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Fusion: Practice and Applications
Volume 11 , Issue 1, PP: 37-56 , 2023 | Cite this article as | XML | Html |PDF

Title

Recent Trends on Sophisticated types of Flooding Attacks and Detection Methods based on Multi Sensors Fusion Data for Cloud Computing Systems

  Nafea A. Majeed Alhammadi 1 * ,   Mohamed Mabrouk 2 ,   Mounir Zrigui 3

1  Research Laboratory in Algebra, Numbers Theory and Intelligent Systems RLANTIS, University of Monastir, Monastir, Tunisia; Department of Computer Sciences, Shatt Al-Arab University College, Basrah, Iraq
    (nafeaalhamadi@yahoo.com)

2  Department of Computer Sciences, Shatt Al-Arab University College, Basrah, Iraq
    (mab.mohamed@gmail.com)

3  Department of Computer Sciences, Shatt Al-Arab University College, Basrah, Iraq
    (mounirzrigur3030@gmail.com)


Doi   :   https://doi.org/10.54216/FPA.110103

Received: December 04, 2022 Accepted: April 03, 2023

Abstract :

Data storage, software services, infrastructure services, and platform services are only some of the benefits of today's widespread use of cloud computing. Since most cloud services run via the internet, they are vulnerable to a comprehensive range of attacks that might end it the disclosure of sensitive information. The distributed denial-of-service (DDoS) is amongst the attacks that pose an active threat to the cloud environment and disrupts the provided services for the legitimate participants. The main aim of this review paper is to present the recent trends on sophisticated flooding attacks detection methods for cloud computing systems. The review only considers the papers published within the period of 2014 until 2022.This study aims to examine the various deep learning-based DDoS detection algorithms and machine learning used across different cloud environments. Also, the study covers the Sophisticated types of Flooding Attacks and the testing dataset. The review outcomes several research challenges, gaps and future research guidelines related to protection of DDoS attack in cloud computing environment.

Keywords :

Flooding Attacks; Detection Methods; Machine Learning; Deep learning; Multi Sensors Fusion Data; Cloud Computing Systems.

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Cite this Article as :
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MLA Nafea A. Majeed Alhammadi, Mohamed Mabrouk, Mounir Zrigui. "Recent Trends on Sophisticated types of Flooding Attacks and Detection Methods based on Multi Sensors Fusion Data for Cloud Computing Systems." Fusion: Practice and Applications, Vol. 11, No. 1, 2023 ,PP. 37-56 (Doi   :  https://doi.org/10.54216/FPA.110103)
APA Nafea A. Majeed Alhammadi, Mohamed Mabrouk, Mounir Zrigui. (2023). Recent Trends on Sophisticated types of Flooding Attacks and Detection Methods based on Multi Sensors Fusion Data for Cloud Computing Systems. Journal of Fusion: Practice and Applications, 11 ( 1 ), 37-56 (Doi   :  https://doi.org/10.54216/FPA.110103)
Chicago Nafea A. Majeed Alhammadi, Mohamed Mabrouk, Mounir Zrigui. "Recent Trends on Sophisticated types of Flooding Attacks and Detection Methods based on Multi Sensors Fusion Data for Cloud Computing Systems." Journal of Fusion: Practice and Applications, 11 no. 1 (2023): 37-56 (Doi   :  https://doi.org/10.54216/FPA.110103)
Harvard Nafea A. Majeed Alhammadi, Mohamed Mabrouk, Mounir Zrigui. (2023). Recent Trends on Sophisticated types of Flooding Attacks and Detection Methods based on Multi Sensors Fusion Data for Cloud Computing Systems. Journal of Fusion: Practice and Applications, 11 ( 1 ), 37-56 (Doi   :  https://doi.org/10.54216/FPA.110103)
Vancouver Nafea A. Majeed Alhammadi, Mohamed Mabrouk, Mounir Zrigui. Recent Trends on Sophisticated types of Flooding Attacks and Detection Methods based on Multi Sensors Fusion Data for Cloud Computing Systems. Journal of Fusion: Practice and Applications, (2023); 11 ( 1 ): 37-56 (Doi   :  https://doi.org/10.54216/FPA.110103)
IEEE Nafea A. Majeed Alhammadi, Mohamed Mabrouk, Mounir Zrigui, Recent Trends on Sophisticated types of Flooding Attacks and Detection Methods based on Multi Sensors Fusion Data for Cloud Computing Systems, Journal of Fusion: Practice and Applications, Vol. 11 , No. 1 , (2023) : 37-56 (Doi   :  https://doi.org/10.54216/FPA.110103)