Detecting and Mitigating DDoS Attacks with AI: A Survey
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914407455391744 |
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| author | Apostu, Alexandru Gheorghe, Silviu Hîji, Andrei Cleju, Nicolae Pătraşcu, Andrei Rusu, Cristian Ionescu, Radu Irofti, Paul |
| author_facet | Apostu, Alexandru Gheorghe, Silviu Hîji, Andrei Cleju, Nicolae Pătraşcu, Andrei Rusu, Cristian Ionescu, Radu Irofti, Paul |
| contents | Distributed Denial of Service attacks represent an active cybersecurity research problem. Recent research shifted from static rule-based defenses towards AI-based detection and mitigation. This comprehensive survey covers several key topics. Preeminently, state-of-the-art AI detection methods are discussed. An in-depth taxonomy based on manual expert hierarchies and an AI-generated dendrogram are provided, thus settling DDoS categorization ambiguities. An important discussion on available datasets follows, covering data format options and their role in training AI detection methods together with adversarial training and examples augmentation. Beyond detection, AI based mitigation techniques are surveyed as well. Finally, multiple open research directions are proposed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17867 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Detecting and Mitigating DDoS Attacks with AI: A Survey Apostu, Alexandru Gheorghe, Silviu Hîji, Andrei Cleju, Nicolae Pătraşcu, Andrei Rusu, Cristian Ionescu, Radu Irofti, Paul Cryptography and Security Artificial Intelligence Machine Learning Networking and Internet Architecture Distributed Denial of Service attacks represent an active cybersecurity research problem. Recent research shifted from static rule-based defenses towards AI-based detection and mitigation. This comprehensive survey covers several key topics. Preeminently, state-of-the-art AI detection methods are discussed. An in-depth taxonomy based on manual expert hierarchies and an AI-generated dendrogram are provided, thus settling DDoS categorization ambiguities. An important discussion on available datasets follows, covering data format options and their role in training AI detection methods together with adversarial training and examples augmentation. Beyond detection, AI based mitigation techniques are surveyed as well. Finally, multiple open research directions are proposed. |
| title | Detecting and Mitigating DDoS Attacks with AI: A Survey |
| topic | Cryptography and Security Artificial Intelligence Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2503.17867 |