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Protect E-Commerce against DDoS Attacks with Improved D-WARD Detection System

Authors: Jian Kang 0001; Zhe Zhang; Jiu-bin Ju;

Protect E-Commerce against DDoS Attacks with Improved D-WARD Detection System

Abstract

DDoS attack has caused severe damage to e-commerce and e-service and is great intimidation to the development of them. After analyzing the detection algorithm of D-WARD, a representative source-end DDoS detection system, we introduced a nonparametric change point detection method in statistics and improved D-WARD with nonparametric recursive CUSUM algorithm and compared their respective performance. Experiments proved that the improved system is lower in false-positive rate and false-negative rate, which is more accurate and could adapt to more complex network environments. This improved system could be utilized in the protection of e-commerce against DDoS attacks.

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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!
5
Average
Top 10%
Average
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