应用深度自编码网络的网络安全态势评估

Network Security Situation Evaluation Using Deep Auto-Encoders Network

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。

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.