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

doi:10.1007/978-3-030-37221-7_20

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

Facultad de Ingeniería

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

1 Cita (Scopus)

Resumen

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 original	Inglés
Título de la publicación alojada	Technology, Sustainability and Educational Innovation, TSIE 2019
Editores	Andrea Basantes-Andrade, Miguel Naranjo-Toro, Marcelo Zambrano Vizuete, Miguel Botto-Tobar
Editorial	Springer
Páginas	231-243
Número de páginas	13
ISBN (versión impresa)	9783030372200
DOI	https://doi.org/10.1007/978-3-030-37221-7_20
Estado	Publicada - 2020
Evento	International Conference on Knowledge Society: Technology, Sustainability and Educational Innovation, TSIE 2019 - Ibarra, Ecuador Duración: 3 jul. 2019 → 5 jul. 2019

Serie de la publicación

Nombre	Advances in Intelligent Systems and Computing
Volumen	1110 AISC
ISSN (versión impresa)	2194-5357
ISSN (versión digital)	2194-5365

Conferencia

Conferencia	International Conference on Knowledge Society: Technology, Sustainability and Educational Innovation, TSIE 2019
País/Territorio	Ecuador
Ciudad	Ibarra
Período	3/07/19 → 5/07/19

Nota bibliográfica

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

Acceder al documento

10.1007/978-3-030-37221-7_20

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Dominguez-Limaico, M., Maya-Olalla, E., Bosmediano-Cardenas, C., Escobar-Teran, C., Chafla-Altamirano, J. F., & Bedón-Chamorro, A. (2020). Machine Learning in an SDN Network Environment for DoS Attacks. En A. Basantes-Andrade, M. Naranjo-Toro, M. Zambrano Vizuete, & M. Botto-Tobar (Eds.), Technology, Sustainability and Educational Innovation, TSIE 2019 (pp. 231-243). (Advances in Intelligent Systems and Computing; Vol. 1110 AISC). Springer. https://doi.org/10.1007/978-3-030-37221-7_20

Dominguez-Limaico, Mauricio ; Maya-Olalla, Edgar ; Bosmediano-Cardenas, Carlos et al. / Machine Learning in an SDN Network Environment for DoS Attacks. Technology, Sustainability and Educational Innovation, TSIE 2019. editor / Andrea Basantes-Andrade ; Miguel Naranjo-Toro ; Marcelo Zambrano Vizuete ; Miguel Botto-Tobar. Springer, 2020. pp. 231-243 (Advances in Intelligent Systems and Computing).

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title = "Machine Learning in an SDN Network Environment for DoS Attacks",

abstract = "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.",

keywords = "Control layer, Data layer, DDoS attacks, Network security, OpenFlow, SDN",

author = "Mauricio Dominguez-Limaico and Edgar Maya-Olalla and Carlos Bosmediano-Cardenas and Charles Escobar-Teran and Chafla-Altamirano, {Juan Francisco} and Arturo Bed{\'o}n-Chamorro",

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year = "2020",

doi = "10.1007/978-3-030-37221-7_20",

language = "English",

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Dominguez-Limaico, M, Maya-Olalla, E, Bosmediano-Cardenas, C, Escobar-Teran, C , Chafla-Altamirano, JF & Bedón-Chamorro, A 2020, Machine Learning in an SDN Network Environment for DoS Attacks. En A Basantes-Andrade, M Naranjo-Toro, M Zambrano Vizuete & M Botto-Tobar (eds.), Technology, Sustainability and Educational Innovation, TSIE 2019. Advances in Intelligent Systems and Computing, vol. 1110 AISC, Springer, pp. 231-243, International Conference on Knowledge Society: Technology, Sustainability and Educational Innovation, TSIE 2019, Ibarra, Ecuador, 3/07/19. https://doi.org/10.1007/978-3-030-37221-7_20

Machine Learning in an SDN Network Environment for DoS Attacks. / Dominguez-Limaico, Mauricio; Maya-Olalla, Edgar; Bosmediano-Cardenas, Carlos et al.
Technology, Sustainability and Educational Innovation, TSIE 2019. ed. / Andrea Basantes-Andrade; Miguel Naranjo-Toro; Marcelo Zambrano Vizuete; Miguel Botto-Tobar. Springer, 2020. p. 231-243 (Advances in Intelligent Systems and Computing; Vol. 1110 AISC).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

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T1 - Machine Learning in an SDN Network Environment for DoS Attacks

AU - Dominguez-Limaico, Mauricio

AU - Maya-Olalla, Edgar

AU - Bosmediano-Cardenas, Carlos

AU - Escobar-Teran, Charles

AU - Chafla-Altamirano, Juan Francisco

AU - Bedón-Chamorro, Arturo

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2020.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

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A2 - Naranjo-Toro, Miguel

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Dominguez-Limaico M, Maya-Olalla E, Bosmediano-Cardenas C, Escobar-Teran C , Chafla-Altamirano JF, Bedón-Chamorro A. Machine Learning in an SDN Network Environment for DoS Attacks. En Basantes-Andrade A, Naranjo-Toro M, Zambrano Vizuete M, Botto-Tobar M, editores, Technology, Sustainability and Educational Innovation, TSIE 2019. Springer. 2020. p. 231-243. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-37221-7_20

Machine Learning in an SDN Network Environment for DoS Attacks

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