In order to validate the feasibility and applicability of the chaotic prediction model of network threat time se-ries

a fractal self-similarity analysis method for network threat time series based on the R/S （rescaled range） analysis was proposed. Using this method

the Hurst exponent of the representative samples from the three data sets of network threat were computed and tested. It was verified that there exist statistic self-similarities in continuous and non-sparse discrete time series of network threat so that it will be feasible to predict. On the other hand

there is no statistic self-similarity in sparse discrete threat time series and it will be very difficult to predict. The research outcome establishes the theory base to utilize the complex non-linearity system theory such as fractal and chaos to process the information security risk as-sessment and network threat prediction.