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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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American Scientific Publishing Group (ASPG)
2025
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| Online Access: | http://umpir.ump.edu.my/id/eprint/42862/ http://umpir.ump.edu.my/id/eprint/42862/1/Machine%20Learning%20and%20Deep%20Learning%20Approaches%20for%20Detecting.pdf |
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| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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 Despite improvements in detection accuracy and efficiency, challenges such as outdated datasets, scalability issues, and the need for real-time adaptive learning persist.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. |
| first_indexed | 2025-11-15T03:49:17Z |
| format | Article |
| id | ump-42862 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:49:17Z |
| publishDate | 2025 |
| publisher | American Scientific Publishing Group (ASPG) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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 |
| 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 |
| title | 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_short | 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 |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/42862/ http://umpir.ump.edu.my/id/eprint/42862/ http://umpir.ump.edu.my/id/eprint/42862/ http://umpir.ump.edu.my/id/eprint/42862/1/Machine%20Learning%20and%20Deep%20Learning%20Approaches%20for%20Detecting.pdf |