计算机科学 ›› 2018, Vol. 45 ›› Issue (10): 138-141.doi: 10.11896/j.issn.1002-137X.2018.10.026

• 信息安全 • 上一篇    下一篇

基于模糊博弈规则的网络节点入侵风险评估

刘建峰1, 陈健2   

  1. 南京大学网络信息中心 南京210023 1
    南京大学计算机科学与技术系 南京210023 2
  • 收稿日期:2017-08-14 出版日期:2018-11-05 发布日期:2018-11-05
  • 作者简介:刘建峰(1979-),男,硕士,工程师,主要研究方向为网络架构、IPV6、安全、大数据分析,E-mail:ljf@nju.edu.cn(通信作者);陈 健(1973-),男,硕士,高级工程师,主要研究方向为计算机网络、计算机网络安全。
  • 基金资助:
    国家下一代互联网信息安全专项试点计划课题(2012BAH01B01)资助

Evaluation of Network Node Invasion Risk Based on Fuzzy Game Rules

LIU Jian-feng1, CHEN Jian2   

  1. Network Information Center,Nanjing University,Nanjing 210023,China 1
    Department of Computer Science and Technology,Nanjing University,Nanjing 210023,China 2
  • Received:2017-08-14 Online:2018-11-05 Published:2018-11-05

摘要: 为了实时评估网络安全状态,弥补传统网络节点入侵风险评估方法评估精度低、实用性差的不足,提出一种新的基于模糊博弈规则的网络节点入侵风险评估方法。该方法通过一组有限状态集合对网络进行描述,给出博弈双方的收益矩阵和模糊博弈元素,获取入侵者和网络节点的预期收益,在此基础上给出模糊博弈规则;通过模糊博弈规则,依据资产、威胁、弱点以及风险要素构建风险评估模型;完成策略成本与收益的量化处理后,建立网络节点模糊博弈树,求出纳什均衡;结合入侵者和网络节点的收益函数,获取模糊博弈规则下网络节点风险期望,确定网络节点入侵风险值,并依据阈值判断是否需报警,以防止网络节点被入侵。实验结果表明,所提方法的评估精度高、可靠性和实用性强。

关键词: 风险, 模糊博弈规则, 评估, 入侵, 网络节点

Abstract: In order to evaluate network security state in real time and make up for the shortcomings of low accuracy and poor practicability of traditional network node intrusion risk assessment method,a new network node intrusion risk assessment method based on fuzzy game rules was proposed.A set of finite state sets is used to describe the network,and the benefit matrix and fuzzy game elements are given to obtain the expected income of intruders and network nodes.On this basis,the fuzzy game rules are given.The risk assessment model is constructed according to the assets,threats,weaknesses and risk factors through the fuzzy game rules.After the quantification of the strategy cost and income,the fuzzy game tree of the network node is established,and the nash equilibrium is obtained.Combined with the income function of intruders and network nodes,the expectation of network nodes’ risk under fuzzy game rules is obtained,and the value of network node’s intrusion risk is determined.The threshold value is used to judge whether alarm is needed to prevent the network node from being invaded.Experimental results show that the proposed method has high accuracy,reliability and practicability.

Key words: Evaluation, Fuzzy game rule, Intrusion, Network node, Risk

中图分类号: 

  • TP309
[1]LIU W F,ZHANG S W,GONG X.An Improved Network Risk Evaluation Method Based on Markov Game[J].Telecommunications Science,2014,30(7):13-18.(in Chinese)
刘文芬,张树伟,龚心.一种优化的基于Markov博弈理论的网络风险评估方法[J].电信科学,2014,30(7):13-18.
[2]ZHANG J,WANG J D,ZHANG H W,et al.Network Risk Analysis Method Based on Node-Game Vulnerability Attack Graph[J].Computer Science,2014,41(9):169-173.(in Chinese)
张健,王晋东,张恒巍,等.基于节点博弈漏洞攻击图的网络风险分析方法[J].计算机科学,2014,41(9):169-173.
[3]LEI J G.Simulation of Game Detection under Unbalanced Invasion Characteristics[J].Computer Simulation,2015,32(9):307-310.(in Chinese)
雷剑刚.不平衡网络入侵特征下的博弈检测仿真[J].计算机仿真,2015,32(9):307-310.
[4]LAI C,CHEN X,CHEN X,et al.A fuzzy comprehensive evaluation model for flood risk based on the combination weight of game theory[J].Natural Hazards,2015,77(2):1243-1259.
[5]GUI M Q,LIU Y B, ZHOU L Y.Intrusion detection based on game theory in wireless sensor network[J].Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition),2016,28(3):414-420.(in Chinese)
桂明倩,刘宴兵,周嘹永.WSN中基于博弈理论的入侵检测研究[J].重庆邮电大学学报(自然科学版),2016,28(3):414-420.
[6]REN L C, LI Z F.A New Model Based on the Games Theory and Fuzzy Mathematics in Bridge Engineering Risk Assessment[J].Highway Engineering,2017,42(1):163-169.(in Chinese)
任丽超,栗振锋.基于博弈论和模糊数学的桥梁风险评价模型[J].公路工程,2017,42(1):163-169.
[7]YU D K,WANG J D,ZHANG H W,et al.Risk assessment selection based on static Bayesian game[J].Computer Engineering and Science,2015,37(6):1079-1086.(in Chinese)
余定坤,王晋东,张恒巍,等.基于静态贝叶斯博弈的风险评估方法研究[J].计算机工程与科学,2015,37(6):1079-1086.
[8]XIE Q L.Design of wireless sensor network the sink node based on OK6410[J].Electronic Design Engineering,2016,24(6):159-161.(in Chinese)
谢巧玲.基于OK6410的无线传感器网络汇聚节点设计[J].电子设计工程,2016,24(6):159-161.
[9]HAN L,SONG Y,DUAN L,et al.Risk assessment methodology for Shenyang Chemical Industrial Park based on fuzzy comprehensive evaluation[J].Environmental Earth Sciences,2015,73(9):5185-5192.
[10]SHI L B,JIAN Z.ulnerability Assessment of Cyber Physical Power System Based on Dynamic Attack-defense Game Model[J].Automation of Electric Power Systems,2016,40(17):99-105.(in Chinese)
石立宝,简洲.基于动态攻防博弈的电力信息物理融合系统脆弱性评估[J].电力系统自动化,2016,40(17):99-105.
[11]HUANG L L,YAO A L,XIAN T,et al.Research on risk assessment method of oil & gas pipeline with consideration of vulnerability[J].China Safety Science Journal,2014,24(7):93-99.(in Chinese)
黄亮亮,姚安林,鲜涛,等.考虑脆弱性的油气管道风险评估方法研究[J].中国安全科学学报,2014,24(7):93-99.
[12]ZHANG H W,ZHANG J,HAN J H,et al.Vulnerability risk analysis method based on game model and risk matrix[J].Computer Engineering and Design,2016, 37(6):1421-1427.(in Chinese)
张恒巍,张健,韩继红,等.基于博弈模型和风险矩阵的漏洞风险分析方法[J].计算机工程与设计,2016,37(6):1421-1427.
[13]ZHANG Y.Research on the computer network security evaluation based on the DHFHCG operator with dual hesitant fuzzy information[J].Journal of Intelligent & Fuzzy Systems,2015,28(1):199-204.
[14]XI R R,YUN X C,ZHANG Y Z,et al.An Improved Quantitative Evaluation Method for Network Security[J].Chinese Journal of Computers,2015,38(4):749-758.(in Chinese)
席荣荣,云晓春,张永铮,等.一种改进的网络安全态势量化评估方法[J].计算机学报,2015,38(4):749-758.
[15]SONG Y U,CHENE J.Research of Aircraft Maintenance Unit Risk Management Based on the Generalized Linear Regression Model[J].Bulletin of Science and Technology,2016,32(1):215-219.(in Chinese)
宋云雪,陈金.基于广义线性回归模型的飞机维修单位风险管理研究[J].科技通报,2016,32(1):215-219.
[16]DAI W.Application of Intrusion Detection Technology in Network Security[J].Journal of Chongqing Institute of Technology,2018,32(4):156-160,185.(in Chinese)
代威.入侵检测技术在网络安全中的应用[J].重庆理工大学学报(自然科学),2018,32(4):156-160,185.
[1] 傅彦铭, 朱杰夫, 蒋侃, 黄保华, 孟庆文, 周兴.
移动众包中基于多约束工人择优的激励机制研究
Incentive Mechanism Based on Multi-constrained Worker Selection in Mobile Crowdsourcing
计算机科学, 2022, 49(9): 275-282. https://doi.org/10.11896/jsjkx.210700129
[2] 柳杰灵, 凌晓波, 张蕾, 王博, 王之梁, 李子木, 张辉, 杨家海, 吴程楠.
基于战术关联的网络安全风险评估框架
Network Security Risk Assessment Framework Based on Tactical Correlation
计算机科学, 2022, 49(9): 306-311. https://doi.org/10.11896/jsjkx.210600171
[3] 王馨彤, 王璇, 孙知信.
基于多尺度记忆残差网络的网络流量异常检测模型
Network Traffic Anomaly Detection Method Based on Multi-scale Memory Residual Network
计算机科学, 2022, 49(8): 314-322. https://doi.org/10.11896/jsjkx.220200011
[4] 吴功兴, 孙兆洋, 琚春华.
考虑中断风险与模糊定价的闭环供应链网络设计模型
Closed-loop Supply Chain Network Design Model Considering Interruption Risk and Fuzzy Pricing
计算机科学, 2022, 49(7): 220-225. https://doi.org/10.11896/jsjkx.201100084
[5] 高春刚, 王永杰, 熊鑫立.
MTDCD:一种对抗网络入侵的混合防御机制
MTDCD:A Hybrid Defense Mechanism Against Network Intrusion
计算机科学, 2022, 49(7): 324-331. https://doi.org/10.11896/jsjkx.210600193
[6] 张洪博, 董力嘉, 潘玉彪, 萧宗志, 张惠臻, 杜吉祥.
视频理解中的动作质量评估方法综述
Survey on Action Quality Assessment Methods in Video Understanding
计算机科学, 2022, 49(7): 79-88. https://doi.org/10.11896/jsjkx.210600028
[7] 周志豪, 陈磊, 伍翔, 丘东亮, 梁广升, 曾凡巧.
基于SMOTE-SDSAE-SVM的车载CAN总线入侵检测算法
SMOTE-SDSAE-SVM Based Vehicle CAN Bus Intrusion Detection Algorithm
计算机科学, 2022, 49(6A): 562-570. https://doi.org/10.11896/jsjkx.210700106
[8] 曹扬晨, 朱国胜, 孙文和, 吴善超.
未知网络攻击识别关键技术研究
Study on Key Technologies of Unknown Network Attack Identification
计算机科学, 2022, 49(6A): 581-587. https://doi.org/10.11896/jsjkx.210400044
[9] 徐佳楠, 张天瑞, 赵伟博, 贾泽轩.
面向供应链风险评估的改进BP小波神经网络研究
Study on Improved BP Wavelet Neural Network for Supply Chain Risk Assessment
计算机科学, 2022, 49(6A): 654-660. https://doi.org/10.11896/jsjkx.210800049
[10] 朱旭辉, 沈国娇, 夏平凡, 倪志伟.
基于螺旋进化萤火虫算法和BP神经网络的模型及其在PPP融资风险预测中的应用
Model Based on Spirally Evolution Glowworm Swarm Optimization and Back Propagation Neural Network and Its Application in PPP Financing Risk Prediction
计算机科学, 2022, 49(6A): 667-674. https://doi.org/10.11896/jsjkx.210800088
[11] 王宇飞, 陈文.
基于DECORATE集成学习与置信度评估的Tri-training算法
Tri-training Algorithm Based on DECORATE Ensemble Learning and Credibility Assessment
计算机科学, 2022, 49(6): 127-133. https://doi.org/10.11896/jsjkx.211100043
[12] 魏辉, 陈泽茂, 张立强.
一种基于顺序和频率模式的系统调用轨迹异常检测框架
Anomaly Detection Framework of System Call Trace Based on Sequence and Frequency Patterns
计算机科学, 2022, 49(6): 350-355. https://doi.org/10.11896/jsjkx.210500031
[13] 刘林云, 陈开颜, 李雄伟, 张阳, 谢方方.
基于卷积神经网络的旁路密码分析综述
Overview of Side Channel Analysis Based on Convolutional Neural Network
计算机科学, 2022, 49(5): 296-302. https://doi.org/10.11896/jsjkx.210300286
[14] 鹿婷, 侯国家, 潘振宽, 王国栋.
基于HVS的水下图像质量评价
Underwater Image Quality Assessment Based on HVS
计算机科学, 2022, 49(5): 98-104. https://doi.org/10.11896/jsjkx.210100224
[15] 储安琪, 丁志军.
基于灰狼优化算法的信用评估样本均衡化与特征选择同步处理
Application of Gray Wolf Optimization Algorithm on Synchronous Processing of Sample Equalization and Feature Selection in Credit Evaluation
计算机科学, 2022, 49(4): 134-139. https://doi.org/10.11896/jsjkx.210300075
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!