Privacy preserving poisoning attack detection in federated learning using CKKS homomorphic encryption & hybrid approach

Katale, Ramani Tukaram (2025) Privacy preserving poisoning attack detection in federated learning using CKKS homomorphic encryption & hybrid approach. Masters thesis, Dublin, National College of Ireland.

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Abstract

The field of federated learning (FL) has seen growing adoption because it provides privacy-protected methods for training machine learning models across distributed datasets. FL systems are exposed to poisoning attacks because malicious clients use manipulated model updates to harm system performance while compromising security. The study introduces a privacy- preserving approach to detect poisoning attacks inside federated learning through Intrusion Detection Systems (IDS). It utilize homomorphic encryption to protect privacy during model updates and using a combine technique that is apply autoencoders for anomaly detection, with clustering techniques to identify outliers indicative of malicious activity. The goal is to create an encryption based security system which protects federation learning process while efficiently identifying attack attempts in diverse network environment. This research fills a gap in existing methods by integrating privacy-preserving techniques with advanced anomaly detection to safeguard IDS models in federated settings.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Aleburu, Joel UNSPECIFIED
Uncontrolled Keywords:	Federated Learning; Intrusion Detection System; Poisoning Attacks; Homomorphic Encryption; CKKS; Autoencoder; Clustering; Privacy Preservation
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:	17 Aug 2026 15:04
Last Modified:	17 Aug 2026 15:04
URI:	https://norma.ncirl.ie/id/eprint/9536

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