alexa Adaptive Discriminating Detection for DDoS Attacks fro
ISSN ONLINE(2320-9801) PRINT (2320-9798)

International Journal of Innovative Research in Computer and Communication Engineering
Open Access

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Special Issue Article

Adaptive Discriminating Detection for DDoS Attacks from Flash Crowds Using Flow Correlation Coe f f ic i ent with Collective Feedback

N.V.Poorrnima, K.ChandraPrabha and B.G.Geetha
Department of Computer Science and Engineering. K.S.Rangasamy College of Technology, Tamilnadu, India
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A Distributed denial of service (DDoS) attack is a most popular and crucial attack in the internet. Its motive is to make a network resource unavailable to the legitimate users. Botnets are commonly the engines behind the attack. In our deep study of the size and organization of current botnets, found that the current attack flowsare usually more similar to each other compared to the flows of flashcrowds In this paper we are concentrating flashcrowd and DDoS there are two steps involved, first it is necessary to differentiate normal traffic and flashcrowd by using Flash Crowd Detection Algorithm. Second we have to differentiate flash crowd and DDoS b y using Flow Correlation Coefficient (FCC). By using this FCC value, algorithm proposed called Adaptive discrimination algorithm is used to detect the DDoS from the flash crowd event. And a s equenti al dete ction and packing al gorithm used t o det ect t he atta cked pa ckets and filt er it out .By using above mentioned algorithms we can improve the accuracy in filtering the attacked packets and also the time consummation is reduced.


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