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Machine Learning Methods for DDoS Attacks Detection in the Cloud Environment

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Advanced Intelligent Systems for Sustainable Development (AI2SD’2020) (AI2SD 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1418))

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Abstract

In the era of evolution of information technologies (IT), cloud computing plays a vital role in providing services through the internet. Cloud computing has many challenges, the major is security issues. Distributed denial of service (DDoS) is one of the most attacks treating cloud systems, it is able to make the service unavailable. So, it is necessary to deploy a mechanism of defense to detect these attacks earliest. Serval DDoS detection methods are challenged by the huge number of network flows generated by different distributed devices and high false-positive rate. In this paper, we present anomaly-based detection method of DDoS attack in the cloud environment based on a combination of deep neural networks and machine learning algorithms to improve accuracy and reduce false-positive rate, we evaluate our approach with various experiments were performed on the CIDDS-001 dataset. The approach conducted a satisfactory result accuracy of 0.99, a f1-score of 0.99.

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Authors and Affiliations

  1. LabSIV, Department of Computer Science, Faculty of Science, Ibn Zohr University, Agadir, Morocco

    Mohamed Ouhssini & Karim Afdel

Authors
  1. Mohamed Ouhssini
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  2. Karim Afdel
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Corresponding author

Correspondence to Mohamed Ouhssini .

Editor information

Editors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, Warszawa, Poland

    Janusz Kacprzyk

  2. Aurel Vlaicu University of Arad, Arad, Romania

    Valentina E. Balas

  3. Sciences and Techniques of Tangier, Abdelmalek Essaâdi University, Tangier, Morocco

    Mostafa Ezziyyani

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Ouhssini, M., Afdel, K. (2022). Machine Learning Methods for DDoS Attacks Detection in the Cloud Environment. In: Kacprzyk, J., Balas, V.E., Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2020). AI2SD 2020. Advances in Intelligent Systems and Computing, vol 1418. Springer, Cham. https://doi.org/10.1007/978-3-030-90639-9_32

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