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2021 | OriginalPaper | Chapter

An HTTP DDoS Detection Model Using Machine Learning Techniques for the Cloud Environment

Authors : N. Muraleedharan, B. Janet

Published in: Advances in Computing and Network Communications

Publisher: Springer Singapore

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Abstract

The cloud computing platform has been evolved as an essential computing paradigm for today's world. As the cloud environment mainly focuses on the service model, to ensure the availability of these services to the intended user is an essential requirement. In this paper, an HTTP DDoS detection model for the cloud environment is presented. The proposed system uses machine learning-based classifiers on network flow data. Four tree-based classifiers, i.e., decision tree, random forest, XGBoost, and AdaBoost are applied to the identified parameters. The CIDDS-001 dataset were used for training and evaluation. Results obtained show that the proposed classifier can achieve 99.99% accuracy using the random forest classifier. Comparing the obtained results with the recent works available in the literature shows the proposed model outperforms it in the classification accuracy.

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Metadata
Title
An HTTP DDoS Detection Model Using Machine Learning Techniques for the Cloud Environment
Authors
N. Muraleedharan
B. Janet
Copyright Year
2021
Publisher
Springer Singapore
Book
Advances in Computing and Network Communications

Print ISBN: 978-981-336-976-4

Electronic ISBN: 978-981-336-977-1

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-33-6977-1_50