Deep learning-driven defense strategies for mitigating DDoS attacks in cloud computing environments
The kind of cyber threat prevalent and most dangerous to networked systems is the Distributed Denial of Service (DDoS), especially with expanded connection of Internet of Things (IoT) devices. This article categorizes DDoS attacks into three primary types: volumetric, protocol based and application...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-12-01
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| Series: | Cyber Security and Applications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772918425000025 |
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| author | Doaa Mohsin Abd Ali Afraji Jaime Lloret Lourdes Peñalver |
| author_facet | Doaa Mohsin Abd Ali Afraji Jaime Lloret Lourdes Peñalver |
| author_sort | Doaa Mohsin Abd Ali Afraji |
| collection | DOAJ |
| description | The kind of cyber threat prevalent and most dangerous to networked systems is the Distributed Denial of Service (DDoS), especially with expanded connection of Internet of Things (IoT) devices. This article categorizes DDoS attacks into three primary types: volumetric, protocol based and application layer of cyber attacks. It discusses the application of security threats that arise from the use of the DL models, accusing recently introduced ideas and stressing pitfalls: the issues of data and methods scarcity. There is the same need for the greater use of explainable and transparent AI to improve confidence in such security systems as is noted in the review. It also reveals that present detection performance is constrained and frequently obstructed by the poor quality of the datasets. The future work is proposed to build superior datasets and use accurate algorithm to improve the security models. This paper focuses on explainability as a way of making the AI model creation process and any consequent decisions explainable and transparent. The use of deep learning enhances the capability of cybersecurity in handling DDoS attacks and preventing or controlling them. But it has to be a part of a more large-scope platform, based on multiple types of longitudinal or cross-sectional data combined with high efficiency, explainable AI. The article ends with call to proceed with studying and advancing the AI application in response to new threats, and make the most of it to enhance protection of the contemporary networked environment. |
| format | Article |
| id | doaj-art-3b721dcf110c4050bd7f2529f2e469e2 |
| institution | Kabale University |
| issn | 2772-9184 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Cyber Security and Applications |
| spelling | doaj-art-3b721dcf110c4050bd7f2529f2e469e22025-01-30T05:15:18ZengKeAi Communications Co., Ltd.Cyber Security and Applications2772-91842025-12-013100085Deep learning-driven defense strategies for mitigating DDoS attacks in cloud computing environmentsDoaa Mohsin Abd Ali Afraji0Jaime Lloret1Lourdes Peñalver2Department of Computer Engineering, Universitat Politècnica de València, Valencia, Spain; Department of Computer Science, College of Education, Mustansiriyah University, Baghdad, IraqDepartment of Computer Engineering, Universitat Politècnica de València, Valencia, Spain; Corresponding author.Integrated Management Coastal Zones Research Institute, Universitat Politècnica de València, Valencia, SpainThe kind of cyber threat prevalent and most dangerous to networked systems is the Distributed Denial of Service (DDoS), especially with expanded connection of Internet of Things (IoT) devices. This article categorizes DDoS attacks into three primary types: volumetric, protocol based and application layer of cyber attacks. It discusses the application of security threats that arise from the use of the DL models, accusing recently introduced ideas and stressing pitfalls: the issues of data and methods scarcity. There is the same need for the greater use of explainable and transparent AI to improve confidence in such security systems as is noted in the review. It also reveals that present detection performance is constrained and frequently obstructed by the poor quality of the datasets. The future work is proposed to build superior datasets and use accurate algorithm to improve the security models. This paper focuses on explainability as a way of making the AI model creation process and any consequent decisions explainable and transparent. The use of deep learning enhances the capability of cybersecurity in handling DDoS attacks and preventing or controlling them. But it has to be a part of a more large-scope platform, based on multiple types of longitudinal or cross-sectional data combined with high efficiency, explainable AI. The article ends with call to proceed with studying and advancing the AI application in response to new threats, and make the most of it to enhance protection of the contemporary networked environment.http://www.sciencedirect.com/science/article/pii/S2772918425000025DDoS attacksInternet of thingsDeep learningCybersecurityExplainable AIDataset limitations |
| spellingShingle | Doaa Mohsin Abd Ali Afraji Jaime Lloret Lourdes Peñalver Deep learning-driven defense strategies for mitigating DDoS attacks in cloud computing environments Cyber Security and Applications DDoS attacks Internet of things Deep learning Cybersecurity Explainable AI Dataset limitations |
| title | Deep learning-driven defense strategies for mitigating DDoS attacks in cloud computing environments |
| title_full | Deep learning-driven defense strategies for mitigating DDoS attacks in cloud computing environments |
| title_fullStr | Deep learning-driven defense strategies for mitigating DDoS attacks in cloud computing environments |
| title_full_unstemmed | Deep learning-driven defense strategies for mitigating DDoS attacks in cloud computing environments |
| title_short | Deep learning-driven defense strategies for mitigating DDoS attacks in cloud computing environments |
| title_sort | deep learning driven defense strategies for mitigating ddos attacks in cloud computing environments |
| topic | DDoS attacks Internet of things Deep learning Cybersecurity Explainable AI Dataset limitations |
| url | http://www.sciencedirect.com/science/article/pii/S2772918425000025 |
| work_keys_str_mv | AT doaamohsinabdaliafraji deeplearningdrivendefensestrategiesformitigatingddosattacksincloudcomputingenvironments AT jaimelloret deeplearningdrivendefensestrategiesformitigatingddosattacksincloudcomputingenvironments AT lourdespenalver deeplearningdrivendefensestrategiesformitigatingddosattacksincloudcomputingenvironments |