Detecting and Mitigating DDoS Attacks with AI: A Survey
This is an incremental survey paper for cybersecurity researchers and practitioners, summarizing existing AI approaches to DDoS attacks.
This survey tackles the problem of DDoS attacks in cybersecurity by comprehensively reviewing AI-based detection and mitigation methods, providing a taxonomy to resolve categorization ambiguities and discussing datasets and training techniques.
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.