计算机科学 ›› 2019, Vol. 46 ›› Issue (11A): 433-437.

• 信息安全 • 上一篇    下一篇

基于差分WGAN的网络安全态势预测

王婷婷, 朱江   

  1. (重庆邮电大学通信与信息工程学院 重庆400065)
  • 出版日期:2019-11-10 发布日期:2019-11-20
  • 作者简介:王婷婷(1993-),女,硕士生,主要研究方向为网络安全态势感知,E-mail:1879213049@163.com。;朱江(1977-),男,博士,教授,主要研究方向为认知无线电。

Network Security Situation Forecast Based on Differential WGAN

WGAN Ting-ting, ZHU Jiang   

  1. (School of Communication and Information Engineering,Chongqing University of Post and Telecommunications,Chongqing 400065,China)
  • Online:2019-11-10 Published:2019-11-20

摘要: 文中提出了一种基于差分WGAN(Wasserstein-GAN)的网络安全态势预测机制,该机制利用生成对抗网络(Generative Adversarial Network,GAN)来模拟态势的发展过程,从时间维度实现态势预测。为了解决GAN具有的网络难以训练、collapse mode及梯度不稳定的问题,提出了利用Wasserstein距离作为GAN的损失函数,并采用在损失函数中添加差分项的方法来提高态势值的分类精度,同时还证明了差分WGAN网络的稳定度。实验结果与分析表明,该机制相比其他机制而言,在收敛性、预测精度和复杂度方面具有优势。

关键词: Wasserstein-GAN, 差分, 生成对抗网络, 态势感知, 态势预测

Abstract: A network security posture prediction mechanism based on differential WGAN(Wasserstein- GAN) is presented in this paper.This mechanism uses Generative adversarial network (GAN) to simulate the development process of the situation,and realizes the situation forecast from the time Dimension.In order to solve the problem of difficult network training,collapse mode and gradient instability of GAN,this paper put forward the method by using Wasserstein distance as the loss function of GAN and adding the difference term in the loss function,to improve the classification precision of the situation value.The stability of the differential WGAN network was also proved.Experimental andanalysis results show that this mechanism has advantages over other mechanisms in terms of convergence,accuracy and complexity.

Key words: Difference, Generative adversarial network, Situation forecast, Situational awareness, Wasserstein-GAN

中图分类号: 

  • TN918.1
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