Please wait a minute...
 首页  期刊介绍 期刊订阅 联系我们 横山亮次奖 百年刊庆
 
最新录用  |  预出版  |  当期目录  |  过刊浏览  |  阅读排行  |  下载排行  |  引用排行  |  横山亮次奖  |  百年刊庆
清华大学学报(自然科学版)  2020, Vol. 60 Issue (6): 474-484    DOI: 10.16511/j.cnki.qhdxxb.2020.22.005
  专题:可信计算与信息安全 本期目录 | 过刊浏览 | 高级检索 |
基于无监督生成推理的网络安全威胁态势评估方法
杨宏宇1, 王峰岩1, 吕伟力2
1. 中国民航大学 计算机科学与技术学院, 天津 300300;
2. 中国石油天然气股份有限公司 管道长春输油气分公司, 长春 130000
Network security threat assessment method based on unsupervised generation reasoning
YANG Hongyu1, WANG Fengyan1, L�Weili2
1. School of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China;
2. Pipeline Changchun Transmission and Oil Company, China National Petroleum Corporation, Changchun 130000, China
全文: PDF(2132 KB)  
输出: BibTeX | EndNote (RIS)      
摘要 针对基于数据类别标记的监督式网络数据建模方式在评估网络威胁态势时存在计算成本高,效率低和耗时长的问题,该文提出一种基于无监督生成推理的网络安全威胁态势评估方法。首先,设计一种变分自动编码器-生成式对抗网络(VAE-GAN)模型,将只包含正常网络流量的训练数据集输入到由VAE-GAN组成的网络集合层进行训练,统计每层网络输出的重构误差,并使用输出层的3层变分自动编码器训练重构误差;然后使用包含异常网络流量的测试数据集进行分组威胁测试,统计每组测试的威胁发生概率;最后根据威胁发生概率确定网络安全威胁严重度,结合威胁影响度计算威胁态势值对网络安全威胁态势进行评估。仿真实验结果表明,与反向传播(BP)和径向基函数(RBF)方法相比,该方法能够更直观地评估网络威胁的整体态势,对网络威胁具有更好的表征效果。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
杨宏宇
王峰岩
吕伟力
关键词 无监督生成推理变分自动编码器-生成式对抗网络(VAE-GAN)威胁发生概率威胁态势评估    
Abstract:Supervised network data modeling based on data category tags is computationally expensive, inefficient and requires long time for network threat assessments. This paper presents a network security threat assessment method based on unsupervised generation reasoning. A variant auto encoder - generative adversarial network (VAE-GAN) model is designed with training data set containing only normal network traffic input to the network collection layer of the VAE-GAN while monitoring the reconstruction error of each layer network output and a 3-layer variant auto encoder of the output layer is used to train the reconstruction error with a test data set used for group threat testing while monitoring the threat occurrence probability for each group of tests. Finally, the severities of the network security threats are determined based on the threat occurrence probability with a threat situation impact factor used to calculate the threat level to quantify the network security threat. Simulations show that this method more intuitively evaluates the overall network security threat than back propagation (BP) and radical basis function (RBF) methods and more effectively characterizes the network threat.
Key wordsunsupervised generation reasoning    variant auto encoder-generative adversarial network (VAE-GAN)    threat probability    threat situation assessment
收稿日期: 2019-09-23      出版日期: 2020-04-27
基金资助:国家自然科学基金民航联合研究项目(U1833107)
通讯作者: 杨宏宇(1969-),男,教授。E-mail:yhyxlx@hotmail.com     E-mail: yhyxlx@hotmail.com
引用本文:   
杨宏宇, 王峰岩, 吕伟力. 基于无监督生成推理的网络安全威胁态势评估方法[J]. 清华大学学报(自然科学版), 2020, 60(6): 474-484.
YANG Hongyu, WANG Fengyan, L�Weili. Network security threat assessment method based on unsupervised generation reasoning. Journal of Tsinghua University(Science and Technology), 2020, 60(6): 474-484.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2020.22.005  或          http://jst.tsinghuajournals.com/CN/Y2020/V60/I6/474
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
[1] WANG H, CHEN Z F, FENG X, et al. Research on network security situation assessment and quantification method based on analytic hierarchy process[J]. Wireless Personal Communications, 2018, 102(2):1401-1420.
[2] ZHOU C, PAN P, MAO X Y, et al. Risk analysis of information system security based on distance of information-state transition[J]. Wuhan University Journal of Natural Sciences, 2018, 23(3):210-218.
[3] 文志诚, 陈志刚, 唐军. 基于信息融合的网络安全态势量化评估方法[J]. 北京航空航天大学学报, 2016, 42(8):1593-1602. WEN Z C, CHEN Z G, TANG J. Assessing network security situation quantitatively based on information fusion[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(8):1593-1602. (in Chinese)
[4] YU J J, HU M, WANG P. Evaluation and reliability analysis of network security risk factors based on D-S evidence theory[J]. Journal of Intelligent & Fuzzy Systems, 2018, 34(2):861-869.
[5] 朱闻亚. 卡尔曼熵值模型的网络安全态势估计[J]. 华侨大学学报(自然科学版), 2017, 38(1):101-104. ZHU W Y. Network security situation assessment based on Kalman entropy model[J]. Journal of Huaqiao University (Natural Science), 2017, 38(1):101-104. (in Chinese)
[6] HU G Y, ZHOU Z J, ZHANG B C, et al. A method for predicting the network security situation based on hidden BRB model and revised CMA-ES algorithm[J]. Applied Soft Computing, 2016, 48:404-418.
[7] 谢丽霞, 王亚超, 于巾博. 基于神经网络的网络安全态势感知[J]. 清华大学学报(自然科学版), 2013, 53(12):1750-1760. XIE L X, WANG Y C, YU J B. Network security situation awareness based on neural networks[J]. Journal of Tsinghua University (Science & Technology), 2013, 53(12):1750-1760. (in Chinese)
[8] DOERSCH C. Tutorial on variational autoencoders[Z]. arXiv preprint:1606.05908, 2016.
[9] AN J, CHO S. Variational autoencoder based anomaly detection using reconstruction probability[R]. Seoul, South Korea:SNU Data Mining Center, 2015.
[10] GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal, Canada:MIT Press, 2014:1-9.
[11] 中华人民共和国国务院. 国家突发公共事件总体应急预案[M]. 北京:中国法制出版社, 2006. State Council of the People's Republic of China. Overall emergency plans for national sudden public incidents[M]. Beijing:China Legal Press, 2006. (in Chinese)
[12] MELL P, SCARFONE K, ROMANOSKY S. Common vulnerability scoring system[J]. IEEE Security & Privacy, 2006, 4(6):85-89.
[13] FIRST. Common vulnerability scoring system v3.1:Specification document[S/OL].[2019-05-20]. https://www.first.org/cvss/specification-document.
[14] 唐成华, 余顺争. 一种基于似然BP的网络安全态势预测方法[J]. 计算机科学, 2009, 36(11):97-100, 168. TANG C H, YU S Z. Method of network security situation prediction based on likelihood BP[J]. Computer Science, 2009, 36(11):97-100, 168. (in Chinese)
[15] 赖智全. 基于混合优化RBF神经网络的网络安全态势预测模型[D]. 兰州:兰州大学, 2017. LAI Z Q. Prediction model of network security situation based on hybrid optimization RBF neural network[D]. Lanzhou:Lanzhou University, 2017. (in Chinese)
No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 《清华大学学报(自然科学版)》编辑部
本系统由北京玛格泰克科技发展有限公司设计开发 技术支持:support@magtech.com.cn