Intrusion detection in Industrial OT environment by combination of different machine learning techniques.

Ghoshal, Arindam (2022) Intrusion detection in Industrial OT environment by combination of different machine learning techniques. Masters thesis, Dublin, National College of Ireland.

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Abstract

The frequency of attack on industrial systems have taken a sharp rise in recent times, as the traditional control systems have evolved and have incorporated parts of modern-day Information Technology into their architecture. Meanwhile the complexity of industrial system keeps us far from defending them largely from intrusion attacks. Hence more development in the security detection systems need to take place to protect such system from modern day cyber attacks. Although Intrusion detection system (IDS) are being used these days to secure such environment but not much research has taken place in this field. This research would throw light on whether Intrusion detection system’s performance can be enhanced with the help of combining the intrusion detection rate of multiple machine learning algorithms like Random Forest, K-Nearest Neighbour (KNN) and Multilayer perceptron (MLP) for identifying the attack vectors in industrial OT environment. This research produced best result with Random Forest when ran in isolation and slightly better result than Random Forest when combined with the other algorithms.

Item Type:	Thesis (Masters)
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:	19 Dec 2022 16:17
Last Modified:	07 Mar 2023 17:22
URI:	https://norma.ncirl.ie/id/eprint/6004

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