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Sridhar, Preetham Charan (2024) Securing 5G IoT Networks: A Machine Learning Framework for Zero-Trust Intrusion Detection System. Masters thesis, Dublin, National College of Ireland.
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Abstract
The integration of 5G and IoT devices has completely changed industries, where this seamless integration made the devices perform faster data transmission, real-time automation, and seamless device connectivity. However, this revolution also introduced major security challenges in the real world. The research addresses the challenges faced by the traditional setup by building advanced Intrusion Detection Systems (IDS) using machine learning techniques. Hybrid models like DT-CART which is combined with XGBoost have shown high performance with an accuracy of 99%. The research further provides an in-depth analysis and key findings of securing the 5G IoT networks by combining machine learning, hybrid models, and federated learning. The Federated Ensemble with Stacking and Majority Voting has achieved an accuracy of 91%, proving that the system can identify and respond to malicious activities in a distributed environment rather than keeping them stored or processed in a central server. The Federated learning was further explored by integrating Zero-Trust principles. Dynamic trust scores were used to exclude the untrusted clients, where this method makes sure that only the trusted clients will contribute to the global model results in reducing security risks like adversarial predictions and data poisoning.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Sahni, Vikas UNSPECIFIED |
| 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 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: | 28 Jul 2025 11:21 |
| Last Modified: | 28 Jul 2025 11:21 |
| URI: | https://norma.ncirl.ie/id/eprint/8261 |
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