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Enhanced Metaheuristics with Machine Learning Enabled Cyberattack Detection Model

Ahmed S. Almasoud*

Department of Information Systems, College of Computer and Information Sciences, Prince Sultan University, Riyadh, 12435, Saudi Arabia

* Corresponding Author: Ahmed S. Almasoud. Email: email

Intelligent Automation & Soft Computing 2023, 37(3), 2849-2863. https://doi.org/10.32604/iasc.2023.039718

Abstract

The Internet of Things (IoT) is considered the next-gen connection network and is ubiquitous since it is based on the Internet. Intrusion Detection System (IDS) determines the intrusion performance of terminal equipment and IoT communication procedures from IoT environments after taking equivalent defence measures based on the identified behaviour. In this background, the current study develops an Enhanced Metaheuristics with Machine Learning enabled Cyberattack Detection and Classification (EMML-CADC) model in an IoT environment. The aim of the presented EMML-CADC model is to detect cyberattacks in IoT environments with enhanced efficiency. To attain this, the EMML-CADC model primarily employs a data preprocessing stage to normalize the data into a uniform format. In addition, Enhanced Cat Swarm Optimization based Feature Selection (ECSO-FS) approach is followed to choose the optimal feature subsets. Besides, Mayfly Optimization (MFO) with Twin Support Vector Machine (TSVM), called the MFO-TSVM model, is utilized for the detection and classification of cyberattacks. Here, the MFO model has been exploited to fine-tune the TSVM variables for enhanced results. The performance of the proposed EMML-CADC model was validated using a benchmark dataset, and the results were inspected under several measures. The comparative study concluded that the EMML-CADC model is superior to other models under different measures.

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Cite This Article

APA Style
Almasoud, A.S. (2023). Enhanced metaheuristics with machine learning enabled cyberattack detection model. Intelligent Automation & Soft Computing, 37(3), 2849-2863. https://doi.org/10.32604/iasc.2023.039718
Vancouver Style
Almasoud AS. Enhanced metaheuristics with machine learning enabled cyberattack detection model. Intell Automat Soft Comput . 2023;37(3):2849-2863 https://doi.org/10.32604/iasc.2023.039718
IEEE Style
A.S. Almasoud, "Enhanced Metaheuristics with Machine Learning Enabled Cyberattack Detection Model," Intell. Automat. Soft Comput. , vol. 37, no. 3, pp. 2849-2863. 2023. https://doi.org/10.32604/iasc.2023.039718



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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