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Shrew Attack in Cloud Data Center Networks

Authors: Zhenqian Feng; Bing Bai; Baokang Zhao; Jinshu Su;

Shrew Attack in Cloud Data Center Networks

Abstract

Multi-tenancy and lack of network performance isolation among tenants together make the public cloud vulnerable to attacks. This paper studies one of the potential attacks, namely, low-rate denial-of-service (DoS) attack (or \textit{Shrew} attack for short), in cloud data center networks (DCNs). To explore the feasibility of launching Shrew attack from the perspective of a normal external tenant, we first leverage a loss-based probe to identify the locations and capabilities of the underlying bottlenecks, and then make use of the low-latency feature of DCNs to synchronize the participating attack flows. Moreover, we quantitatively analyze the necessary and sufficient traffic for an effective attack. Using a combination of analytical modeling and extensive experiments, we demonstrate that a tenant could initiate an efficient Shrew attack with extremely little traffic, e.g., milliseconds-long burst traffic, which imposes significant difficulty for the switching boxes and counter-DoS mechanisms to detect. We identify that both the conventional protocol assumption and new features of DCNs enable such Shrew attack, and new techniques are required to thwart it in the DCNs.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Top 10%
Average
Average
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