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Volume 13
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10.3390/app132312687
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Article

Enhancing Internet of Things Network Security Using Hybrid CNN and XGBoost Model Tuned via Modified Reptile Search Algorithm

by
Mohamed Salb
1,†,
Luka Jovanovic
1,†,
Nebojsa Bacanin
1,*,†,
Milos Antonijevic
1,†,
Miodrag Zivkovic
1,†,
Nebojsa Budimirovic
1,† and
Laith Abualigah
2,3,4,5,6,7,8,†
1
Department of Informatics and Computing, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia
2
Computer Science Department, Al al-Bayt University, Mafraq 25113, Jordan
3
Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon
4
Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan
5
MEU Research Unit, Middle East University, Amman 11831, Jordan
6
Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan
7
School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Malaysia
8
School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya 27500, Malaysia
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Appl. Sci. 2023, 13(23), 12687; https://doi.org/10.3390/app132312687
Submission received: 25 October 2023 / Revised: 14 November 2023 / Accepted: 17 November 2023 / Published: 27 November 2023
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Abstract

This paper addresses the critical security challenges in the internet of things (IoT) landscape by implementing an innovative solution that combines convolutional neural networks (CNNs) for feature extraction and the XGBoost model for intrusion detection. By customizing the reptile search algorithm for hyperparameter optimization, the methodology provides a resilient defense against emerging threats in IoT security. By applying the introduced algorithm to hyperparameter optimization, better-performing models are constructed capable of efficiently handling intrusion detection. Two experiments are carried out to evaluate the introduced technique. The first experiment tackles detection through binary classification. The second experiment handles the task by specifically identifying the type of intrusion through multi-class classification. A publicly accessible real-world dataset has been utilized for experimentation and several contemporary algorithms have been subjected to a comparative analysis. The introduced algorithm constructed models with the best performance in both cases. The outcomes have been meticulously statistically evaluated and the best-performing model has been analyzed using Shapley additive explanations to determine feature importance for model decisions.
Keywords: internet of things; feature reduction; convolutional neural networks; XGBoost; reptile search algorithm internet of things; feature reduction; convolutional neural networks; XGBoost; reptile search algorithm

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MDPI and ACS Style

Salb, M.; Jovanovic, L.; Bacanin, N.; Antonijevic, M.; Zivkovic, M.; Budimirovic, N.; Abualigah, L. Enhancing Internet of Things Network Security Using Hybrid CNN and XGBoost Model Tuned via Modified Reptile Search Algorithm. Appl. Sci. 2023, 13, 12687. https://doi.org/10.3390/app132312687

AMA Style

Salb M, Jovanovic L, Bacanin N, Antonijevic M, Zivkovic M, Budimirovic N, Abualigah L. Enhancing Internet of Things Network Security Using Hybrid CNN and XGBoost Model Tuned via Modified Reptile Search Algorithm. Applied Sciences. 2023; 13(23):12687. https://doi.org/10.3390/app132312687

Chicago/Turabian Style

Salb, Mohamed, Luka Jovanovic, Nebojsa Bacanin, Milos Antonijevic, Miodrag Zivkovic, Nebojsa Budimirovic, and Laith Abualigah. 2023. "Enhancing Internet of Things Network Security Using Hybrid CNN and XGBoost Model Tuned via Modified Reptile Search Algorithm" Applied Sciences 13, no. 23: 12687. https://doi.org/10.3390/app132312687

APA Style

Salb, M., Jovanovic, L., Bacanin, N., Antonijevic, M., Zivkovic, M., Budimirovic, N., & Abualigah, L. (2023). Enhancing Internet of Things Network Security Using Hybrid CNN and XGBoost Model Tuned via Modified Reptile Search Algorithm. Applied Sciences, 13(23), 12687. https://doi.org/10.3390/app132312687

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