An advanced Intrusion Detection System for iiot based on ga and Tree based Algorithms



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An Advanced Intrusion Detection System for IIoT Based on GA and Tree Based Algorithms



This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2021.3104113, IEEE Access
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.
Digital Object Identifier 10.1109/ACCESS.2019.DOI
An advanced Intrusion Detection System
for IIoT Based on GA and Tree based
Algorithms
SYDNEY MAMBWE KASONGO
Department of Industrial Engineering and School of Data Science and Computational Thinking, University of Stellenbosch, South Africa
Corresponding author: Sydney M. Kasongo (e-mail: sydneyk@sun.ac.za).
ABSTRACT
The evolution of the Internet and cloud-based technologies have empowered several organizations with the
capacity to implement large-scale Internet of Things (IoT)-based ecosystems, such as Industrial IoT (IIoT).
The IoT and, by virtue, the IIoT, are vulnerable to new types of threats and intrusions because of the nature of
their networks. So it is crucial to develop Intrusion Detection Systems (IDSs) that can provide the security,
privacy, and integrity of IIoT networks. In this research, we propose an IDS for IIoT that was implemented
using the Genetic Algorithm (GA) for feature selection, and the Random Forest (RF) model was employed
in the GA fitness function. The models used for the intrusion detection processes include classifiers such
as the RF, Linear Regression (LR), Naïve Bayes (NB), Decision Tree (DT), Extra-Trees (ET), and Extreme
Gradient Boosting (XGB). The GA-RF generated 10 feature vectors for the binary classification scheme
and seven feature vectors for the multiclass classification procedure. The UNSW-NB15 is used to assess the
effectiveness and the robustness of our proposed approach. The experimental outcomes demonstrated that
for the binary modeling process, the GA-RF achieved a test accuracy (TAC) of 87.61% and an Area Under
the Curve (AUC) of 0.98, using a feature vector that contained 16 features. These results were superior to
existing IDS frameworks.

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