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.

翻译：分布式拒绝服务攻击一直是网络安全领域活跃的研究问题。近年来，研究重心已从基于静态规则的防御方法转向基于人工智能的检测与缓解技术。本综述涵盖多个关键议题:首先重点探讨了当前最先进的AI检测方法；其次，通过基于专家手动分类体系与AI生成的树状图相结合的分类框架，解决了DDoS攻击类别划分的歧义性问题；随后梳理了现有数据集的讨论，涵盖数据格式选择及其在训练AI检测方法中的作用，同时涉及对抗训练与样本增强技术；在检测之外，本文还综述了基于AI的缓解技术。最后，提出了多个具有潜力的开放性研究方向。