Programmable data planes have provided great flexibility in defining the behaviors of packet forwarding switches, routers, and network interface cards (NICs). The In-band Network Telemetry (INT) technology further increased network operators’ potential to manage packet flows by enabling real-time and customizable monitoring of packets without creating much overhead on the network. These recent advancements in networking technology have generated significant research interest and activity, including studies on INT-based DDoS detection and mitigation mechanisms. However, in practice, INT technology has not been fully realized yet, especially in detecting network anomalies in real-time. There is also a gap in the literature that provides a comparative evaluation of INT-based solutions against existing alternatives. In this paper, we aim to implement a holistic real-time INT-based DDoS detection mechanism. The proposed mechanism will retrieve INT data from the network, analyze it using machine learning (ML) models in real-time, and send the information to the control plane. We will also compare the performance of using INT to detect DDoS attacks against sFlow-based detection.