ML-Based DDoS Detection and Identification Using Native Cloud Telemetry Macroscopic Monitoring-Reference-Cited by-同舟云学术

ML-Based DDoS Detection and Identification Using Native Cloud Telemetry Macroscopic Monitoring

Published:2021-01-20 Issue:2 Volume:29 Page:
ISSN:1064-7570
Container-title:Journal of Network and Systems Management
language:en
Short-container-title:J Netw Syst Manage

Author:

Corrêa João Henrique^ORCID,Ciarelli Patrick M.,Ribeiro Moises R. N.,Villaça Rodolfo S.

Funder

Coordenação de Aperfeimento de Pessoal de Nível Superior

Fundação Estadual de Amparo à Pesquisa do Estado do Espírito Santo

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Springer Science and Business Media LLC

Subject

Strategy and Management,Computer Networks and Communications,Hardware and Architecture,Information Systems

Link

http://link.springer.com/content/pdf/10.1007/s10922-020-09578-1.pdf

Reference36 articles.

1. Verisign: what is a ddos attack? https://blog.verisign.com/security/ddos-protection/q2-2018-ddos-trends-report-52-percent-of-attacks-employed-multiple-attack-types/, (2018). Accessed 11 March 2019

2. Arkko, J.: Centralised architectures in internet infrastructure. Internet-Draft draft-arkko-arch-infrastructure-centralisation-00, Internet Engineering Task Force, (2019). Work in Progress

3. Bhardwaj, K., Miranda, J.C., Gavrilovska, A.: Towards iot-ddos prevention using edge computing. In: USENIX Workshop on Hot Topics in Edge Computing (HotEdge 18). USENIX Association, Boston (2018)

4. Doshi, R., Apthorpe, N., Feamster, N.: Machine learning ddos detection for consumer internet of things devices. In: 2018 IEEE Security and Privacy Workshops (SPW), pp. 29–35 (2018)

5. SNORT Team Project. Snort users manual (2020)

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