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是网络安全领域一个活跃的研究问题。近期研究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。本综述全面涵盖了若干关键议题。首先重点讨论了当前最先进的人工智能检测方法。通过基于专家手工分类体系与人工智能生成的树状图相结合的方式，提出了一种深入的分类法，从而解决了DDoS攻击分类模糊的问题。随后对现有数据集进行了重要讨论，涵盖了数据格式选择及其在训练人工智能检测方法中的作用，同时涉及对抗训练与样本增强技术。除检测外，本综述还调研了基于人工智能的缓解技术。最后提出了多个尚未解决的研究方向。