基于时间因子和复合CNN结构的网络安全态势评估

Network Security Situation Based on Time Factor and Composite CNN Structure

为了解决传统的网络安全态势感知研究方法在网络信息复杂情况下准确率不高等缺陷,文中结合深度学习,提出了一种基于时间因子和复合CNN结构的网络安全态势评估模型,将卷积分解技术和深度可分离技术相结合,形成4层串联复合最优单元结构;将一维网络数据转换为二维矩阵,以灰度值的形式载入神经网络模型,从而有效发挥卷积神经网络的优势。为充分利用数据间的时序关系,引入时间因子形成融合数据,使网络同时学习具备时序关系的原始数据和融合数据,增强模型的特征提取能力,同时利用时间因子和点卷积建立时序数据的空间映射,提高模型结构的完整性。实验结果证明,所提模型在两个数据集上的准确率分别达到了92.89%和92.60%,相比随机森林和LSTM算法提升了2%~6%。

In order to solve the problem of low accuracy of traditional network security situation awareness research methods in the case of complex network information, combined with deep learning, this paper proposes a network security situation assessment model based on time factor and composite CNN structure, which combines volume integral solution technology and deep separable technology to form a four layer series composite optimal unit structure.The one-dimensional network data are transformed into two-dimensional matrix and loaded into the neural network model in the form of gray value, so as to give full play to the advantages of convolution neural network.In order to make full use of the time-series relationship between data, time factor is introduced to form fusion data, which makes the network to learn the original data and fusion data with time-series relationship at the same time, the feature extraction ability of the model is increased, the spatial mapping of time-series data is established by using time factor and point convolution, and the integrity of the model structure is increased.Experimental results show that the accuracy of the proposed model on two datasets is 92.89% and 92.60% respectively, which is 2%~6% higher than random fo-rest and LSTM algorithm.