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A Prevention Model for Algorithmic Complexity Attacks

Authors: Suraiya Khan; Issa Traoré;

A Prevention Model for Algorithmic Complexity Attacks

Abstract

Denial of Service (DoS) attack has been identified in security surveys as the second largest cause of monetary loss. Hence, DoS is a very important problem that needs to be dealt with seriously. Many DoS attacks are conducted by generating extremely high rate traffic; these are classified as flooding attacks. Other DoS attacks, which are caused by resource consumption, belong to the so-called logic attacks category, one such example is algorithmic complexity attack. Complexity attacks generate traffic containing data, which exploits the working principle of the algorithms running on a machine. In such an attack, a request imposes worst-case execution time on a resource and repeatedly re-uses the same resource for further services. In this paper, we propose a regression analysis based model that can prevent algorithmic complexity attacks. We demonstrate our model on quick-sort algorithm.

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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!
3
Average
Average
Average
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