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^1,2*, Mohamed Mabrouk², Mounir Zrigui²

¹Research Laboratory in Algebra, Numbers Theory and Intelligent Systems RLANTIS, University of Monastir, Monastir, Tunisia

²Department of Computer Sciences, Shatt Al-Arab University College, Basrah, Iraq

Emails: nafeaalhamadi@yahoo.com ; mab.mohamed@gmail.com ; mounirzrigur3030@gmail.com

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.