南京大学学报(自然科学版) ›› 2017, Vol. 53 ›› Issue (2): 350–.

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一种新的基于节点重要性的免疫策略研究

刘振杰1,2,赵 姝1,2*,陈 洁1,2,张燕平1,2,陈 喜1,2   

  • 出版日期:2017-03-26 发布日期:2017-03-26
  • 作者简介:1.安徽大学计算机科学与技术学院,合肥,230601;2.安徽大学协同创新中心,合肥,230601
  • 基金资助:
    基金项目:国家高技术研究发展计划(“863”计划)(2015AA124102),国家自然科学基金(61402006,61175046),安徽省自然科学基金(1508085MF113),安徽省高等学校省级自然科学基金重点项目(KJ2013A016),教育部留学回国人员科研启动基金(第49批)收稿日期:2016-10-26*通讯联系人,E-mail:zhaoshuzs2002@hotmail.com

A novel immune strategy based on node importance

Liu Zhenjie1,2,Zhao Shu1,2*,Chen Jie1,2,Zhang Yanping1,2,Chen Xi1,2   

  • Online:2017-03-26 Published:2017-03-26
  • About author:1.School of Computer Science and Technology,Anhui University,Hefei,230601,China;2.Center of Information Support and Assurance Technology,Anhui University,Hefei,230601,China

摘要: 为了抑制病毒在网络中快速爆发,快速有效的免疫策略将有助于减少病毒带来的巨大损失,随机免疫、目标免疫、熟人免疫以及多种改进的免疫策略已经被提出.目前基于节点重要性的免疫策略主要关注该节点的度大小,而忽略了与其相邻的不同节点的重要性并不相同.基于节点的重要性提出一种改进的免疫策略——基于节点度与聚类系数的病毒免疫算法(Virus immunization based on degree and clustering coefficient of node,IDCC).通过考虑节点的度信息和与其邻居节点间的连接紧密程度计算节点重要性,选择用聚类系数表示连接紧密程度,并计算节点的度大小与聚类系数之和,选择和值较大的节点进行免疫.在人工合成网络和真实的大学邮件网络实现免疫模型并记录感染的节点数目.实验结果表明,使用IDCC免疫策略后,更能抑制病毒传播,且在免疫比例低于20%时,IDCC免疫策略效率最高.

Abstract: In order to effectively restrain the rapid propagating of virus on the network,fast and effective immune strategies are studied to reduce the losses brought by virus.Random immunization,targeted immunization,and acquaintance immunization as well as other improved immune strategies have been proposed.Under current circumstances,immune strategy based on the importance of node mainly focuses on its degree but ignores the fact that the importance of different neighbor node is not the same.Based on the importance of the node,the paper proposes a novel immune strategy,named virus immunization based on degree and clustering coefficient of node(IDCC).IDCC Algorithm not only calculate the node importance by completely considering the total information of node degree,but also compute the closeness of how it connect with the adjacent node.The paper chooses clustering coefficient to show the closeness and sum the degree and clustering coefficient,of which the max is chosen to conduct immunization.So as to better prove the validity of the experiment,immunization model is implemented in the synthetic network as well as real university mail network and the amount of infectious nodes is recorded.The experiment result shows that IDCC can better restrain the virus propagate and exerts the highest efficiency when the immunization proportion is below 20%.

[1] Sophia.2015年度中国互联网站安全报告 安全漏洞频发 网络攻击行为加剧.信息安全与通信保密,2016(2):1-3.(Sophia.2015 annual China Internet website security vulnerabilities frequent attacks on network attacks.Information Security and Communication Security,2016(2):1-3.)[2] 张 健,梁 宏,陈建民等.计算机病毒危害性的评估.信息网络安全,2005(1):39-41.(Zhang J,Liang H,Chen J M,et al.Evaluation of computer viruses.Information Network Security,2005(1):39-41.)[3] Pastor?Satorras R,Vespignani A.Epidemic spreading in scale?free networks.Physical Review Letters,2001,86:3200-3203.[4] Eguiluz V M,Klemm K.Epidemic threshold in structured scale?free networks.Physical Review Letters,2002,89:108701.[5] Moore C,Newmanm E J.Epidemics and percolation in small world network.Physical Review E,2000,61:5678-5682.[6] Cliff C Z,Towsley D,Gong W.Modeling and simulation study of the propagation and defense of internet email worm.IEEE Transaction on Dependable and Secure Computing,2007,4(2):105-118.[7] Gallos L K,Liljeros F,Argyrakis P,et al.Improving immunization strategies.Physical Review E(Statistical,Nonlinear,and Soft Matter Physics),2007,75(4 Pt 2):361-369.[8] Cohen R,Havlin S,Ben Avraham D.Efficient immunization strategies for computer networks and populations.Physical Review Letters,2003,91(24):12343.[9] Gomez?Gardenes J,Echenique P,Moreno Y.Immunization of real complex communication networks.European Physical Journal B,2006,49(2):259-264.[10] Gao C,Liu J,Zhong N.Network Immunization with distributed autonomy?oriented entities.IEEE Transactions on Parallel and Distributed Systems,2011,22(7):1222-1229.[11] 高 超,刘际明,钟 宁等.邮件网络中基于介数的免疫策略研究.计算机工程,2010,36(5):18-20.(Gao C,Liu J M,Zhong N,et al.Research on immune strategy based on the number of media in mail network.Computer Engineering,2010,36(5):18-20.)[12] Echenique P,Gomez G J,Moreno Y,et al. Distance?D covering problem in scale?free networks with degree correlation.Physical Review E,2005,71(3 Pt 2A):035102.[13] 林 兵,郭文忠,陈国龙等.无标度网络中基于最短路径免疫策略的病毒传播研究.计算机科学,2012(B06):136-138.(Lin B,Guo W Z,Chen G L,et al.Study of virus transmission based on the shortest path immunization strategy in the scale free network.Computer Science,2012(B06):136-138.)[14] 方宝平.复杂网络的病毒传播及免疫策略.博士学位论文.安徽:安徽大学,2011.(Fang B P.Virus transmission and immunization strategies in complex networks.Ph.D.Dissertation.Anhui:Anhui University,2011.)[15] 汪小帆,李 翔,陈关荣.复杂网络理论及其应用.北京:清华大学出版社,2006,1-99.(Wang X F,Li X,Chen G R.Complex network theory and its application.Beijing:Tsinghua University Press,2006,1-99.)[16] Bu T,Towsley D.On distinguishing between internet power law topology generators.In:Lee D,Orda A.Proceedings of the 21st Annual Joint Conference of the IEEE Computer and Communications Societies(INFOCOM’02).NewYork:IEEE,2002:638-647.[17] 李向华,王 欣,高 超.复杂网络免疫策略分析.吉林大学学报(理学版),2013,51(3):444-452.(Li X H,Wang X,Gao C.Analysis of immune strategies in complex networks.Journal of Jilin University(Science Edition),2013,51(3):444-452.)
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