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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. Secon d we ha ve t o di fferentiat e fl ash crowd and DDoS b y usin g Fl ow Correl ati on Coeffi ci ent (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 equ en t i al det e ct i on and pac ki n g al gori t h m u sed t o d et ect t he at t a cked pa ck et s and fi l t er i t out .By using above mentioned algorithms we can improve the accuracy in filtering the attacked packets and also the time consummation is reduced. KEYWORDS—DDoS attacks, flashcrowd event, discrimination.

Adaptive Discrimination Detection for DDoS Attacks from Flash Crowds Using Flow Correlation Coefficient with Collective Feedback