art review has accomplished using the references from 2009 to 2020 to encompass the real demography of the IOT security research data. This work also highlights the deep learning-based intrusion detection approaches for Internet of Things (IOT) security. With the systematic literature review approach, the review suggests that implementing existing security measures, such as encryption, authentication, access control, network and application security for IoT systems and their intrinsic amenability is ineffective for the IOT systems.

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Authors and Affiliations

  1. School of Information Technology, Skyline University College, Sharjah, UAE

    Taher M. Ghazal & Waleed Alomoush

  2. Faculty of Information Science and Technology, Center for Cyber Security, University Kebansaan Malaysia (UKM), Bangi, Malaysia

    Taher M. Ghazal, Mohammad Kamrul Hasan, Siti Norul Huda Sheikh Abdullah & Khairul Azmi Abu Bakar

  3. Department of Computer Engineering, College of Engineering, Mutah University, Mu’tah, Jordan

    Nidal A. Al-Dmour

  4. Faculty of Management, Canadian University Dubai, Dubai, UAE

    Raed A. Said

  5. Faculty of Engineering, Applied Science and Technology, Canadian University Dubai, Dubai, UAE

    Tamer Mohamed Abdellatif

  6. Electrical & Computer Engineering Department, University of Western Ontario, Ontario, ON, Canada

    Abdallah Moubayed

  7. School of Business, Skyline University College, Sharjah, UAE

    Haitham M. Alzoubi

  8. Department of Marketing, School of Business, The University of Jordan, Amman, 11942, Jordan

    Muhammad Alshurideh

  9. Department of Management, College of Business, University of Sharjah, 27272, Sharjah, United Arab Emirates

    Muhammad Alshurideh

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  1. Taher M. Ghazal
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  2. Mohammad Kamrul Hasan
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  7. Tamer Mohamed Abdellatif
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  8. Abdallah Moubayed
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  9. Haitham M. Alzoubi
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  10. Muhammad Alshurideh
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  11. Waleed Alomoush
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Correspondence to Haitham M. Alzoubi .

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Editors and Affiliations

  1. Dept of Mngmnt, Cllg of Busness Admin, University of Sharjah, Sharjah, United Arab Emirates

    Muhammad Alshurideh

  2. Department of Business Administration, Faculty of Economics and Administrative Sciences, Hashemite University, Zarqa, Jordan

    Barween Hikmat Al Kurdi

  3. Management Information Systems Department, School of Business, University of Jordan, Aqaba, Jordan

    Ra’ed Masa’deh

  4. Skyline University College, Sharjah, United Arab Emirates

    Haitham M. Alzoubi

  5. Research Institute of Sciences and Engineering, University of Sharjah, Sharjah, United Arab Emirates

    Said Salloum

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Ghazal, T.M. et al. (2023). Machine Learning-Based Intrusion Detection Approaches for Secured Internet of Things. In: Alshurideh, M., Al Kurdi , B.H., Masa’deh, R., Alzoubi , H.M., Salloum, S. (eds) The Effect of Information Technology on Business and Marketing Intelligence Systems. Studies in Computational Intelligence, vol 1056. Springer, Cham. https://doi.org/10.1007/978-3-031-12382-5_110

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  • DOI: https://doi.org/10.1007/978-3-031-12382-5_110

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  • Online ISBN: 978-3-031-12382-5

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