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Network traffic anomaly detection can be done through the self-similar analysis of network traffic. In this case, the abnormal condition of network can be indicated by investigating if the performance parameters of real time data locate at the acceptable ranges. A common method of estimating self-similar parameter is the Wavelet transform. However, the Wavelet transform fails to exclude the influence of non-stationary signal’s periodicity and trend term. In view of the fact that Hilbert-Huang Transform (HHT) has unique advantage on non-stationary signal treatment, in this paper, a refined self-similar parameter estimation algorithm is designed through the combination of wavelet analysis and Hilbert-Huang Transform and a set of experiments are run to verify the improvement in the accuracy of parameter estimation and network traffic anomaly detection.

In accordance with the characteristics and the special demands of electric power information system, this paper designs a support vector machines (SVM) risk assessment method based on group decision-marking. According to security technology indices of electric power information system, the method chooses the mode of expert scoring, based on the group decision-marking, to calculate integrated value of each index, which is as a training sample used to train SVM, and it forecasts risk level for the system. Finally, it verifies the correctness of the method by analyzing results of the examples of the electric power information system security assessment. The experiment shows that the method can not only forecast the current risk level of the electric power system with a high accuracy rate, but also reduce the influence of the subjective factors in some degree.