Recognition of Network Security Situation Elements Based on Depth Stack Encoder and Back Propagation Algorithm
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摘要: 网络安全态势要素识别的基础是对态势数据集进行有效的特征提取。针对反向传播(BP)神经网络对海量安全态势信息数据学习时过度依赖数据标签的问题，该文提出一种结合深度堆栈编码器和反向传播算法的网络安全态势要素识别方法，通过无监督学习算法逐层训练网络，在此基础上堆叠得到深度堆栈编码器，利用编码器提取数据集特征，实现了网络的无监督训练。仿真实验验证了该方法能有效提升安全态势感知的效能和准确度。Abstract: The basis of the identification of network security situation element is to perform the feature extraction of situation data effectively. Considering the problem that the Back Propagation(BP) neural networks have excessive dependence on data labels when it has a learning of massive security situation information data, a network security situation element identification method is proposed, which combines deep stack encoder and BP algorithm. It trains the network layer by layer through unsupervised learning algorithm. On this basis the deep track encoder by stacking can be obtained. The unsupervised training of the network is realized when using the encoder to extract the characteristic of the data sets. It is verified by simulation experiments that the method can improve the performance and accuracy of situational awareness effectively.
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表 1 不同样本数量下的BP神经网络和改进型BP神经网络识别率结果
样本数量 识别率 BP 改进BP 1000 0.893 0.940 3000 0.919 0.954 5000 0.924 0.953 7000 0.892 0.954 9000 0.960 0.972 11000 0.957 0.970 13000 0.901 0.987 15000 0.952 0.982 17000 0.963 0.965 19000 0.959 0.986 21000 0.964 0.972 23000 0.966 0.980 25000 0.958 0.989 27000 0.959 0.979 29000 0.965 0.984 31000 0.965 0.988 33000 0.961 0.988 35000 0.972 0.978 37000 0.972 0.992 40000 0.975 0.993 
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表 2 不同标签占比下的BP神经网络和改进型BP神经网络识别率结果
训练集中标签占比(%) 识别率(DARPA1999) 识别率(ISCX 2012) BP 改进BP BP 改进BP 10 0.899 0.951 0.854 0.926 30 0.925 0.959 0.862 0.934 50 0.936 0.965 0.877 0.936 70 0.939 0.967 0.879 0.944 90 0.942 0.971 0.892 0.949 100 0.951 0.973 0.905 0.952
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