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

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.

@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},
  volume    = {16},
  number    = {4},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Wenpan SHI and Ning ZHANG},
  doi       = {10.14569/IJACSA.2025.0160443},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160443}
}