Cloud-Based Intrusion Detection using Super Learner Ensemble in ICS/SCADA

Shaik, Mohammed Zubair (2025) Cloud-Based Intrusion Detection using Super Learner Ensemble in ICS/SCADA. Masters thesis, Dublin, National College of Ireland.

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Abstract

In cloud-integrated industrial environments, Intrusion Detection Systems (IDS) play a pivotal role in safeguarding Industrial Control Systems (ICS) and SCADA networks. Traditional IDS models often lack the scalability, adaptability, and performance required to handle heterogeneous and dynamic Industrial Internet of things (IIoT) data streams. Addressing these challenges, this study proposes a cloud-based IDS leveraging the ToN_IoT dataset and a Super Learner ensemble model. The system integrates Random Forest, Logistic Regression, and Naïve Bayes as base learners with Gradient Boosting as the meta-learner, achieving superior accuracy (95%) and efficient latency-throughput performance. Motivated by the increasing sophistication of cyber threats and the limitations of static signature-based methods, this approach enhances generalization and real-time detection capability. The entire pipeline is deployed on AWS (EC2, Cloud9, and S3), enabling scalable, accessible, and real-time operation. This work demonstrates the effectiveness of stacking ensemble techniques in cloud environments for ICS security and sets a benchmark for future intelligent IDS solutions.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Samarawickrama, Yasantha UNSPECIFIED
Uncontrolled Keywords:	Intrusion Detection System; ToN_IoT Dataset; Super Learning; Cloud Deployment
Subjects:	T Technology > T Technology (General) > Information Technology > Cloud computing 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
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Ciara O'Brien
Date Deposited:	31 Mar 2026 08:25
Last Modified:	31 Mar 2026 08:25
URI:	https://norma.ncirl.ie/id/eprint/9264

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