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

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dc.contributor.authorGhazal, Taher M.
dc.contributor.authorHasan, Mohammad Kamrul
dc.contributor.authorAbdullah, Siti Norul Huda Sheikh
dc.contributor.authorBakar, Khairul Azmi Abu
dc.contributor.authorAl-Dmour, Nidal A.
dc.contributor.authorSaid, Raed A.
dc.contributor.authorAbdellatif, Tamer Mohamed
dc.contributor.authorMoubayed, Abdallah
dc.contributor.authorAlzoubi, Haitham M.
dc.contributor.authorAlshurideh, Muhammad
dc.contributor.authorAlomoush, Waleed
dc.date.accessioned2023-10-09T13:48:25Z
dc.date.available2023-10-09T13:48:25Z
dc.date.copyright© 2023
dc.date.issued2023
dc.description.abstractNowadays, 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. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
dc.identifier.citationGhazal, T. M., Hasan, M. K., Abdullah, S. N. H. S., Bakar, K. A. A., Al-Dmour, N. A., Said, R. A., Abdellatif, T. M., Moubayed, A., Alzoubi, H. M., Alshurideh, M., & Alomoush, W. (2023). Machine Learning-Based Intrusion Detection Approaches for Secured Internet of Things. In M. Alshurideh, B.H. Al Kurdi, R. Masa’deh, H.M. Alzoubi, & S. Salloum (Eds.) The Effect of Information Technology on Business and Marketing Intelligence Systems. Studies in Computational Intelligence, 1056 (pp. 2013 - 2036). Cham: Springer. https://doi.org/10.1007/978-3-031-12382-5_110
dc.identifier.issn1860949X
dc.identifier.urihttps://doi.org/10.1007/978-3-031-12382-5_110
dc.identifier.urihttps://hdl.handle.net/20.500.12519/882
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofseriesThe Effect of Information Technology on Business and Marketing Intelligence Systems. Studies in Computational Intelligence; Volume 1056
dc.rightsLicense to reuse abstract has been secured and provided by Springer Nature and Copyright Clearance Center.
dc.rights.holderCopyright : © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
dc.subjectInternet of Things security
dc.subjectIntrusion detection
dc.subjectMachine learning
dc.titleMachine Learning-Based Intrusion Detection Approaches for Secured Internet of Things
dc.typeBook chapter

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