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Intrusion Detection System for Industrial Control Systems using Classification Techniques

Multani, Prabhjeet Singh (2022) Intrusion Detection System for Industrial Control Systems using Classification Techniques. Masters thesis, Dublin, National College of Ireland.

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The operational technology using intelligent devices and make all the machineries work automatically to increase the production and the attacks in these systems are increasing exponentially. Attackers can send the malicious data in the SCADA system to interrupt the function or cause severe damage. The researchers implemented several models to improve the intrusion detection system on this problem. The explanation in this paper is about the implementation of classification techniques using machine learning algorithms for building the model with the ICS dataset which is provided in the Mississippi university platform. Dataset has been cleaned, transformed, and analysed. In machine learning total four number of models are implemented such as Logistic Regression, Random Forest, Decision Tree, and XGB classifier. As mentioned earlier dataset is about industrial control system, where the bulk of units are allotted to natural and attack.

The proposed study focuses on machine learning techniques to detect attacks in the SCADA network. After implementing four models, the best detection model will be selected for IDS in ICS. The research was carried out to measure the accuracy, precision, recall, and f1-score and compared to get the best model for the detection of the attacks.

Item Type: Thesis (Masters)
Uncontrolled Keywords: SCADA systems; Intrusion Detection System; Random Forest; Decision Tree; Logistic Regression; XGB classifier
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software > Computer Security
T Technology > T Technology (General) > Information Technology > Computer software > Computer Security
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Cyber Security
Depositing User: Tamara Malone
Date Deposited: 22 Dec 2022 13:13
Last Modified: 07 Mar 2023 14:24

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