This paper proposes a detection method that combines maximum likelihood estimation and entropy analysis to address the problem of fast evolution of DDoS attacks and slow detection response in the high dynamic and high timeliness characteristics of intelligent IoT emergency communication networks. This method identifies data by quantitatively analyzing the deviation of normal traffic, calculating the probability of abnormal traffic, evaluating the credibility of edge nodes based on the time series of abnormal traffic to provide feedback, reconstructing the scale space of attack parameters, and achieving attack behavior detection and risk analysis. Experimental results have shown that this method can accurately identify all abnormal attack traffic, effectively detect the sustained stability of multi node DDoS attack signals, and adapt to the rapid evolution of attack methods.