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

^*Corresponding author for this work

Faculty of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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.

Original language	English
Title of host publication	Technology, Sustainability and Educational Innovation, TSIE 2019
Editors	Andrea Basantes-Andrade, Miguel Naranjo-Toro, Marcelo Zambrano Vizuete, Miguel Botto-Tobar
Publisher	Springer
Pages	231-243
Number of pages	13
ISBN (Print)	9783030372200
DOIs	https://doi.org/10.1007/978-3-030-37221-7_20
State	Published - 2020
Event	International Conference on Knowledge Society: Technology, Sustainability and Educational Innovation, TSIE 2019 - Ibarra, Ecuador Duration: 3 Jul 2019 → 5 Jul 2019

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1110 AISC
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	International Conference on Knowledge Society: Technology, Sustainability and Educational Innovation, TSIE 2019
Country/Territory	Ecuador
City	Ibarra
Period	3/07/19 → 5/07/19

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

Keywords

Control layer
Data layer
DDoS attacks
Network security
OpenFlow
SDN

Access to Document

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

Cite this

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. In 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.",

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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",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; International Conference on Knowledge Society: Technology, Sustainability and Educational Innovation, TSIE 2019 ; Conference date: 03-07-2019 Through 05-07-2019",

year = "2020",

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

language = "English",

isbn = "9783030372200",

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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. in 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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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KW - Data layer

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BT - Technology, Sustainability and Educational Innovation, TSIE 2019

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A2 - Naranjo-Toro, Miguel

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ER -

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. In Basantes-Andrade A, Naranjo-Toro M, Zambrano Vizuete M, Botto-Tobar M, editors, 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

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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Cite this