Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (6): 92-98.DOI: 10.3778/j.issn.1002-8331.1811-0344

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Network Security Situation Evaluation Using Deep Auto-Encoders Network

ZHANG Yuchen, ZHANG Renchuan, LIU Jing, WANG Yongwei   

  1. Information Engineering University, Zhengzhou 450004, China
  • Online:2020-03-15 Published:2020-03-13

应用深度自编码网络的网络安全态势评估

张玉臣,张任川,刘璟,汪永伟   

  1. 信息工程大学,郑州 450004

Abstract:

Aiming at the defect of the dependence of BP neural network on label data, a situation assessment method based on deep automatic coding network is proposed. The model uses depth auto-encoder as the basic unit to construct depth auto-coding network, and trains depth auto-coding network with expert experience and hierarchical evaluation method. The network is pre-trained by unsupervised layer-by-layer algorithm using unlabeled data to determine the range space of parameters and weights of each layer of the network. Based on this, the network is fine-tuned by using labeled samples with supervised algorithm so that the parameters and weights of each layer are optimized. As a result a model with the ability to accurately evaluate the input situation data is formed. Compared with BP neural network, the deep auto-coding network model is less affected by labels, which significantly reduces the dependence on expert experience, and has a higher overall evaluation accuracy.

Key words: neural network, situation prediction, deep automatic encoder, deep auto-encoders network, tag

摘要:

针对BP神经网络类方法对标签数据的依赖性缺陷,提出了一种基于深度自动编码网络的态势评估方法。模型应用深度自动编码器作为基本单元构建深度自编码网络,结合专家经验和层次化评估的方法训练深度自编码网络。利用无标签数据采用无监督逐层算法对网络进行预训练,确定网络各层参数及权值的范围空间。在此基础上,采用有监督算法使用有标签样本对网络进行微调,对各层参数及权值进行优化,最终形成具有对输入态势数据进行准确评估能力的模型。多种样本数量条件下的对比实验表明,相对于BP神经网络类方法,基于深度自动编码网络模型受标签的影响较小,明显减少了对专家经验的依赖,并且具有整体上较高的评估精度。

关键词: 神经网络, 态势评估, 深度自动编码器, 深度自编码网络, 标签