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Open Access

An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security

Technology Higher School, Cadi Ayyad University, Essaouira 44000, Morocco.
IDMS Team, Faculty of Sciences and Techniques, Moulay Ismail University of Meknès, Errachidia 52000, Morocco.
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Abstract

Industrial Internet of Things (IIoT) represents the expansion of the Internet of Things (IoT) in industrial sectors. It is designed to implicate embedded technologies in manufacturing fields to enhance their operations. However, IIoT involves some security vulnerabilities that are more damaging than those of IoT. Accordingly, Intrusion Detection Systems (IDSs) have been developed to forestall inevitable harmful intrusions. IDSs survey the environment to identify intrusions in real time. This study designs an intrusion detection model exploiting feature engineering and machine learning for IIoT security. We combine Isolation Forest (IF) with Pearson’s Correlation Coefficient (PCC) to reduce computational cost and prediction time. IF is exploited to detect and remove outliers from datasets. We apply PCC to choose the most appropriate features. PCC and IF are applied exchangeably (PCCIF and IFPCC). The Random Forest (RF) classifier is implemented to enhance IDS performances. For evaluation, we use the Bot-IoT and NF-UNSW-NB15-v2 datasets. RF-PCCIF and RF-IFPCC show noteworthy results with 99.98% and 99.99% Accuracy (ACC) and 6.18 s and 6.25 s prediction time on Bot-IoT, respectively. The two models also score 99.30% and 99.18% ACC and 6.71 s and 6.87 s prediction time on NF-UNSW-NB15-v2, respectively. Results prove that our designed model has several advantages and higher performance than related models.

Keywords

Industrial Internet of Things (IIoT)isolation forestIntrusion Detection Dystem (IDS)intrusionPearson’s Correlation Coefficient (PCC)random forest

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Big Data Mining and Analytics
Volume 6 Issue 3,
September 2023
Pages 273-287
DOI: 10.26599/BDMA.2022.9020032
Cite this article:
Mohy-Eddine M, Guezzaz A, Benkirane S, et al. An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security. Big Data Mining and Analytics, 2023, 6(3): 273-287. https://doi.org/10.26599/BDMA.2022.9020032

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Received: 25 June 2022
Revised: 18 August 2022
Accepted: 01 September 2022
Published: 07 April 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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