Open Access

ARTICLE

An Intrusion Detection System Based on HiTar-2024 Dataset Generation from LOG Files for Smart Industrial Internet-of-Things Environment

Tarak Dhaouadi¹, Hichem Mrabet^1,2,*, Adeeb Alhomoud³, Abderrazak Jemai^1,4

1 SER’Com Laboratory, Tunisia Polytechnic School, University of Carthage, Tunis, 2078, Tunisia
2 Computer Sciences Department, Faculty of Sciences of Tunis, University of Tunis El Manar, Tunis, 1001, Tunisia
3 Department of Science, College of Science and Theoretical Studies, Saudi Electronic University, Riyadh, 11673, Saudi Arabia
4 Computer Sciences Department, INSAT, University of Carthage, Tunis, 1080, Tunisia

* Corresponding Author: Hichem Mrabet. Email:

(This article belongs to the Special Issue: Exploring Recent Trends and Advances in Sensors Cybersecurity)

Computers, Materials & Continua 2025, 82(3), 4535-4554. https://doi.org/10.32604/cmc.2025.060935

Received 13 November 2024; Accepted 13 January 2025; Issue published 06 March 2025

Abstract

The increasing adoption of Industrial Internet of Things (IIoT) systems in smart manufacturing is leading to raise cyberattack numbers and pressing the requirement for intrusion detection systems (IDS) to be effective. However, existing datasets for IDS training often lack relevance to modern IIoT environments, limiting their applicability for research and development. To address the latter gap, this paper introduces the HiTar-2024 dataset specifically designed for IIoT systems. As a consequence, that can be used by an IDS to detect imminent threats. Likewise, HiTar-2024 was generated using the AREZZO simulator, which replicates realistic smart manufacturing scenarios. The generated dataset includes five distinct classes: Normal, Probing, Remote to Local (R2L), User to Root (U2R), and Denial of Service (DoS). Furthermore, comprehensive experiments with popular Machine Learning (ML) models using various classifiers, including BayesNet, Logistic, IBK, Multiclass, PART, and J48 demonstrate high accuracy, precision, recall, and F1-scores, exceeding 0.99 across all ML metrics. The latter result is reached thanks to the rigorous applied process to achieve this quite good result, including data pre-processing, features extraction, fixing the class imbalance problem, and using a test option for model robustness. This comprehensive approach emphasizes meticulous dataset construction through a complete dataset generation process, a careful labelling algorithm, and a sophisticated evaluation method, providing valuable insights to reinforce IIoT system security. Finally, the HiTar-2024 dataset is compared with other similar datasets in the literature, considering several factors such as data format, feature extraction tools, number of features, attack categories, number of instances, and ML metrics.

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Keywords

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