F-NIDS : a network intrusion detection system based on federated learning

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

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Campo DC	Valor	Idioma
dc.contributor.author	Oliveira, Jonathas Alves de	-
dc.contributor.author	Gonçalves, Vinícius Pereira	-
dc.contributor.author	Meneguette, Rodolfo I.	-
dc.contributor.author	Sousa Júnior, Rafael Timóteo de	-
dc.contributor.author	Guidoni, Daniel L.	-
dc.contributor.author	Oliveira, José Carlos Martins	-
dc.contributor.author	Rocha Filho, Geraldo Pereira	-
dc.date.accessioned	2024-03-21T11:49:08Z	-
dc.date.available	2024-03-21T11:49:08Z	-
dc.date.issued	2023	-
dc.identifier.citation	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.	pt_BR
dc.identifier.uri	http://repositorio2.unb.br/jspui/handle/10482/48015	-
dc.language.iso	por	pt_BR
dc.publisher	Elsevier B.V.	pt_BR
dc.rights	Acesso Restrito	pt_BR
dc.title	F-NIDS : a network intrusion detection system based on federated learning	pt_BR
dc.type	Artigo	pt_BR
dc.subject.keyword	Sistema de detecção de intrusão (IDS)	pt_BR
dc.subject.keyword	Sistemas de segurança	pt_BR
dc.subject.keyword	Computação	pt_BR
dc.subject.keyword	Aprendizado de máquina	-
dc.identifier.doi	https://doi.org/10.1016/j.comnet.2023.110010	pt_BR
dc.description.abstract1	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.	pt_BR
dc.identifier.orcid	https://orcid.org/0009-0006-8034-4795	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0002-3771-2605	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0003-2982-4006	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0003-1101-3029	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0002-1953-9132	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0003-2148-4502	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0001-6795-2768	pt_BR
dc.contributor.affiliation	University of Brasilía, Department of Electrical Engineering	pt_BR
dc.contributor.affiliation	University of Brasilía, Department of Electrical Engineering	pt_BR
dc.contributor.affiliation	University of São Paulo, Department of Computing System	pt_BR
dc.contributor.affiliation	University of Brasilía, Department of Electrical Engineering	pt_BR
dc.contributor.affiliation	Federal University of Ouro Preto, Department of Computer Science	pt_BR
dc.contributor.affiliation	State University of Southwest Bahia, Department of Exact and Technological Sciences	pt_BR
dc.contributor.affiliation	State University of Southwest Bahia, Department of Exact and Technological Sciences	pt_BR
dc.description.unidade	Faculdade de Tecnologia (FT)	pt_BR
dc.description.unidade	Departamento de Engenharia Elétrica (FT ENE)	pt_BR
dc.description.ppg	Programa de Pós-Graduação em Engenharia Elétrica	pt_BR
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