Adversarial Deep Learning approach detection and defense against DDoS attacks in SDN environments

Author:

Novaes Matheus P.ORCID,Carvalho Luiz F.,Lloret JaimeORCID,Proença Mario LemesORCID

Funder

Fundação Araucária

Ministerio de Economía y Competitividad

National Council for Scientific and Technological Development

Publisher

Elsevier BV

Subject

Computer Networks and Communications,Hardware and Architecture,Software

Reference66 articles.

1. A survey on stateful data plane in software defined networks;Zhang;Comput. Netw.,2020

2. A survey on controller placement in SDN;Das;IEEE Commun. Surv. Tutor.,2020

3. IoT survey: An SDN and fog computing perspective;Salman;Comput. Netw.,2018

4. Hybrid deep-learning-based anomaly detection scheme for suspicious flow detection in SDN: A social multimedia perspective;Garg;IEEE Trans. Multimed.,2019

5. Lightweight algorithm for protecting SDN controller against DDoS attacks;Gkountis,2017

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3. A Survey on DDoS Detection Using Deep Learning in Software Defined Networking;Lecture Notes in Electrical Engineering;2023-11-30

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