Security Situation Prediction Based on Dynamic BP Neural with Covariance
Chenghua Tang, Yi Xie, Baohua Qiang, Xin Wang, Ruixia Zhang · Procedia Engineering · 2011
Abstract Situation prediction is the advanced purpose of situation assessment. In order to resolve the limitations of depending on experts giving weight, lacking of self-learning on data processing in situation assessment, a method of network security situation prediction based on dynamic BP neural with covariance is proposed. The traditional error function is replaced by the maximum likelihood error function. The impact of sample covariance and noise on the network training is considered. The situation sequences established through the situation assessment model are used as the training input sequences, and the self-learning dynamic adjustment of the appointed parameters’ values is implemented in the process of back propagation training. The new method can make full use of the characteristics of the network more complex, finer grain size, the higher the efficiency. Experimental results show that the method has better approximation effect situation, and provides an effective way of network security strategic early warning.