Detecting and Mitigating DDoS Attacks: The Role of AI and Machine Learning

The growing frequency and complexity of Distributed Denial of Service (DDoS) attacks have necessitated more dynamic and intelligent cybersecurity solutions. This paper explores the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in detecting, mitigating, and preventing DDoS attacks across modern network infrastructures, especially in the context of IoT environments. Traditional security mechanisms struggle to adapt to the evolving tactics used in multi-vector DDoS attacks. In contrast, AI-driven intrusion detection systems provide adaptive learning, real-time anomaly detection, and improved decision-making through explainable AI. The study also examines AI-enabled methodologies such as deep learning, ensemble models, and blockchain integration, highlighting their potential to enhance the resilience and accuracy of defense mechanisms. Furthermore, the paper addresses the challenges of false positives, scalability, and privacy concerns associated with AI deployment. It concludes by emphasizing the need for continuous research and development in AI-centric security frameworks to ensure future-proof defense against sophisticated cyber threats.