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Cluster Computing
Erschienen in: Cluster Computing 2/2021

18.09.2020

A survey and classification of the security anomaly detection mechanisms in software defined networks

verfasst von: Tohid Jafarian, Mohammad Masdari, Ali Ghaffari, Kambiz Majidzadeh

Erschienen in: Cluster Computing | Ausgabe 2/2021

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Abstract

Software defined network (SDN) decouples the network control and data planes. Despite various advantages of SDNs, they are vulnerable to various security attacks such anomalies, intrusions, and Denial-of-Service (DoS) attacks and so on. On the other hand, any anomaly and intrusion in SDNs can affect many important domains such as banking system and national security. Therefore, the anomaly detection topic is a broad research domain, and to mitigate these security problems, a great deal of research has been conducted in the literature. In this paper, the state-of-the-art schemes applied in detecting and mitigating anomalies in SDNs are explained, categorized, and compared. This paper categorizes the SDN anomaly detection mechanisms into five categories: (1) flow counting scheme, (2) information-based scheme, (3) entropy-based scheme, (4) deep learning, and (5) hybrid scheme. The research gaps and major existing research issues regarding SDN anomaly detection are highlighted. We hope that the analyses, comparisons, and classifications might provide directions for further research.
Vorheriger Artikel A regression tree predictive model for virtual machine startup time in IaaS clouds
Nächster Artikel Simultaneous application assignment and virtual machine placement via ant colony optimization for energy-efficient enterprise data centers

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Metadaten
Titel
A survey and classification of the security anomaly detection mechanisms in software defined networks
verfasst von
Tohid Jafarian
Mohammad Masdari
Ali Ghaffari
Kambiz Majidzadeh
Publikationsdatum
18.09.2020
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe 2/2021
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-020-03184-1

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