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A Quantitative Prediction Method of Network Security Situation Based on Wavelet Neural Network

机译:基于小波神经网络的网络安全态势定量预测方法

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摘要

In order to predict the network security situation, a quantitative prediction method of network security situation based on Wavelet Neural Network (WNN) was proposed. After analyzing the past and the current network security situation in detail, we built the WNN architecture of network security situation prediction and adopted it to forecast the non-linear time series of network security situation. Simulation experiments proved that the proposed method had advantages over a Back Propagation Neural Network method (BPNN) with the same architecture in the convergence speed, functional approximation and prediction accuracy.
机译:为了预测网络安全情况,提出了一种基于小波神经网络(WNN)的网络安全情况的定量预测方法。详细分析了过去和当前的网络安全情况后,我们建立了网络安全情况预测的WNN架构,并采用了预测网络安全情况的非线性时间序列。仿真实验证明,该方法的优点在于回到传播神经网络方法(BPNN)具有相同的架构,在收敛速度,功能近似和预测精度。

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