Abstract: The measurement studies show that the burstiness of packet traffic in LAN as well as WAN is associated with self－similar and long－range dependency,and Hurst parameter is the key value of this model representing the burstiness of traffic.An adaptive,efficient unbiased estimator of Hurst index based on the lifting scheme　for wavelet transform and correlation coefficient is presented.Compared with the existing wavelet－based estimator,the new method performs inplace computation and reduces the　computational complexity by about half.Simulation results based on fractal Gaussian noise and real traffic data reveal the proposed approach shows more adaptiveness,accuracy and robustness than traditional estimators.Thus this estimator can be applied to traffic management and real－time control in high－speed networks.