A Taxonomy of IDS in IoTs: ML Classifiers, Feature Selection Models, Datasets and Future Directions

The applications of the Internet of Things (IoT) are becoming increasingly popular nowadays. Network security and privacy are major concerns of the IoTs, as many IoT devices are connected to the network via the Internet, making IoT networks more vulnerable to various cyber-attacks. An Intrusion Detection System (IDS) is a solution to deal with security and privacy issues by protecting IoT networks from different types of attacks. In this paper, we provide a taxonomy of IDS in IoT. Different Machine Learning (ML) classifiers, feature selection models, and Datasets with high detection accuracy are presented. Our analysis indicates a heightened emphasis on ML-based IDS, with Support vector machines (SVMs) at 33% and RFs at 31% being the most widely used classifiers. Despite the diversity in the use of different datasets for IDS, the NSL-KDD is the most commonly used in 49% of studies. In the realm of feature selection, the K-means and SMO algorithms emerge with an impressive 99.33%, marking the highest percentage in previous research on feature selection for ML-based ID. Moreover, we addressed the future pathways and challenges of IDS detection.

@article{Alqahtani2024,
  title     = {A Taxonomy of IDS in IoTs: ML Classifiers, Feature Selection Models, Datasets and Future Directions},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Hessah Alqahtani and Monir Abdullah},
  doi       = {10.14569/IJACSA.2024.0150682},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150682}
}