Machine Learning Evaluation of the HiTar-2024 Dataset for Intrusion Detection in Smart Manufacturing Environments

This study presents an investigation of the HiTar-2024 dataset performed in terms of the distribution of label attack types and the distribution of attacks by protocol, normal, and Denial of Service (DoS) connections over time. The investigation carried out a performance evaluation of the HiTar-2024 dataset using a machine learning approach to classify benign and malicious activities, based on BayesNet, Logistic, IBk, Multiclass, PART, and J48 classifiers. It was found that the HiTar-2024 dataset can serve as a training set for an anomaly-based intrusion detection system (IDS) in a smart manufacturing environment to detect normal and malicious activities. Furthermore, an anomaly-based IDS using the HiTar-2024 dataset is able to group malicious activities into Probing, Remote-to-Local, User-to-Root, and DoS attacks.

@article{Alhomoud2026,
  title     = {Machine Learning Evaluation of the HiTar-2024 Dataset for Intrusion Detection in Smart Manufacturing Environments},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {5},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Adeeb Alhomoud},
  doi       = {10.14569/IJACSA.2026.0170522},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170522}
}