A Hybrid Levy Arithmetic and Machine Learning-Based Intrusion Detection System for Software-Defined Internet of Things Environments

Abstract: The convergence of Software-Defined Networking (SDN) and the Internet of Things (IoT) has enabled a more adaptable framework for managing SDN-enabled IoT (SD-IoT) applications, but it also introduces significant cyber security risks. This study proposes a lightweight and explainable intrusion detection system (IDS) based on a hybrid Levy Arithmetic Algorithm (LAA) for SD-IoT environments. By integrating Levy randomization with the Arithmetic Optimization Algorithm (AOA), the LAA enhances feature selection efficiency while minimizing computational overhead. The model was evaluated using the NSL-KDD and UNSW-NB15 datasets. Experimental results demonstrate that the LAA outperformed baseline models, achieving up to 89.2% F1-score and 95.4% precision, while maintaining 100% detection of normal behaviors. These outcomes highlight the proposed system's potential for accurate and efficient detection of cyber-attacks in resource-constrained SD-IoT environments.

Wenpan SHI and Ning ZHANG. “A Hybrid Levy Arithmetic and Machine Learning-Based Intrusion Detection System for Software-Defined Internet of Things Environments”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.4 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160443

@article{SHI2025,
title = {A Hybrid Levy Arithmetic and Machine Learning-Based Intrusion Detection System for Software-Defined Internet of Things Environments},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160443},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160443},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {4},
author = {Wenpan SHI and Ning ZHANG}
}