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Machine learning and deep learning approaches for detecting DDoS attacks in cloud environments

Distributed Denial of Service (DDoS) attacks pose a significant threat to cloud computing environments, necessitating advanced detection methods. This review examines the application of Machine Learning (ML) and Deep Learning (DL) techniques for DDoS detection in cloud settings, focusing on research...

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Main Authors: Khan, Muhammad Asif, Mohd Faizal, Ab Razak, Zafril Rizal, M. Azmi, Ahmad Firdaus, Zainal Abidin, Nuhu, Abdul Hafeez, Hussain, Syed Shuja
Format: Article
Language:English
Published: American Scientific Publishing Group (ASPG) 2025
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/42862/1/Machine%20Learning%20and%20Deep%20Learning%20Approaches%20for%20Detecting.pdf
http://umpir.ump.edu.my/id/eprint/42862/
https://doi.org/10.54216/FPA.170207
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spelling my.ump.umpir.428622024-10-28T02:50:59Z http://umpir.ump.edu.my/id/eprint/42862/ Machine learning and deep learning approaches for detecting DDoS attacks in cloud environments Khan, Muhammad Asif Mohd Faizal, Ab Razak Zafril Rizal, M. Azmi Ahmad Firdaus, Zainal Abidin Nuhu, Abdul Hafeez Hussain, Syed Shuja QA75 Electronic computers. Computer science Distributed Denial of Service (DDoS) attacks pose a significant threat to cloud computing environments, necessitating advanced detection methods. This review examines the application of Machine Learning (ML) and Deep Learning (DL) techniques for DDoS detection in cloud settings, focusing on research from 2019 to 2024. It evaluates the effectiveness of various ML and DL approaches, including traditional algorithms, ensemble methods, and advanced neural network architectures, while critically analyzing commonly used datasets for their relevance and limitations in cloud-specific scenarios. Despite improvements in detection accuracy and efficiency, challenges such as outdated datasets, scalability issues, and the need for real-time adaptive learning persist. Future research should focus on developing cloud-specific datasets, advanced feature engineering, explainable AI, and cross-layer detection approaches, with potential exploration of emerging technologies like quantum machine learning. American Scientific Publishing Group (ASPG) 2025 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/42862/1/Machine%20Learning%20and%20Deep%20Learning%20Approaches%20for%20Detecting.pdf Khan, Muhammad Asif and Mohd Faizal, Ab Razak and Zafril Rizal, M. Azmi and Ahmad Firdaus, Zainal Abidin and Nuhu, Abdul Hafeez and Hussain, Syed Shuja (2025) Machine learning and deep learning approaches for detecting DDoS attacks in cloud environments. Fusion: Practice and Applications (FPA), 17 (2). pp. 79-97. ISSN 2692-4048. (Published) https://doi.org/10.54216/FPA.170207 10.54216/FPA.170207
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Khan, Muhammad Asif
Mohd Faizal, Ab Razak
Zafril Rizal, M. Azmi
Ahmad Firdaus, Zainal Abidin
Nuhu, Abdul Hafeez
Hussain, Syed Shuja
Machine learning and deep learning approaches for detecting DDoS attacks in cloud environments
description Distributed Denial of Service (DDoS) attacks pose a significant threat to cloud computing environments, necessitating advanced detection methods. This review examines the application of Machine Learning (ML) and Deep Learning (DL) techniques for DDoS detection in cloud settings, focusing on research from 2019 to 2024. It evaluates the effectiveness of various ML and DL approaches, including traditional algorithms, ensemble methods, and advanced neural network architectures, while critically analyzing commonly used datasets for their relevance and limitations in cloud-specific scenarios. Despite improvements in detection accuracy and efficiency, challenges such as outdated datasets, scalability issues, and the need for real-time adaptive learning persist. Future research should focus on developing cloud-specific datasets, advanced feature engineering, explainable AI, and cross-layer detection approaches, with potential exploration of emerging technologies like quantum machine learning.
format Article
author Khan, Muhammad Asif
Mohd Faizal, Ab Razak
Zafril Rizal, M. Azmi
Ahmad Firdaus, Zainal Abidin
Nuhu, Abdul Hafeez
Hussain, Syed Shuja
author_facet Khan, Muhammad Asif
Mohd Faizal, Ab Razak
Zafril Rizal, M. Azmi
Ahmad Firdaus, Zainal Abidin
Nuhu, Abdul Hafeez
Hussain, Syed Shuja
author_sort Khan, Muhammad Asif
title Machine learning and deep learning approaches for detecting DDoS attacks in cloud environments
title_short Machine learning and deep learning approaches for detecting DDoS attacks in cloud environments
title_full Machine learning and deep learning approaches for detecting DDoS attacks in cloud environments
title_fullStr Machine learning and deep learning approaches for detecting DDoS attacks in cloud environments
title_full_unstemmed Machine learning and deep learning approaches for detecting DDoS attacks in cloud environments
title_sort machine learning and deep learning approaches for detecting ddos attacks in cloud environments
publisher American Scientific Publishing Group (ASPG)
publishDate 2025
url http://umpir.ump.edu.my/id/eprint/42862/1/Machine%20Learning%20and%20Deep%20Learning%20Approaches%20for%20Detecting.pdf
http://umpir.ump.edu.my/id/eprint/42862/
https://doi.org/10.54216/FPA.170207
_version_ 1822924734072881152
score 13.235362

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