Adaptive Discrimination Detection for DDoS Attacks from Flash Crowds Using Flow Correlation Coefficient with Collective Feedback
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2014, Vol 16, Issue 1
Abstract
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 flows are usually more similar to each other compared to the flows of flash crowds In this paper we are concentrating flash crowd 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 by using Flow Correlation Coefficient (FCC). By using this FCC value, algorithmproposedcalled Adaptive discrimination algorithm is used to detect the DDoS from the flash crowd event. And a sequential d etection and packing algorithm used to detect the attacked packets and filter it out.By using above mentioned algorithms we can improve the accuracy in filtering the attacked packets and also the time consummation is reduced.
Authors and Affiliations
N. V. Poorrnima
Keywords
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