Open Access

ARTICLE

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

Fadwa Alrowais¹, Sami Althahabi², Saud S. Alotaibi³, Abdullah Mohamed⁴, Manar Ahmed Hamza^5,*, 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:

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

Received 20 March 2022; Accepted 20 April 2022; Issue published 16 August 2022

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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Keywords

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