Advancements in detecting, preventing, and mitigating DDoS attacks in cloud environments: A comprehensive systematic review of state-of-the-art approaches

被引：0
作者：
Ouhssini, Mohamed [1 ]
Afdel, Karim [1 ]
Akouhar, Mohamed [2 ]
Agherrabi, Elhafed [3 ]
Abarda, Abdallah [4 ]
机构：
[1] Univ IBN Zohr, Dept Comp Sci, Lab SIV, Agadir, Morocco
[2] Univ IBN Tofail, Lab Partial Differential Equat Algebra & Spectral, Kenitra 14000, Morocco
[3] Ibn Zohr Univ, Fac Sci, Dept Math, LabIRF SIC, Agadir, Morocco
[4] Hassan First Univ Settat, Lab Math Modeling & Econ Calculat, Settat, Morocco
来源：
EGYPTIAN INFORMATICS JOURNAL | 2024年 / 27卷
关键词：
DDoS attacks; Cloud environments; Effective strategies; Systematic review; Cloud security; Defense mechanisms; Machine learning; Security measures; Deep learning; MODEL; DEFENSE;
D O I：
10.1016/j.eij.2024.100517
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This comprehensive study examines cutting-edge strategies for combating Distributed Denial of Service (DDoS) attacks in cloud environments, addressing a critical gap in recent literature. Through a systematic review of the latest advancements, we propose a framework for identifying, preventing, and mitigating DDoS threats specifically tailored to cloud infrastructures. Our research highlights the urgent need for robust defense mechanisms to enhance cloud security, minimize service disruptions, and safeguard against data breaches. By analyzing the strengths and limitations of current models, we underscore the importance of continued innovation in this rapidly evolving field. This study provides essential insights for academics and industry professionals aiming to enhance the resilience of cloud infrastructure against the ongoing and adaptive menace of DDoS attacks.
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页数：37
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