基于微分流形的网络攻防效用度量方法

赵小林, 姜筱奕, 赵晶晶, 徐浩, 郭煚

清华大学学报（自然科学版） ›› 2020, Vol. 60 ›› Issue (5) : 380-385.

PDF(880 KB)
横山亮次奖  |  百年刊庆  |  中文  |  English
PDF(880 KB)
清华大学学报（自然科学版） ›› 2020, Vol. 60 ›› Issue (5) : 380-385. DOI: 10.16511/j.cnki.qhdxxb.2020.26.002
专题：漏洞分析与风险评估

基于微分流形的网络攻防效用度量方法

  • 赵小林, 姜筱奕, 赵晶晶, 徐浩, 郭煚
作者信息 +

Metrics for network attack and defense effectiveness based on differential manifolds

  • ZHAO Xiaolin, JIANG Xiaoyi, ZHAO Jingjing, XU Hao, GUO Jiong
Author information +
文章历史 +

摘要

针对网络安全的度量缺乏有效的衡量动态网络中攻防的风险的问题和存在指标较多时维度高难以计算的问题，该文提出一种网络攻防效用的度量方法，通过聚类和主成分分析对指标降维、分配权重，将指标随时间的变化嵌入到微分流形中，结合攻防效用评估网络的风险值，达到衡量网络安全的效果。以CIC2017数据集为例进行实验，结果表明：该方法可衡量动态攻防过程中产生的风险从而评估网络安全，可为网络安全度量提供一种动态的衡量方式。

Abstract

Network security methods lack effective metrics to measure attack risks and defense capabilities in dynamic networks, especially since they have high dimensionality and are difficult to analyze since there are many indicators. This paper presents a method to quantify network attack and defense capabilities. Clustering and principal component analyses are used to reduce the dimensionality and allocate weights to the indicator groups. These indexes are embedded in differential manifolds that change with time with the network risk evaluated based on the attack risks and defense capabilities to quantify the network security effectiveness. The CIC2017 dataset is used as an example to show that this method can indicate the attach and defense risks for dynamic networks. The results show that this method can provide a dynamic method for network security measurements.

关键词

网络安全度量 / 攻防效用 / 指标降维 / 微分流形

Key words

network security metrics / attack and defense effectiveness / indicator dimension reduction / differential manifold

引用本文

EndNote

Ris (Procite)

Bibtex

导出引用
赵小林, 姜筱奕, 赵晶晶, 徐浩, 郭煚. 基于微分流形的网络攻防效用度量方法[J]. 清华大学学报（自然科学版）. 2020, 60(5): 380-385 https://doi.org/10.16511/j.cnki.qhdxxb.2020.26.002
ZHAO Xiaolin, JIANG Xiaoyi, ZHAO Jingjing, XU Hao, GUO Jiong. Metrics for network attack and defense effectiveness based on differential manifolds[J]. Journal of Tsinghua University(Science and Technology). 2020, 60(5): 380-385 https://doi.org/10.16511/j.cnki.qhdxxb.2020.26.002

参考文献

[1] BURKE D A. Towards a game theory model of information warfare[D]. Dayton:Air force Institute of Technology, 1999.
[2] ZAKERI R, JALILI R, SHAHRIARI H R, et al. Using description logics for network vulnerability analysis[C]//International Conference on Networking, International Conference on Systems and International Conference on Mobile Communications and Learning Technologies. Morne, Mauritius:IEEE, 2006:78-83.
[3] LYE K W, WING J M. Game strategies in network security[J]. International Journal of Information Security, 2005, 4(1-2):71-86.
[4] 尹发, 艾中良. 基于攻防行动链的网络对抗推演系统[J]. 计算机与现代化, 2019(2):107-111, 117.YIN F, AI Z L. Network confrontation deduction system based on offensive and defensive action chain[J]. Computer and Modernization, 2019(2):107-111, 117. (in Chinese)
[5] 姜伟, 方滨兴, 田志宏, 等. 基于攻防博弈模型的网络安全测评和最优主动防御[J]. 计算机学报, 2009, 32(4):817-827.JIANG W, FANG B X, TIAN Z H, et al. Evaluating network security and optimal active defense based on attack-defense game model[J]. Chinese Journal of Computers, 2009, 32(4):817-827. (in Chinese)
[6] 张恒巍, 李涛, 黄世锐. 基于攻防微分博弈的网络安全防御决策方法[J]. 电子学报, 2018, 46(6):151-158.ZHANG H W, LI T, HUANG S R. Network defense decision-making method based on attack-defense differential game[J]. Acat Electronica Sinica, 2018, 46(6):151-158.(in Chinese)
[7] 鲜明, 包卫东, 王永杰, 等. 网络攻击效果评估导论[M]. 长沙:国防科技大学出版社, 2007.XIAN M, BAO W D, WANG Y J, et al. Introduction to network attack effectiveness evaluation[M]. Changsha:National Defense Science and Technology University Press, 2007.(in Chinese)
[8] 王永杰, 鲜明, 王国玉, 等. 计算机网络攻击效能评估研究[J]. 计算机工程与设计, 2005, 26(11):2868-2870, 2901.WANG Y J, XIAN M, WANG G Y, et al. Study on effectiveness evaluation of computer network attacks[J]. Computer Engineering and Design, 2005, 26(11):2868-2870, 2901. (in Chinese)
[9] 邹海洋. 基于灰色系统理论的网络攻击效果评估[J]. 电脑知识与技术, 2011, 7(4):795-799.ZOU H Y. Evaluation of the attack effect of the network based on grey system theory[J]. Computer Knowledge and Technology, 2011, 7(4):795-799. (in Chinese)
[10] 胡昌振, 刘臻, 单纯, 等. 基于微分流形的网络状态模型构建方法和状态评估方法:2018106493246[P]. 2018-12-11.HU C Z, LIU Z, SHAN C, et al. Differential manifold-based network state model construction and state assessment:2018106493246[P]. 2018-12-11. (in Chinese)
[11] HU C Z. Calculation of the behavior utility of a network system:Conception and principle[J]. Engineering, 2018, 4(1):78.
[12] 赵小林, 陈全保, 薛静锋, 等. 一种基于Lie群的网络系统风险度量方法:CN108777641A[P]. 2018-11-19.ZHAO X L, CHEN Q B, XUE J F, et al. A network system risk measurement method based on Lie group:CN108777641A[P]. 2018-11-19. (in Chinese)
[13] IRVINE. KDD Cup 1999 Data[DS/OL].[2019-08-10]. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.
[14] Canadian Institute for Cybersecurity. Intrusion detection evaluation dataset (CICIDS2017).[DS/OL].[2019-08-10]. https://www.unb.ca/cic/.
[15] LEE J M. Smooth manifolds[M]. New York:Springer, 2013.
[16] 史忠植, 胡宏. 基于微分流形的神经计算[J]. 计算机研究与发展, 1994, 31(8):28-32.SHI Z Z, HU H. Neural computing based on differential manifold[J]. Computer Research and Development, 1994, 31(8):28-32. (in Chinese)

PDF(880 KB)

Accesses

Citation

Detail
段落导航
相关文章
扫码关注，本刊微信公众号
地址：清华大学东门外学研大厦B座908室　
邮编：100084
电话：010-62788108/62771385/62792976/62792994　
E-mail：xuebaost@tsinghua.edu.cn
访问统计
    总访问量
    今日访问
    在线人数
　
版权所有 © 《清华大学学报（自然科学版）》编辑部

/

〈 〉