Open Access

ARTICLE

Machine Learning Based Cybersecurity Threat Detection for Secure IoT Assisted Cloud Environment

by Z. Faizal Khan¹, Saeed M. Alshahrani^2,*, Abdulrahman Alghamdi², Someah Alangari³, Nouf Ibrahim Altamami⁴, Khalid A. Alissa⁵, Sana Alazwari⁶, Mesfer Al Duhayyim⁷, Fahd N. Al-Wesabi⁸

1 Department of Computer Engineering, College of Computing and Information Technology, Shaqra University, Shaqra, Saudi Arabia
2 Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, Saudi Arabia
3 Department of Computer Science, College of Science and Humanities Dawadmi, Shaqra University, Shaqra, Saudi Arabia
4 Department of Mathematics, College of Education, Shaqra University, Shaqra, Saudi Arabia
5 Saudi Aramco Cybersecurity Chair, Networks and Communications Department, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia
6 Department of Information Technology, College of Computers and Information Technology, Taif University, Taif P.O. Box 11099, Taif, 21944, Saudi Arabia
7 Department of Computer Science, College of Sciences and Humanities-Aflaj, Prince Sattam bin Abdulaziz University, Alaflaj, 16828, Saudi Arabia
8 Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia

* Corresponding Author: Saeed M. Alshahrani. Email:

Computer Systems Science and Engineering 2023, 47(1), 855-871. https://doi.org/10.32604/csse.2023.036735

Received 10 October 2022; Accepted 21 December 2022; Issue published 26 May 2023

Abstract

The Internet of Things (IoT) is determine enormous economic openings for industries and allow stimulating innovation which obtain between domains in childcare for eldercare, in health service to energy, and in developed to transport. Cybersecurity develops a difficult problem in IoT platform whereas the presence of cyber-attack requires that solved. The progress of automatic devices for cyber-attack classifier and detection employing Artificial Intelligence (AI) and Machine Learning (ML) devices are crucial fact to realize security in IoT platform. It can be required for minimizing the issues of security based on IoT devices efficiently. Thus, this research proposal establishes novel mayfly optimized with Regularized Extreme Learning Machine technique called as MFO-RELM model for Cybersecurity Threat classification and detection from the cloud and IoT environments. The proposed MFO-RELM model provides the effective detection of cybersecurity threat which occur in the cloud and IoT platforms. To accomplish this, the MFO-RELM technique pre-processed the actual cloud and IoT data as to meaningful format. Besides, the proposed models will receive the pre-processing data and carry out the classifier method. For boosting the efficiency of the proposed models, the MFO technique was utilized to it. The experiential outcome of the proposed technique was tested utilizing the standard CICIDS 2017 dataset, and the outcomes are examined under distinct aspects.

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Keywords

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