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Erschienen in: Neural Computing and Applications 12/2017

13.04.2016 | Review

Taxonomy of DoS and DDoS attacks and desirable defense mechanism in a Cloud computing environment

verfasst von: B. B. Gupta, Omkar P. Badve

Erschienen in: Neural Computing and Applications | Ausgabe 12/2017

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Abstract

As Cloud computing is reforming the infrastructure of IT industries, it has become one of the critical security concerns of the defensive mechanisms applied to secure Cloud environment. Even if there are tremendous advancements in defense systems regarding the confidentiality, authentication and access control, there is still a challenge to provide security against availability of associated resources. Denial-of-service (DoS) attack and distributed denial-of-service (DDoS) attack can primarily compromise availability of the system services and can be easily started by using various tools, leading to financial damage or affecting the reputation. These attacks are very difficult to detect and filter, since packets that cause the attack are very much similar to legitimate traffic. DoS attack is considered as the biggest threat to IT industry, and intensity, size and frequency of the attack are observed to be increasing every year. Therefore, there is a need for stronger and universal method to impede these attacks. In this paper, we present an overview of DoS attack and distributed DoS attack that can be carried out in Cloud environment and possible defensive mechanisms, tools and devices. In addition, we discuss many open issues and challenges in defending Cloud environment against DoS attack. This provides better understanding of the DDoS attack problem in Cloud computing environment, current solution space, and future research scope to deal with such attacks efficiently.

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Metadaten
Titel
Taxonomy of DoS and DDoS attacks and desirable defense mechanism in a Cloud computing environment
verfasst von
B. B. Gupta
Omkar P. Badve
Publikationsdatum
13.04.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 12/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2317-5

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