Metaheuristic-Driven Feature Selection for IoT Intrusion Detection: A Hierarchical Arithmetic Optimization Approach

The increasing sophistication of cyberattacks in Internet of Things (IoT) networks requires strong Intrusion Detection Systems (IDS) with optimal feature selection mechanisms. High-dimensional data, computational complexity, and suboptimal detection accuracy hinder conventional IDS mechanisms. To overcome these limitations, in this study, the Hierarchical Self-Adaptive Arithmetic Optimization Algorithm (HSAOA) is introduced as a new metaheuristic method for IDS feature selection. HSAOA combines a stochastic spiral exploration method, an adaptive hierarchical model of leaders and followers, and a differential mutation mechanism to improve exploration-exploitation balance, global search capability, and premature convergence. The NF-ToN-IoT dataset is used to test the model, wherein HSAOA undertakes the feature selection process, and classification accuracy is increased by utilizing Random Forest (RF). The experimental results indicate that the proposed HSAOA is better than other advanced approaches in accuracy, computational efficiency, and convergence speed. These results validate the proposed algorithm as a scalable and effective solution for enhancing cybersecurity in IoT environments by improving IDS performance and reducing feature selection complexity.

@article{GUO2025,
  title     = {Metaheuristic-Driven Feature Selection for IoT Intrusion Detection: A Hierarchical Arithmetic Optimization Approach},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {6},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Jing GUO and Dejun ZHU and Qing XU},
  doi       = {10.14569/IJACSA.2025.0160664},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160664}
}