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Use este identificador para citar ou linkar para este item: http://repositorio2.unb.br/jspui/handle/10482/48015
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dc.contributor.authorOliveira, Jonathas Alves de-
dc.contributor.authorGonçalves, Vinícius Pereira-
dc.contributor.authorMeneguette, Rodolfo I.-
dc.contributor.authorSousa Júnior, Rafael Timóteo de-
dc.contributor.authorGuidoni, Daniel L.-
dc.contributor.authorOliveira, José Carlos Martins-
dc.contributor.authorRocha Filho, Geraldo Pereira-
dc.date.accessioned2024-03-21T11:49:08Z-
dc.date.available2024-03-21T11:49:08Z-
dc.date.issued2023-
dc.identifier.citationOLIVEIRA, 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.urihttp://repositorio2.unb.br/jspui/handle/10482/48015-
dc.language.isoporpt_BR
dc.publisherElsevier B.V.pt_BR
dc.rightsAcesso Restritopt_BR
dc.titleF-NIDS : a network intrusion detection system based on federated learningpt_BR
dc.typeArtigopt_BR
dc.subject.keywordSistema de detecção de intrusão (IDS)pt_BR
dc.subject.keywordSistemas de segurançapt_BR
dc.subject.keywordComputaçãopt_BR
dc.subject.keywordAprendizado de máquina-
dc.identifier.doihttps://doi.org/10.1016/j.comnet.2023.110010pt_BR
dc.description.abstract1The 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.orcidhttps://orcid.org/0009-0006-8034-4795pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-3771-2605pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0003-2982-4006pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0003-1101-3029pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-1953-9132pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0003-2148-4502pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0001-6795-2768pt_BR
dc.contributor.affiliationUniversity of Brasilía, Department of Electrical Engineeringpt_BR
dc.contributor.affiliationUniversity of Brasilía, Department of Electrical Engineeringpt_BR
dc.contributor.affiliationUniversity of São Paulo, Department of Computing Systempt_BR
dc.contributor.affiliationUniversity of Brasilía, Department of Electrical Engineeringpt_BR
dc.contributor.affiliationFederal University of Ouro Preto, Department of Computer Sciencept_BR
dc.contributor.affiliationState University of Southwest Bahia, Department of Exact and Technological Sciencespt_BR
dc.contributor.affiliationState University of Southwest Bahia, Department of Exact and Technological Sciencespt_BR
dc.description.unidadeFaculdade de Tecnologia (FT)pt_BR
dc.description.unidadeDepartamento de Engenharia Elétrica (FT ENE)pt_BR
dc.description.ppgPrograma de Pós-Graduação em Engenharia Elétricapt_BR
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