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Automated Machine Learning Enabled Cybersecurity Threat Detection in Internet of Things Environment

Fadwa Alrowais1, Sami Althahabi2, Saud S. Alotaibi3, Abdullah Mohamed4, Manar Ahmed Hamza5,*, Radwa Marzouk6

1 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
2 Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia
3 Department of Information Systems, College of Computing and Information System, Umm Al-Qura University, Saudi Arabia
4 Research Centre, Future University in Egypt, New Cairo, 11745, Egypt
5 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, AlKharj, Saudi Arabia
6 Department of Mathematics, Faculty of Science, Cairo University, Giza, 12613, Egypt

* Corresponding Author: Manar Ahmed Hamza. Email: email

Computer Systems Science and Engineering 2023, 45(1), 687-700. https://doi.org/10.32604/csse.2023.030188

Abstract

Recently, Internet of Things (IoT) devices produces massive quantity of data from distinct sources that get transmitted over public networks. Cybersecurity becomes a challenging issue in the IoT environment where the existence of cyber threats needs to be resolved. The development of automated tools for cyber threat detection and classification using machine learning (ML) and artificial intelligence (AI) tools become essential to accomplish security in the IoT environment. It is needed to minimize security issues related to IoT gadgets effectively. Therefore, this article introduces a new Mayfly optimization (MFO) with regularized extreme learning machine (RELM) model, named MFO-RELM for Cybersecurity Threat Detection and classification in IoT environment. The presented MFO-RELM technique accomplishes the effectual identification of cybersecurity threats that exist in the IoT environment. For accomplishing this, the MFO-RELM model pre-processes the actual IoT data into a meaningful format. In addition, the RELM model receives the pre-processed data and carries out the classification process. In order to boost the performance of the RELM model, the MFO algorithm has been employed to it. The performance validation of the MFO-RELM model is tested using standard datasets and the results highlighted the better outcomes of the MFO-RELM model under distinct aspects.

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APA Style
Alrowais, F., Althahabi, S., Alotaibi, S.S., Mohamed, A., Hamza, M.A. et al. (2023). Automated machine learning enabled cybersecurity threat detection in internet of things environment. Computer Systems Science and Engineering, 45(1), 687-700. https://doi.org/10.32604/csse.2023.030188
Vancouver Style
Alrowais F, Althahabi S, Alotaibi SS, Mohamed A, Hamza MA, Marzouk R. Automated machine learning enabled cybersecurity threat detection in internet of things environment. Comput Syst Sci Eng. 2023;45(1):687-700 https://doi.org/10.32604/csse.2023.030188
IEEE Style
F. Alrowais, S. Althahabi, S.S. Alotaibi, A. Mohamed, M.A. Hamza, and R. Marzouk "Automated Machine Learning Enabled Cybersecurity Threat Detection in Internet of Things Environment," Comput. Syst. Sci. Eng., vol. 45, no. 1, pp. 687-700. 2023. https://doi.org/10.32604/csse.2023.030188



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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