Machine Learning in an SDN Network Environment for DoS Attacks

Mauricio Dominguez-Limaico, Edgar Maya-Olalla, Carlos Bosmediano-Cardenas, Charles Escobar-Teran, Juan Francisco Chafla-Altamirano, Arturo Bedón-Chamorro

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Denial of service (DoS) attacks in Software-Defined Network (SDN) environments are increasing despite the capabilities and benefits of SDN. Software-based traffic analysis, centralized control and automatic information forwarding offered by SDN, makes it easier to detect and react effectively to DoS attacks; however, the security of the SDN itself has not yet been resolved, and there are a number of potential vulnerabilities not only of the DoS type, on the SDN platforms. In this document, we review some applications and defense mechanisms to mitigate these types of attacks in an SDN network environment. This work could help us to understand the advantages of SDNs compared to current network architectures, without leaving aside the security issue that will continue to be maintained over the years.

Idioma originalInglés
Título de la publicación alojadaTechnology, Sustainability and Educational Innovation, TSIE 2019
EditoresAndrea Basantes-Andrade, Miguel Naranjo-Toro, Marcelo Zambrano Vizuete, Miguel Botto-Tobar
Número de páginas13
ISBN (versión impresa)9783030372200
EstadoPublicada - 2020
EventoInternational Conference on Knowledge Society: Technology, Sustainability and Educational Innovation, TSIE 2019 - Ibarra, Ecuador
Duración: 3 jul. 20195 jul. 2019

Serie de la publicación

NombreAdvances in Intelligent Systems and Computing
Volumen1110 AISC
ISSN (versión impresa)2194-5357
ISSN (versión digital)2194-5365


ConferenciaInternational Conference on Knowledge Society: Technology, Sustainability and Educational Innovation, TSIE 2019

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© Springer Nature Switzerland AG 2020.

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