Enhancing Network Intrusion Detection using Federated Learning

Altaf, Jawad Muhammad (2024) Enhancing Network Intrusion Detection using Federated Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

Cyber-attacks are increasing at an alarming rate as IOT, industrial control systems and other devices connected to the internet are exposed to malware, DDOS, DOS and malicious activities. Past research work is on centralized intrusion detection, which introduces issues like single point of failure, data privacy and scalability. Federated learning (FL) provides solutions to the issues concerning privacy and scalability by working and learning locally on distributed devices.

This research introduces a novel approach for enhanced intrusion detection using federated learning with Gated recurrent neural network integrated into flower federated framework, while comparing it with centralized machine learning technique using GRU (Gated Recurrent Unit). This research demonstrated centralized learning showed high accuracy of 97%, however, federated learning models preserved the privacy of data with moderated performance measures in terms of multiple clients. The research indicates that FL could be a useful approach for creating efficient and private NIDS solutions.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Sahni, Vikas UNSPECIFIED
Uncontrolled Keywords:	Federated Learning; NIDS (Network intrusion Detection System); GRU; Flower
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:	Ciara O'Brien
Date Deposited:	29 Jul 2025 10:02
Last Modified:	29 Jul 2025 10:02
URI:	https://norma.ncirl.ie/id/eprint/8289

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