P2P网络中被动型蠕虫传播与免疫建模

doi:10.3969/j.issn.0372-2112.2013.05.009

电子学报 ›› 2013, Vol. 41 ›› Issue (5): 884-889.DOI: 10.3969/j.issn.0372-2112.2013.05.009

P2P网络中被动型蠕虫传播与免疫建模

冯朝胜^1,2,3, 秦志光², 袁丁¹, 卿昱³

1. 四川师范大学计算机科学学院,可视化计算与虚拟现实四川省重点实验室,四川成都 610101;
2. 电子科技大学计算机科学与工程学院,四川成都 610054;
3. 中国电子科技集团公司第30研究所,四川成都 610041

收稿日期:2011-01-27 修回日期:2013-03-13 出版日期:2013-05-25
- 作者简介:
- 冯朝胜 男,1971年生于四川广元,博士后,副教授,硕士生导师,中国计算机协会高级会员.2010年获得电子科技大学信息与通信工程博士学位.研究方向为分布式计算、网络与信息安全、恶意代码分析. E-mail:csfenggy@126.com
- 基金资助:
- 国家科技重大专项课题 (No.2011ZX03002-002-03); 国家自然科学基金 (No.60873075); 可视化计算与虚拟现实四川省重点实验室课题 (No.PJ2012002); 广东省产学研重点项目 (No.2012B091000054)

Modeling Propagation and Immunization of Passive Worms in Peer-to-Peer Networks

FENG Chao-sheng^1,2,3, QIN Zhi-guang², YUAN Ding¹, QING Yu³

1. School of Computer Science,Sichuan Normal University,Chengdu,Sichuan 610101,China;
2. School of Computer Science & Engineering,University of Electronic Science and Technology of China,Chengdu,Sichuan 610054,China;
3. The No.30 institute of China Electronic Technology Corporation,Chengdu,Sichuan 610041,China

Received:2011-01-27 Revised:2013-03-13 Online:2013-05-25 Published:2013-05-25
- Supported by:
- Subject of National Science and Technology Major Project of the Ministry of Science and Technology (No.2011ZX03002-002-03); National Natural Science Foundation of China (No.60873075); Visual Computing and Virtual Reality Key Laboratory of Sichuan Province (No.PJ2012002); Key Project of Industry-university-research Integration program of Guangdong Provicne (No.2012B091000054)

摘要/Abstract

摘要： 鉴于被动型蠕虫的危害性,对被动型蠕虫进行了深入分析,进而基于平均场法建立了被动型蠕虫的传播模型和免疫模型.基于传播模型和流行病传播学理论推导出进入无蠕虫平衡状态的充分条件,仿真实验证明了该充分条件的正确性.另外,仿真实验还表明,下载率和恢复率是控制蠕虫传播的两个可控的关键参数.在免疫软件被编制出来前,降低下载率和提高恢复率能有效控制被动型蠕虫的传播.

关键词: 被动型蠕虫, P2P文件共享网, 传播, 免疫, 建模, 仿真

Abstract: In this paper,we identified the features of passive worm.Further the models of propagation and immunization of passive worms are proposed in the mean-field methods.Based on the model of worm propagation and Epidemiology,the sufficient condition for the global stability of the worm free equilibrium is deduced.Simulations validate the condition.Both the sufficient condition and all the experiment results show that amongst all P2P-related factors having effect on passive wormpropagation,attack performance of passive worms is most sensitive to two P2P system parameters:the download rate and the recovery rate.Controlling the two parameters,i.e.decreasing the download rate and increasing the recovery rate,provides an effective means for throttling the spread of passive worms.

Key words: passive worms, P2P networks, propagation, immunization, modeling, Simulations

中图分类号:

TP393

冯朝胜, 秦志光, 袁丁, 等. P2P网络中被动型蠕虫传播与免疫建模[J]. 电子学报, 2013, 41(5): 884-889.

FENG Chao-sheng, QIN Zhi-guang, YUAN Ding, et al. Modeling Propagation and Immunization of Passive Worms in Peer-to-Peer Networks[J]. Acta Electronica Sinica, 2013, 41(5): 884-889.

参考文献

[1] 夏春和,石昀平,李肖坚.结构化对等网中的P2P蠕虫传播模型研究[J].计算机学报,2006,29(7):952-959. Xia Chun-he,Shi Yun-ping,Li Xiao-jian.Research on epidemic modesl of P2P worms in structured peer-to-peer networks[J].Chinese Journal of Computers,2006,29(7):952-959.(in Chinese)
[2] Chen G,Gray R S.Simulating non-scanning worms on peer-to-peer networks .Proc of the 1st Int Conf on Scalable Information Systems .Hong Kong:ACM,2006.
[3] Zhou L,Zhang L,McSherry F,et al.A first look at peer-to-peer worms:Threats and defenses .Proc of the 4th Int Workshop on Peer-to-Peer Systems .New York:Springer,2005.24-35.
[4] Nassima K,Yannick C,Nazim A.The emerging threat of peer-to-peer worms .Proc of IEEE / IST Workshop on Monitoring,Attack Detection and Mitigation .Tuebingen,Germany:IEEE,2006.18-20.
[5] Frauenthal J C.Mathematical Modeling in Epidemiology[M].New York:Springer,1980.
[6] Driessche P,Watmough J.Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission[J].Mathematical Biosciences,2002,180:29-48.
[7] Arnio J,Davis J,Hartley D,et al.A multi-species epidemic model with spatial dynamics[J].Mathematical Medicine and Biology,2005.
[8] Diekmann O,Heesterbeek J A P.Mathematical Epidemiology of Infectious Diseases:Model Builind,Analysis and Interpretation[M].Wiley,1999.
[9] Thommes R W,Coates M J.Modeling Virus Propagation in Peer-to-Peer Networks .Montreal,Canada:Department of Electrical and Computer Engineering,McGill University,2005.
[10] Murray W H.The application of epidemiology to computer viruses[J].Computers and Security,1988,7:130-150.
[11] Kephart J O,White S R.Directed-graph epidemiological models of computer viruses .Proc of IEEE Symp.Security and Privacy .Oakland :IEEE,1991.343-359.
[12] Ripeanu M.Peer-to-peer architecture case study:Gnutella network .Proc of the First International Conference on Peer-to-Peer Computing .Linkoping,Sweden:IEEE,2001.
[13] Sen S,Wang J.Analyzing peer-to-peer traffic across large networks[J].IEEE/ACM Transactions on Networking,2004,12(2):219-232.
[14] Stutzbach D,Rejaie R,Sen S.Characterizing unstructured overlay topologies in modern P2P file-sharing systems . Proc of the Fifth ACM Internet Measurement Conference .Berkeley:ACM,2005.49-62.
[15] Pastor-Satorras R,Vespignani A.Epidemic spreading in scale-free networks[J].Physical Review Letters,2001,86(14):3200-3203.
[16] Staniford S,Paxson V,Weaver N.How to Own the Internet in Your Spare Time .Proc of the 11th USENIX Security Symposium .San Francisco:ACM,2002.149-167.
[17] Dumitriu D,Knightly E,Kuzmanovic A,et al.Denial-of-service resilience in peer-to-peer file-sharing systems .Proc of ACM Sigmetrics .Banff,Canada:ACM,2005.
[18] 王方伟,张运凯,马剑峰.无结构P2P网络中被动型蠕虫传播建模和防治[J].天津大学学报,2008,14(1):66-72. Wang Fang-wei,Zhang Yun-kai,Ma Jian-feng.Modeling and defending passive worms over unstructured peer-to-peer networks[J].Transaction of Tianjin University,2008,14(1):66-72.(in Chinese)
[19] 应凌云,冯登国,苏璞睿.基于P2P 的僵尸网络及其防御[J].电子学报,2009,37(1):31-37. Ying Ling-yun,Feng Deng-guo,Su Pu-rui.P2P-based super botnet:Threat s and defenses[J].Acta Electronica Sinica,2009,37(1):31-37.(in Chinese)

P2P网络中被动型蠕虫传播与免疫建模

Modeling Propagation and Immunization of Passive Worms in Peer-to-Peer Networks

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	王神龙, 雍宇, 吴晨睿. 基于伪孪生神经网络的低纹理工业零件6D位姿估计[J]. 电子学报, 2023, 51(1): 192-201.
[2]	邓海刚, 王传旭, 李成伟, 林晓萌. 深度学习框架下群组行为识别算法综述[J]. 电子学报, 2022, 50(8): 2018-2036.
[3]	任莎莎, 刘琼. 小目标特征增强图像分割算法[J]. 电子学报, 2022, 50(8): 1894-1904.
[4]	江兵兵, 何文达, 吴兴宇, 项俊浩, 洪立斌, 盛伟国. 基于自适应图学习的半监督特征选择[J]. 电子学报, 2022, 50(7): 1643-1652.
[5]	罗忠涛, 严美慧, 卢琨, 夏杭. 超视距雷达中射频干扰仿真与距离-多普勒图检测方法[J]. 电子学报, 2022, 50(5): 1174-1180.
[6]	朱永鑫, 徐权, 陈墨, 侯莉萍, 包伯成. 自适应突触神经元简化模型的动力学与电路实现[J]. 电子学报, 2022, 50(5): 1083-1088.
[7]	王鼎, 尹洁昕, 郑娜娥, 聂福全. 信号传播速度未知下基于运动单站到达频率的定位新方法[J]. 电子学报, 2022, 50(5): 1181-1191.
[8]	宋唐益嘉, 胡鹤飞, 张思睿, 许凌毅, 辛宁. 天基信息港对等共享网络性能仿真[J]. 电子学报, 2022, 50(5): 1266-1269.
[9]	姜斌, 唐禹, 包建荣, 唐向宏, 刘超. 水声信道频域Turbo均衡的期望传播改进算法[J]. 电子学报, 2022, 50(3): 652-662.
[10]	王刚, 陆世伟, 冯云, 刘文斌, 马润年. 网络节点增减下的潜伏型病毒传播行为建模研究[J]. 电子学报, 2022, 50(2): 273-283.
[11]	史宏强, 李岳衡, 黄平, 谭跃跃, 刘陕陕, 居美艳. 基于APD与PIN接收机的UWOC系统误码率性能优化研究[J]. 电子学报, 2022, 50(2): 446-454.
[12]	廖希, 黄晨曦, 王洋, 车延庭. 室内毫米波3D-MIMO无线信道参数提取与多径簇特性研究[J]. 电子学报, 2022, 50(11): 2698-2706.
[13]	文溢, 陈建军, 梁斌, 池雅庆, 黄俊. 28nm CMOS工艺8-Gbps SerDes单粒子辐射特性研究[J]. 电子学报, 2022, 50(11): 2653-2658.
[14]	赵雄文, 张钰, 秦鹏, 王晓晴, 耿绥燕, 宋俊元, 刘瑶, 李思峰. 空天地一体化无线光通信网络关键技术及其发展趋势[J]. 电子学报, 2022, 50(1): 1-17.
[15]	王相海, 宋若曦, 曲思洁, 穆振华, 宋传鸣. 图像多尺度几何分析域隐马尔可夫树模型研究进展[J]. 电子学报, 2022, 50(1): 238-249.