A Combinational Approach for Intrusion Detection against Cyber Attacks in SCADA using Machine learning and Deep Learning Models

Punetha Velu, Ajay Karthi (2023) A Combinational Approach for Intrusion Detection against Cyber Attacks in SCADA using Machine learning and Deep Learning Models. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (1MB) | Preview
Preview	PDF (Configuration Manual) Download (2MB) | Preview

Abstract

This research work serves to explore and apply machine learning and deep learning models to support cyber security in SCADA systems for the purpose of intrusion detection. The Chapter investigates the development of SCADA systems and how interoperability has made it susceptible to cyber-attacks. The paper examines different machine learning and deep learning models, such as AdaBoost, XGBoost, GRU+LSTM, and GRU+BILSTM, which are specifically designed to detect and classify different cyberattack types. The key chapters involve a detailed literature review, comprehensive methodology, design specification, rigorous modelling implementation and evaluation, and employing UNR-IDD datasets to authentically model cyber threats. The research includes several case studies that showcase how these models can be effective against several common SCADA cyber threats, namely DoS, MitM, SQL Injection, and APTs. The results curating during the process demonstrate that out of the four models used GRUBILSTM provided the highest accuracy value of ‘89%’. The paper focuses on the gaps and weaknesses of the current research and possible directions for future research, emphasising the prospect of employing machine learning & deep learning models in conjunction for hardening critical infrastructures against cyberattacks. The work is invaluable as a contribution to SCADA cybersecurity and its convergence with machine learning and deep learning.

Item Type:	Thesis (Masters)
Supervisors:	Name Email McLaughlin, Eugene UNSPECIFIED
Uncontrolled Keywords:	SCADA (supervisory control and data acquisition); Intrusion detection System (IDS); Machine Learning (ML); Deep Learning (DL); LSTM (Long Short Term Memory); Gated recurring unit (GRU); Industrial Internet of Thing (IIOT)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Algebra > Algorithms > Computer algorithms Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > Computer networks > Internet of things Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	22 Apr 2025 12:30
Last Modified:	06 May 2025 14:21
URI:	https://norma.ncirl.ie/id/eprint/7456

Actions (login required)

View Item