A Fuzzy Intrusion Detection System for Identifying Cyber-Attacks on IoT Networks

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorFed Inst Sao Paulo IFSP-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.creatorCristiani, Andre L.-
Autor(es): dc.creatorLieira, Douglas D.-
Autor(es): dc.creatorMeneguette, Rodolfo I.-
Autor(es): dc.creatorCamargo, Heloisa A.-
Autor(es): dc.creatorVelazquez, R.-
Data de aceite: dc.date.accessioned2025-08-21T18:59:30Z-
Data de disponibilização: dc.date.available2025-08-21T18:59:30Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2019-12-31-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/245190-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/245190-
Descrição: dc.descriptionThe Internet of Things (IoT) is increasingly present in our daily activities, connecting the most varied types of physical devices present around us to the internet. IoT is the basis for smart cities, e-health, precision agriculture, among others. With this growth, the number of cyber-attacks against these types of devices and services has also increased. Each type of attack has its specific characteristics that allow its identification and prevention through machine learning techniques. However, classic machine learning techniques may have their performance compromised due to the non-stationary characteristics of these environments, together with the search for different types of vulnerabilities by attackers, attacks can suffer different types of mutations, in addition to the great possibility of new types of attacks arising over time. In this article, we propose an algorithm called Fuzzy Intrusion Detection System for IoT Networks (FROST) to identify cyber-attacks on IoT networks. FROST uses the concepts of fuzzy set theory to make the learning task more flexible, seeking to improve the performance in the classification of inaccurate data. In addition, FROST has a mechanism for identifying new types of intrusion online, during the classification of new instances. To evaluate our approach, we used the UNSW-NB15 data set and compared our method with another approach, very consolidated in the literature, which performs the same type of task. The results showed that FROST has a good performance in the classification of different types of attacks and that the fuzzy technique used helped to reduce errors and identify anomalies.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoa de Nível Superior (CAPES)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionFed Univ Sao Carlos UFSCar, Sao Carlos, SP, Brazil-
Descrição: dc.descriptionSao Paulo State Univ UNESP, Sao Jose Do Rio Preto, SP, Brazil-
Descrição: dc.descriptionFed Inst Sao Paulo IFSP, Catanduva, SP, Brazil-
Descrição: dc.descriptionUniv Sao Paulo, Sao Carlos, SP, Brazil-
Descrição: dc.descriptionSao Paulo State Univ UNESP, Sao Jose Do Rio Preto, SP, Brazil-
Descrição: dc.descriptionCAPES: 001-
Descrição: dc.descriptionCNPq: 407248/2018-8-
Descrição: dc.descriptionCNPq: 309822/2018-1-
Formato: dc.format6-
Idioma: dc.languageen-
Publicador: dc.publisherIeee-
Relação: dc.relation2020 Ieee Latin-american Conference On Communications (latincom 2020)-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectInternet of Things-
Palavras-chave: dc.subjectcyber-attacks-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectfuzzy-
Título: dc.titleA Fuzzy Intrusion Detection System for Identifying Cyber-Attacks on IoT Networks-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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