<p>Software-Defined Networking (SDN), as a new network architecture, has brought convenience, but also suffered from the threat of Distributed Denial of Service (DDoS) attack. However, most existing DDoS attack detection schemes for SDN employ only a single detection method, leading to imbalances in detection speed, system overhead, and detection accuracy. Even though a few schemes improve detection efficiency and accuracy through two-stage detection, they still suffer from low system flexibility and do not support dynamic threshold adjustment. In order to resolve these issues, we propose an adaptive two-stage DDoS attack detection scheme with Dynamic Threshold Adjustment (ATS-DTA for short), which contains three sub-modules. More specifically, by dividing DDoS attack detection into two modules: a conditional entropy-based network traffic anomaly detection phase and a DDoS attack detection phase based on machine learning methods. Additionally, an adaptive threshold adjustment module is introduced to improve the system’s flexibility. Finally, the experimental results show that our scheme, compared to related schemes, not only significantly improves detection accuracy and speed but also supports flexible and dynamic threshold adjustment. Specifically, our method achieves an average accuracy improvement of 1.91% and a precision increase of 1.23% over baseline methods, underscoring its effectiveness in adapting to complex and evolving network environments. These advantages illustrate that our ATS-DTA scheme provides a more balanced, efficient, and reliable solution for DDoS detection in dynamic network scenarios.</p>