Detection of DDOS Attacks Using Machine Learning Techniques: A Hybrid Approach

Abstract

The essential advantage of cloud computing is that it flexibly scales to fulfill various needs and it provides the sufficient environment that scales up and downsizes quickly as indicated by the interest, so it needs incredible security from DDoS attack to handle vacation impacts of DDoS attacks. Circulate DoS assaults fall on the classification of basic assaults that bargain the accessibility of the system. These assaults have gotten refined and keep on developing at a quick pace so to identify and to take down these assaults have had become a difficult undertaking. In this project, we have designed a hybrid algorithm which consists of a combination of several machine learning techniques to train a model which can be used to detect and classify the type of DDoS attack with greater accuracy than that of each individual machine learning technique used in the hybrid model.