AI-based DDoS detection and mitigation focuses on identifying distributed denial-of-service activity and evaluating its impact on web server availability using machine learning and deep learning techniques. It involves analyzing incoming traffic patterns, extracting relevant network and request-level features, distinguishing legitimate client activity from potentially malicious traffic surges, and assessing the factors contributing to attack detection. By combining deep learning-based traffic classification with explainable machine learning, this approach helps identify abnormal request patterns, understand the characteristics associated with DDoS activity, and support appropriate mitigation decisions. The resulting analysis enables security teams to evaluate detection performance, investigate attack behavior, assess mitigation effectiveness, and strengthen the resilience and availability of web services against traffic-based threats.