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DDoS attack on cloud auto-scaling mechanisms

Authors: Anat Bremler-Barr; Eli Brosh; Mor Sides;

DDoS attack on cloud auto-scaling mechanisms

Abstract

Auto-scaling mechanisms are an important line of defense against Distributed Denial of Service (DDoS) in the cloud. Using auto-scaling, machines can be added and removed in an on-line manner to respond to fluctuating load. It is commonly believed that the auto-scaling mechanism casts DDoS attacks into Economic Denial of Sustainability (EDoS) attacks. Rather than suffering from performance degradation up to a total denial of service, the victim suffers only from the economic damage incurred by paying for the extra resources required to process the bogus traffic of the attack. Contrary to this belief, we present and analyze the Yo-Yo attack, a new attack against the auto-scaling mechanism, that can cause significant performance degradation in addition to economic damage. In the Yo-Yo attack, the attacker sends periodic bursts of overload, thus causing the auto-scaling mechanism to oscillate between scale-up and scale-down phases. The Yo-Yo attack is harder to detect and requires less resources from the attacker compared to traditional DDoS. We demonstrate the attack on Amazon EC2 [4], and analyze protection measures the victim can take by reconfiguring the auto-scaling mechanism.

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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!
34
Top 10%
Top 10%
Top 10%
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