Abstract: Aiming at the low efficiency of the existing network security situation assessment method based on neural network, a network security situation assessment method based on Cuckoo Search (CS) optimized Back Propagation (BP) Neural Network (CSBPNN) was proposed. Firstly, the numbers of input and output nodes of the BP Neural Network (BPNN) were determined according to the number of input index and the output value. The number of hidden layer nodes was calculated according to the empirical formula and the trial and error method. Secondly, the connection weights and thresholds were randomly initialized, and the weights and thresholds were coded into cuckoo by using floating point coding. Finally, the weights and thresholds were optimized by using CS algorithm. The CSBPNN model for situation assessment was got and trained. The situation data was input into the CSBPNN model to obtain the situation value. The experimental results show that the iterative number of CSBPNN is reduced by 943 and 47 respectively, and the prediction accuracy is 8.06 percentage points and 3.89 percentage points higher than that of BPNN and Genetic Algorithm (GA) optimized BP neural network. The proposed algorithm has faster convergence speed and higher prediction accuracy.

Key words: situation assessment, network security, Cuckoo Search (CS), neural network, high-precision

摘要： 针对现有基于神经网络的网络安全态势评估方法效率低等问题，提出基于布谷鸟搜索（CS）优化反向传播（BP）神经网络（CSBPNN）的网络安全态势评估方法。首先，根据态势输入指标数和输出态势值确定BP神经网络（BPNN）的输入输出节点数，根据经验公式和试凑法计算出隐含层节点数；然后，随机初始化各层的连接权值和阈值，使用浮点数编码方式将权值与阈值编码成布谷鸟；最后，使用CS算法对权值和阈值进行优化，得到用于态势评估的CSBPNN模型并对其进行训练，将网络安全态势数据输入到CSBPNN模型中，获取网络的安全态势值。实验结果表明，与BPNN和遗传算法优化BP神经网络方法相比，基于CSBPNN的网络安全态势评估方法的迭代代数分别减少943和47且预测精度提高8.06个百分点和3.89个百分点，所提方法具有较快的收敛速度和较高的预测精度。

关键词: 态势评估, 网络安全, 布谷鸟搜索, 神经网络, 高精度