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Detection of DDOS Attacks Using Machine Learning Techniques: A Hybrid Approach
D.A. Varma, R. Ashish, V. Venkata Sai Sandeep, B. Venkatesh,
Published in Springer Science and Business Media Deutschland GmbH
Volume: 1270
Pages: 439 - 446
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. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About the journal
JournalData powered by TypesetAdvances in Intelligent Systems and Computing
PublisherData powered by TypesetSpringer Science and Business Media Deutschland GmbH