Assessing SDN Controller Vulnerabilities: A Survey on Attack Typologies, Detection Mechanisms, Controller Selection, and Dataset Application in Machine Learning: Assessing SDN Controller Vulnerabilities...: J. Arevalo-Herrera et al.
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| Title: | Assessing SDN Controller Vulnerabilities: A Survey on Attack Typologies, Detection Mechanisms, Controller Selection, and Dataset Application in Machine Learning: Assessing SDN Controller Vulnerabilities...: J. Arevalo-Herrera et al. |
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| Authors: | Arevalo-Herrera, Juliana1,2 (AUTHOR) j.arevaloh.2019@alumnos.urjc.es, Camargo Mendoza, Jorge3 (AUTHOR) jecamargom@unal.edu.co, Martínez Torre, Jose Ignacio1 (AUTHOR) joseignacio.martinez@urjc.es, Zona-Ortiz, Tatiana2 (AUTHOR) angelazona@usta.edu.co, Ramirez, Juan M.4 (AUTHOR) juan.ramirez@imdea.org |
| Source: | Wireless Personal Communications. Jan2025, Vol. 140 Issue 1, p739-775. 37p. |
| Subjects: | OpenFlow (Computer network protocol), Statistical learning, Artificial intelligence, Machine learning, Mathematical statistics |
| Abstract: | SDN controllers become the main advantage of the architecture because they present a centralized control decision-making and general view of the network. They are, however, also a critical point that an attacker could exploit. More review of the body of research is needed regarding the types of attacks on SDN controllers, methods to detect them, and mitigation techniques directed specifically to the controller, particularly considering the approach of machine learning detection methods. This survey addresses the topics of attacks targeting the SDN controller, methods for their detection, what types of controllers are used in different studies, and datasets used in machine learning detection methods. The findings highlight that most attacks exploit vulnerabilities inherent in the OpenFlow protocol, while the detection methodologies remain primarily statistical and machine learning approaches. Additionally, the review shows that while outdated controllers like Floodlight and Ryu are still widely used in studies, actively supported controllers such as ONOS and ODL are used much less. Finally, the survey finds only two publicly available datasets tailored for SDN environments, none considering attacks directed at the controllers, illustrating a notable gap in the existing research. This survey also highlights the need for further research focusing on modern SDN controllers and developing comprehensive datasets to advance effective security solutions. [ABSTRACT FROM AUTHOR] |
| Copyright of Wireless Personal Communications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: Assessing SDN Controller Vulnerabilities: A Survey on Attack Typologies, Detection Mechanisms, Controller Selection, and Dataset Application in Machine Learning: Assessing SDN Controller Vulnerabilities...: J. Arevalo-Herrera et al. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Arevalo-Herrera%2C+Juliana%22">Arevalo-Herrera, Juliana</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> j.arevaloh.2019@alumnos.urjc.es</i><br /><searchLink fieldCode="AR" term="%22Camargo+Mendoza%2C+Jorge%22">Camargo Mendoza, Jorge</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> jecamargom@unal.edu.co</i><br /><searchLink fieldCode="AR" term="%22Martínez+Torre%2C+Jose+Ignacio%22">Martínez Torre, Jose Ignacio</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> joseignacio.martinez@urjc.es</i><br /><searchLink fieldCode="AR" term="%22Zona-Ortiz%2C+Tatiana%22">Zona-Ortiz, Tatiana</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> angelazona@usta.edu.co</i><br /><searchLink fieldCode="AR" term="%22Ramirez%2C+Juan+M%2E%22">Ramirez, Juan M.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> juan.ramirez@imdea.org</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Wireless+Personal+Communications%22">Wireless Personal Communications</searchLink>. Jan2025, Vol. 140 Issue 1, p739-775. 37p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22OpenFlow+%28Computer+network+protocol%29%22">OpenFlow (Computer network protocol)</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+learning%22">Statistical learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+statistics%22">Mathematical statistics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: SDN controllers become the main advantage of the architecture because they present a centralized control decision-making and general view of the network. They are, however, also a critical point that an attacker could exploit. More review of the body of research is needed regarding the types of attacks on SDN controllers, methods to detect them, and mitigation techniques directed specifically to the controller, particularly considering the approach of machine learning detection methods. This survey addresses the topics of attacks targeting the SDN controller, methods for their detection, what types of controllers are used in different studies, and datasets used in machine learning detection methods. The findings highlight that most attacks exploit vulnerabilities inherent in the OpenFlow protocol, while the detection methodologies remain primarily statistical and machine learning approaches. Additionally, the review shows that while outdated controllers like Floodlight and Ryu are still widely used in studies, actively supported controllers such as ONOS and ODL are used much less. Finally, the survey finds only two publicly available datasets tailored for SDN environments, none considering attacks directed at the controllers, illustrating a notable gap in the existing research. This survey also highlights the need for further research focusing on modern SDN controllers and developing comprehensive datasets to advance effective security solutions. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Wireless Personal Communications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11277-025-11748-w Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 37 StartPage: 739 Subjects: – SubjectFull: OpenFlow (Computer network protocol) Type: general – SubjectFull: Statistical learning Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Mathematical statistics Type: general Titles: – TitleFull: Assessing SDN Controller Vulnerabilities: A Survey on Attack Typologies, Detection Mechanisms, Controller Selection, and Dataset Application in Machine Learning: Assessing SDN Controller Vulnerabilities...: J. Arevalo-Herrera et al. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Arevalo-Herrera, Juliana – PersonEntity: Name: NameFull: Camargo Mendoza, Jorge – PersonEntity: Name: NameFull: Martínez Torre, Jose Ignacio – PersonEntity: Name: NameFull: Zona-Ortiz, Tatiana – PersonEntity: Name: NameFull: Ramirez, Juan M. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09296212 Numbering: – Type: volume Value: 140 – Type: issue Value: 1 Titles: – TitleFull: Wireless Personal Communications Type: main |
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