Título:	F-NIDS : a network intrusion detection system based on federated learning
Autor(es):	Oliveira, Jonathas Alves de Gonçalves, Vinícius Pereira Meneguette, Rodolfo I. Sousa Júnior, Rafael Timóteo de Guidoni, Daniel L. Oliveira, José Carlos Martins Rocha Filho, Geraldo Pereira
ORCID:	https://orcid.org/0009-0006-8034-4795 https://orcid.org/0000-0002-3771-2605 https://orcid.org/0000-0003-2982-4006 https://orcid.org/0000-0003-1101-3029 https://orcid.org/0000-0002-1953-9132 https://orcid.org/0000-0003-2148-4502 https://orcid.org/0000-0001-6795-2768
Afiliação do autor:	University of Brasilía, Department of Electrical Engineering University of Brasilía, Department of Electrical Engineering University of São Paulo, Department of Computing System University of Brasilía, Department of Electrical Engineering Federal University of Ouro Preto, Department of Computer Science State University of Southwest Bahia, Department of Exact and Technological Sciences State University of Southwest Bahia, Department of Exact and Technological Sciences
Assunto:	Sistema de detecção de intrusão (IDS) Sistemas de segurança Computação Aprendizado de máquina
Data de publicação:	2023
Editora:	Elsevier B.V.
Referência:	OLIVEIRA, Jonathas A. de et al. F-NIDS: a network intrusion detection system based on federated learning. Computer Networks, v. 236, 110010, 2023. DOI: https://doi.org/10.1016/j.comnet.2023.110010.
Abstract:	The rise of IoT networks has presented fresh challenges in terms of scalability and security for distributed Network Intrusion Detection Systems (NIDS) due to privacy concerns. While some progress has been made in addressing these challenges, there are still unanswered questions regarding how to achieve a balance between performance and robustness to ensure privacy in a distributed manner. Additionally, there is a need to develop a reliable and scalable architecture for distributed NIDS that can be effectively deployed in various IoT scenarios. These questions about robustness relied mainly on choosing privacy-secured and distributed Machine Learning techniques. In this work, we propose the F-NIDS, an intrusion detector that utilizes federated artificial intelligence and asynchronous communication techniques between system entities to provide horizontal scalability, along with differential privacy techniques to address data confidentiality concerns. The architecture of F-NIDS is designed to be adaptable for usage in IoT networks, suited to be used in cloud or fog-based environments. Results from our experiments have shown that the confidential detection model employed in F-NIDS – considering multi-class accuracy, binary accuracy, precision, and recall metrics – was capable of predicting and determining the nature of attacks when they occur. In order to determine optimal parameters that strike a balance between data privacy and classification performance, three strategies were employed, each evaluated for its corresponding robustness performance. Firstly, models were trained with varying Gaussian noise values, and subjected to membership inference black box rule-based attacks. Secondly, regular membership inference black box attacks were performed, utilizing different stolen samples with varying sizes to determine the maximum amount of data that could be securely stored on the detection agents for training tasks. Lastly, the robustness of the trained models was evaluated against a model inversion attack, and the results were compared through graphical comparisons. Based on these evaluations, Gaussian noise level and sample size values of 21 were obtained for each detection agent in the system, with sample sizes ranging from 10K to 25K.
Unidade Acadêmica:	Faculdade de Tecnologia (FT) Departamento de Engenharia Elétrica (FT ENE)
Programa de pós-graduação:	Programa de Pós-Graduação em Engenharia Elétrica
DOI:	https://doi.org/10.1016/j.comnet.2023.110010
Aparece nas coleções:	Artigos publicados em periódicos e afins

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