Real-time application of OPF-based classifier in Snort IDS

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorCzestochowa University of Technology-
Autor(es): dc.creatorUtimura, Luan-
Autor(es): dc.creatorCosta, Kelton-
Autor(es): dc.creatorScherer, Rafał-
Data de aceite: dc.date.accessioned2025-08-21T21:10:37Z-
Data de disponibilização: dc.date.available2025-08-21T21:10:37Z-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2022-01-23-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/B978-0-12-822688-9.00011-6-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/240550-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/240550-
Descrição: dc.descriptionAs the internet grows over the years, it is possible to observe an increase in the amount of data that travels on computer networks around the world. In a context in which the volume of data is continuously being renewed, from the perspective of the Computer Network Security area, it becomes a great challenge to protect, in terms of effectiveness and efficiency, today's computer systems. Among the primary security mechanisms employed in these environments, the Network Intrusion Detection Systems stand out. Although the signature-based detection approach of these tools is sufficient to combat known attacks, with the eventual discovery of new vulnerabilities, it is necessary to use anomaly-based detection approaches to mitigate the damage of unknown attacks. In the academic field, several studies have explored the development of hybrid approaches to improve the accuracy of these tools, with the aid of machine learning techniques. In this same line of research, this chapter aims at the application of these techniques for intrusion detection in a real-time environment using a popular and widely utilized tool, the Snort IDS. The presented results show that in certain attack scenarios, the anomaly-based detection approach can outperform the signature-based detection approach, with emphasis on the optimum-path forest, AdaBoost, Random Forests, decision tree, and support vector machine techniques. © 2022 Copyright-
Descrição: dc.descriptionSão Paulo State University Department of Computing-
Descrição: dc.descriptionCzestochowa University of Technology Department of Computing-
Descrição: dc.descriptionSão Paulo State University Department of Computing-
Formato: dc.format55-93-
Idioma: dc.languageen-
Relação: dc.relationOptimum-Path Forest: Theory, Algorithms, and Applications-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectAnomaly detection-
Palavras-chave: dc.subjectIntrusion detection systems-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectOPF-
Palavras-chave: dc.subjectSnort-
Título: dc.titleReal-time application of OPF-based classifier in Snort IDS-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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