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In order to realize anomaly detection in dynamic data flow environment of wireless sensor networks, an iterative hyperelliptic boundary decision method is proposed in this paper. Firstly, the anomaly detection hyperellipse model is established, and then each node adjusts its hyperellipse model based on the measured values up to the current time, and finally the hyperellipse model converges to the hyperellipse boundary covering the normal and abnormal measurements. In order to improve the tracking ability of the model to the data changes in the monitoring environment, a forgetting factor combined with the benchmark estimation of sliding window and effective N tracking method is proposed to capture the real data flow attributes. The simulation results show that,compared with the advanced static modeling methods,the proposed dynamic modeling method not only has higher accuracy and anomaly detection ability, but also has stronger tracking and detection ability of data changes.