针对大规模网络态势要素获取时间复杂度较高和攻击样本不平衡导致小类样本分类精度不高的问题,提出一种基于深度自编码网络的态势要素获取机制.在该机制下,利用优化后的深度自编码网络作为基分类器,识别数据类型.一方面,在自编码网络的逐层训练中,提出一种结合交叉熵(CE)函数和反向传播(BP)算法的训练规则,克服传统的方差代价函数更新权值过慢的缺陷;另一方面,在深度网络的微调和分类阶段,提出一种主动在线采样(AOS)算法应用于分类器中,通过在线选择用于更新网络权值的攻击样本,达到总样本的去冗余和平衡各类攻击样本数量的目的,从而提高小类攻击样本的分类精度.经对实例数据的仿真分析,该方案有较好的态势要素获取精度,并能有效减少数据传输时的通信开销.%To reduce the time complexity of situational element acquisition and cope with the low detection accuracy of small class samples caused by imbalanced class distribution of attack samples in large-scale networks,a situation element extraction mechanism based on deep auto-encoder network was proposed.In this mechanism,the improved deep auto-encoder network was introduced as basic classifier to identify data type.On the one hand,in the training of the auto-encoder network,the training rule based on Cross Entropy (CE) function and Back Propagation (BP) algorithm was adopted to overcome the shortcoming of slow weights updating by the traditional variance cost function.On the other hand,in the stage of fine-tuning and classification of the deep network,an Active Online Sampling (AOS) algorithm was applied in the classifier to select the samples online for updating the network weights,so as to eliminate redundancy of the total samples,balance the amounts of all sample types,improve the classification accuracy of small class samples.Simulation and analysis results show that the proposed scheme has a good accuracy of situation element extraction and small communication overhead of data transmission.
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