F-NIDS Sistema de Detecção de Intrusão Baseado em Aprendizado Federado

• Jonathas Alves de Oliveira UnB http://orcid.org/0009-0006-8034-4795
• Vinícius P. Gonçalves UnB
• Geraldo P. Rocha Filho UESB https://orcid.org/0000-0001-6795-2768

DOI: https://doi.org/10.5753/sbrc_estendido.2025.6896

Abstract

In this dissertation, we address the challenges of scalability and security faced by Network Intrusion Detection Systems (NIDS) in IoT scenarios, particularly those arising from privacy concerns. To overcome these challenges, we propose the F-NIDS (Federated Network Intrusion Detection System), a system designed based on Federated Learning (FL), integrating asynchronous communication techniques and Differential Privacy (DP). Additionally, three strategies were developed to balance performance and privacy. The results demonstrate that F-NIDS achieves high robustness and performance, with superior detection values in distributed scenarios. The parameter adjustment strategy allowed the identification of the optimal level of Gaussian noise, ensuring privacy preservation without compromising accuracy. Therefore, F-NIDS represents a significant advancement in the state of the art, combining scalability, security, and privacy in distributed systems, reaffirming its effectiveness and potential to redefine intrusion detection standards in IoT environments.

Keywords: Big data and machine learning applications for computer networks and distributed systems Network, distributed systems security

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