Abstract A statistical method for prediction and modeling of cyber-attack signal is proposed. The proposed method is developed by coupling the traditional ARIMA method with Hilbert–Huang transform (HHT), designed to reduce the dimensionality and to extract meaningful signals for reliable prediction. HHT decomposes cyber-attack signals of interest into several components including short- and long-term patterns, and random fluctuation. Due to Hilbert transform, the method selects significant decomposed signals that will be employed for signal prediction. Subsequently, by using the traditional dynamic models, the proposed method provides a stable prediction of cyber-attack signal. To show the performance of the proposed method, we analyze daily worm count data from August 1, 2005 to October 9, 2006.