The spread of new technologies like the Internet of Things (IoT) and Software-Defined Networking (SDN) in recent years has extended the distributed denial of service (DDoS) attack vector and created new possibilities for more advanced DDoS attacks on the intended targets. The new attack vector comprises internet-enabled IoT devices that are unprotected and vulnerable. Given the high volume and widespread nature of these attacks, it is very difficult to detect and analyze the frequency of DDoS attacks. There are existing techniques available to mitigate the attacks, including intelligent machine learning models. In this paper we will be discussing emerging techniques and how to implement those new techniques with existing machine learning models to make the detection and mitigation model robust to handle the attacks. We will be discussing advanced anomaly detection and traffic analysis, traffic scrubbing, and Content delivery network (CDN). We will also be discussing ensemble learning to implement an efficient and strong model for the detection and mitigation of Distributed Denial of Service (DDoS) attacks on the internet connected devices