Machine Learning in an SDN Network Environment for DoS Attacks-Reference-Cited by-同舟云学术

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Machine Learning in an SDN Network Environment for DoS Attacks

Published:2020 Issue: Volume: Page:231-243
ISSN:2194-5357
Container-title:Technology, Sustainability and Educational Innovation (TSIE)
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Author:

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

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-37221-7_20

Reference28 articles.

1. Hong, K., Kim, Y., Choi, H., Park, J.: SDN-assisted slow HTTP DDoS attack defense method. IEEE Commun. Lett. 22(4), 688–691 (2018)

2. Alharbi, T., Portmann, M., Pakzad, F.: The (in)security of topology discovery in software defined networks. In: Proceedings of the Conference on Local Computer Networks (LCN), vol. 2629, pp. 502–505, Oct 2015

3. Bjorn, M.: Software Defined Networks (SDN) and its relevance in the DC, pp. 143 (2013)

4. Yan, Q., Yu, F.R., Gong, Q., Li, J.: Software-defined networking (SDN) and distributed denial of service (DDOS) attacks in cloud computing environments: a survey, some research issues, and challenges. IEEE Commun. Surv. Tutorials 18(1), 602–622 (2016)

5. Abdulqadder, I.H., Zou, D., Aziz, I.T., Yuan, B., Li, W.: SecSDN-cloud: defeating vulnerable attacks through secure software-defined networks. IEEE Access 6, 8292–8301 (2018)

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1. Improved Network Traffic by Attacking Denial of Service to Protect Resource Using Z-Test Based 4-Tier Geomark Traceback (Z4TGT);Wireless Personal Communications;2020-06-11

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