基于社会计算的网络恶意代码防护机制研究
|
摘要
互联网已经成为人们日常生活不可或缺的一部分，网络用户应用各种网络服务获取信息与资源，并与其他网络用户交互。但蠕虫、木马和病毒等网络恶意代码通常通过恶意网页、即时通信网络、P2P网络等途径快速传播，盗取用户敏感信息，破坏用户数据，对用户主机和网络造成严重威胁。
为了抑制恶意代码传播，本文提出了一个基于社会计算的恶意代码网络防护机制。从终端用户的角度，以用户社会关系为基础形成个体中心网络，利用社会计算和人计算融合多个用户掌握的安全经验和多种安全软件的检测结果，应用群体智慧动态感知恶意代码态势，预先采取防护措施。在微观上，用户利用个体中心网络中多个好友的智慧增强用户主机对抗恶意代码的能力；在宏观上，网络用户之间互相协作形成一个网状的防护体系，能够提高整个网络的安全程度。
将多个即时通信工具整合到一起构成综合即时通信工具，作为社会网络平台部署网络防护以实现用户间的实时相互协作。根据恶意代码的几个重要传播途径分别采取不同的网络防护子机制，各子机制之间相互依存，且紧密相关，形成一个统一防护整体。具体研究内容包括：
1.提出了一个动态信任值算法。根据用户对统一好友列表中每个用户的直接信任值以及个体中心网络中的信任链计算间接信任值，随着网络社会关系的演化，应用用户之间的交互信息和信任的传递计算动态信任值，为恶意代码网络防护机制提供进行社会计算的基础。
2.针对恶意网页的威胁，提出了一种基于社会计算的分布式恶意网页协作防护机制。该机制结合第三方专业服务机构提供的恶意网址列表，利用社会网络中好友间的动态信任获取好友对网页的评价信息，并融合好友的安全浏览经验形成网页综合评价。每个用户都与其好友进行协作，形成一个网状的恶意网页防护体系，减少恶意网页的访问量。
3.针对通过即时通信工具传播的恶意代码，提出了一种基于社会计算的IM蠕虫防护机制。该机制以综合即时通信工具为平台，在IM客户端部署方案，利用用户与好友之间的动态社会信任关系，通过社会计算和人计算，融合网络中多种反病毒软件的检测结果及用户的安全经验，形成群体智慧实施本地主机防护。用户之间实时相互协作，抵御通过即时通信工具传播的恶意代码，构成分布式IM蠕虫协作防护机制。
4.提出了一个安全P2P网络构建机制。针对利用节点邻居列表传播的P2P蠕虫，采用双邻居列表对其进行围堵，根据节点抵抗蠕虫的能力为其选择邻居，使得P2P网络中的节点分布更利于抵抗蠕虫的攻击，并将该邻居选择机制应用到无结构P2P网络KaZaA中。进一步，用一个携带目标节点列表（Hitlist）的良性蠕虫清除P2P网络中的恶意蠕虫并对存在漏洞的节点进行补丁修补，良性蠕虫的传播过程同时形成分布式自动补丁过程。针对P2P网络中大量节点存在的漏洞，利用基于社会计算的自动补丁机制进行修补：安全服务器生成自动补丁，选择存在漏洞的节点投放自动补丁，然后自动补丁沿着社会网络快速传播，从而使得P2P网络中被社会网络覆盖的易感染节点被修补。为节点选择合适的邻居能够显著降低P2P节点的感染率，利用良性蠕虫能够清除恶意蠕虫并修复存在漏洞的节点，利用社会网络传播自动补丁能够为大量存在漏洞的P2P节点进行修补，从而提高整个P2P网络的安全程度。
恶意网页协作防护、IM蠕虫协作防护和安全P2P网络三个子机制之间相互渗透、支持和协作，形成不可分割的整体对抗各种恶意代码，增强整个网络的防护性能。
Internet gradually becomes a necessary part of our lives. Users are able to obtaininformation and resources, and interact with others through network services. Butmalicious codes such as worms, Trojans and viruses propagate quickly throughmalicious Web pages, instant messaging and P2P network, etc. And they are able tosteal sensitive information and destroy user data. The hosts and the network arethreatened seriously by various malicious codes.
To restrain the propagation of malicious codes, a defending scheme based onsocial computing is proposed in this dissertation. To a user, the egocentric network inwhich the user is the centre is formed by social relationships between him and otherusers. Social computing and human computation are utilized to make a fusion forWeb surfing experiences and security software checking results from other users toproduce collective intelligence which can dynamically perceive network securitysituation about malicious codes. And then the user can take some measures to protectthe host in advance. On the micro level, a user can utilize the collective intelligencefrom friends in his egocentric network to improve his ability to counter maliciouscodes. On the macro level, a meshy collaborative defending scheme is formed bycollaborations among online users, which can improve the security of the wholenetwork.
The synthetical social network formed by various IM (instant messaging) toolsis used as the platform to deploy network defending in the scheme, by which userscan collaborate with each other. Several different network defending schemes aretaken according to various propagation ways of malicious codes. All the schemes aredepended on each other, and collaborate closely to form an integrated defendingsystem. The main work can be summarized as follows.
An algorithm on dynamic trust is proposed. In the algorithm, indirect trustvalues are calculated depending on direct trust values and trust chains in theegocentric network. As the relationships evolve, the dynamic trust values can be resulted from the interactions between user and his friends and trust transmission,which is the basis of the social computing-based defending system against maliciouscodes.
To deal with the threat of malicious Web pages, a distributed defending schemeagainst malicious Web pages based on social computing is proposed. Besides themalicious URL list from third-party professional organizations, the dynamic trustbetween a certain user and his friends in social network is used to obtain evaluationsof Web pages. The experiences about Web surfing from his friends are collected toresult in synthetical evaluations on the local host. Each user is able to cooperate withhis friends, so that a meshy defending system is formed on overall perspective,which can reduce the visits of malicious Web pages.
To restrain malicious codes spread through IM tools, a collaborative defendingscheme against IM worms based on social computing is proposed. The platform ofthe scheme is the synthetical IM tool. At a certain IM client, through utilizing thedynamic trusts between users, security experiences about Web pages and scanningresults from trusted users are merged together by social computing and humancomputation to produce collective intelligence and defend at the local host. Users cancooperate with their friends instantaneously to resist malicious codes which propagatethrough IM tools. Therefore, a distributed defending scheme against IM worms isconstructed.
A scheme for constructing a secure P2P network is proposed. A double neighborlist is used to contain P2P worms which exploit the neighbor list on peers. Theneighbors of a peer are selected according to their resistance ability against worms,which make the node distribution in P2P network is more advantageous to resistworm attacks. And the neighbor selecting scheme is applied to the unstructured P2Psystem KaZaA. Next, the benign worm with a hitlist is generated to clean thecorresponding malicious worm and patch vulnerable peers in the P2P network. Thespread of the benign worm is also a distributed patching process. For the vulnerabilitywhich makes most peers vulnerable to the worm, an automatic patching based onsocial computing is proposed to deal with such kind of worms. The security server inP2P network can generate automatic patch to the vulnerability, and place the automatic patch to vulnerable peers selected previously. Then the patch propagates insocial network rapidly. Thus, the vulnerable P2P peers in social network are repaired.The double neighbor list is able to reduce the infected rate of vulnerable peers in P2Pnetworks. The benign worm can clean malicious worms and patch vulnerable peers.The automatic patching in social network can repair a large number peers withvulnerabilities in P2P networks. After that, the P2P network is secure to maliciouscodes.
The three subsystems such as the defending scheme against malicious Webpages, the defending scheme against IM worms and the scheme of constructingsecure P2P network integrate, support and collaborate each other to form an integralwhole system against various malicious codes, and enhance the defendingperformance of the whole network.
为了抑制恶意代码传播，本文提出了一个基于社会计算的恶意代码网络防护机制。从终端用户的角度，以用户社会关系为基础形成个体中心网络，利用社会计算和人计算融合多个用户掌握的安全经验和多种安全软件的检测结果，应用群体智慧动态感知恶意代码态势，预先采取防护措施。在微观上，用户利用个体中心网络中多个好友的智慧增强用户主机对抗恶意代码的能力；在宏观上，网络用户之间互相协作形成一个网状的防护体系，能够提高整个网络的安全程度。
将多个即时通信工具整合到一起构成综合即时通信工具，作为社会网络平台部署网络防护以实现用户间的实时相互协作。根据恶意代码的几个重要传播途径分别采取不同的网络防护子机制，各子机制之间相互依存，且紧密相关，形成一个统一防护整体。具体研究内容包括：
1.提出了一个动态信任值算法。根据用户对统一好友列表中每个用户的直接信任值以及个体中心网络中的信任链计算间接信任值，随着网络社会关系的演化，应用用户之间的交互信息和信任的传递计算动态信任值，为恶意代码网络防护机制提供进行社会计算的基础。
2.针对恶意网页的威胁，提出了一种基于社会计算的分布式恶意网页协作防护机制。该机制结合第三方专业服务机构提供的恶意网址列表，利用社会网络中好友间的动态信任获取好友对网页的评价信息，并融合好友的安全浏览经验形成网页综合评价。每个用户都与其好友进行协作，形成一个网状的恶意网页防护体系，减少恶意网页的访问量。
3.针对通过即时通信工具传播的恶意代码，提出了一种基于社会计算的IM蠕虫防护机制。该机制以综合即时通信工具为平台，在IM客户端部署方案，利用用户与好友之间的动态社会信任关系，通过社会计算和人计算，融合网络中多种反病毒软件的检测结果及用户的安全经验，形成群体智慧实施本地主机防护。用户之间实时相互协作，抵御通过即时通信工具传播的恶意代码，构成分布式IM蠕虫协作防护机制。
4.提出了一个安全P2P网络构建机制。针对利用节点邻居列表传播的P2P蠕虫，采用双邻居列表对其进行围堵，根据节点抵抗蠕虫的能力为其选择邻居，使得P2P网络中的节点分布更利于抵抗蠕虫的攻击，并将该邻居选择机制应用到无结构P2P网络KaZaA中。进一步，用一个携带目标节点列表（Hitlist）的良性蠕虫清除P2P网络中的恶意蠕虫并对存在漏洞的节点进行补丁修补，良性蠕虫的传播过程同时形成分布式自动补丁过程。针对P2P网络中大量节点存在的漏洞，利用基于社会计算的自动补丁机制进行修补：安全服务器生成自动补丁，选择存在漏洞的节点投放自动补丁，然后自动补丁沿着社会网络快速传播，从而使得P2P网络中被社会网络覆盖的易感染节点被修补。为节点选择合适的邻居能够显著降低P2P节点的感染率，利用良性蠕虫能够清除恶意蠕虫并修复存在漏洞的节点，利用社会网络传播自动补丁能够为大量存在漏洞的P2P节点进行修补，从而提高整个P2P网络的安全程度。
恶意网页协作防护、IM蠕虫协作防护和安全P2P网络三个子机制之间相互渗透、支持和协作，形成不可分割的整体对抗各种恶意代码，增强整个网络的防护性能。
Internet gradually becomes a necessary part of our lives. Users are able to obtaininformation and resources, and interact with others through network services. Butmalicious codes such as worms, Trojans and viruses propagate quickly throughmalicious Web pages, instant messaging and P2P network, etc. And they are able tosteal sensitive information and destroy user data. The hosts and the network arethreatened seriously by various malicious codes.
To restrain the propagation of malicious codes, a defending scheme based onsocial computing is proposed in this dissertation. To a user, the egocentric network inwhich the user is the centre is formed by social relationships between him and otherusers. Social computing and human computation are utilized to make a fusion forWeb surfing experiences and security software checking results from other users toproduce collective intelligence which can dynamically perceive network securitysituation about malicious codes. And then the user can take some measures to protectthe host in advance. On the micro level, a user can utilize the collective intelligencefrom friends in his egocentric network to improve his ability to counter maliciouscodes. On the macro level, a meshy collaborative defending scheme is formed bycollaborations among online users, which can improve the security of the wholenetwork.
The synthetical social network formed by various IM (instant messaging) toolsis used as the platform to deploy network defending in the scheme, by which userscan collaborate with each other. Several different network defending schemes aretaken according to various propagation ways of malicious codes. All the schemes aredepended on each other, and collaborate closely to form an integrated defendingsystem. The main work can be summarized as follows.
An algorithm on dynamic trust is proposed. In the algorithm, indirect trustvalues are calculated depending on direct trust values and trust chains in theegocentric network. As the relationships evolve, the dynamic trust values can be resulted from the interactions between user and his friends and trust transmission,which is the basis of the social computing-based defending system against maliciouscodes.
To deal with the threat of malicious Web pages, a distributed defending schemeagainst malicious Web pages based on social computing is proposed. Besides themalicious URL list from third-party professional organizations, the dynamic trustbetween a certain user and his friends in social network is used to obtain evaluationsof Web pages. The experiences about Web surfing from his friends are collected toresult in synthetical evaluations on the local host. Each user is able to cooperate withhis friends, so that a meshy defending system is formed on overall perspective,which can reduce the visits of malicious Web pages.
To restrain malicious codes spread through IM tools, a collaborative defendingscheme against IM worms based on social computing is proposed. The platform ofthe scheme is the synthetical IM tool. At a certain IM client, through utilizing thedynamic trusts between users, security experiences about Web pages and scanningresults from trusted users are merged together by social computing and humancomputation to produce collective intelligence and defend at the local host. Users cancooperate with their friends instantaneously to resist malicious codes which propagatethrough IM tools. Therefore, a distributed defending scheme against IM worms isconstructed.
A scheme for constructing a secure P2P network is proposed. A double neighborlist is used to contain P2P worms which exploit the neighbor list on peers. Theneighbors of a peer are selected according to their resistance ability against worms,which make the node distribution in P2P network is more advantageous to resistworm attacks. And the neighbor selecting scheme is applied to the unstructured P2Psystem KaZaA. Next, the benign worm with a hitlist is generated to clean thecorresponding malicious worm and patch vulnerable peers in the P2P network. Thespread of the benign worm is also a distributed patching process. For the vulnerabilitywhich makes most peers vulnerable to the worm, an automatic patching based onsocial computing is proposed to deal with such kind of worms. The security server inP2P network can generate automatic patch to the vulnerability, and place the automatic patch to vulnerable peers selected previously. Then the patch propagates insocial network rapidly. Thus, the vulnerable P2P peers in social network are repaired.The double neighbor list is able to reduce the infected rate of vulnerable peers in P2Pnetworks. The benign worm can clean malicious worms and patch vulnerable peers.The automatic patching in social network can repair a large number peers withvulnerabilities in P2P networks. After that, the P2P network is secure to maliciouscodes.
The three subsystems such as the defending scheme against malicious Webpages, the defending scheme against IM worms and the scheme of constructingsecure P2P network integrate, support and collaborate each other to form an integralwhole system against various malicious codes, and enhance the defendingperformance of the whole network.
引文
[1] Roberta Bragg, Mark Rhodes-Ousley, Keith Strassberg.网络安全完全手册.北京:电子工业出版社,2005
[2]国家计算机病毒应急处理中心.2010年全国信息网络安全状况与计算机及移动终端病毒疫情调查分析报告.2011.Available from: http://www.antivirus-china.org.cn/
[3] AhnLab.安博士六月社交网站恶意代码攻击报告.2011. Available from: http://www.ahnlabchina.com/article_128.html
[4] Liu P, Shi Y, Francis C. M., et al. Grid Demo Proposal: AntiSpamGrid, IEEE InternationalConference on Cluster Computing, Hong Kong: Dec1,2003
[5] Liu P, Chen G, Ye L, et al. Anti-Spam Grid: A Dynamically Organized Spam FilteringInfrastructure, Proceedings of the5th WSEAS Int. Conf. on Simulation, Modeling andOptimization, Corfu, Greece: August17,2005.61～66
[6]瑞星.瑞星2011年上半年安全报告.2011. Available from: http://www.rising.com.cn/about/news/rising/2011-07-20/9802.html
[7] Mark Zuckerberg officially confirmed that Facebook reached750million active usersmilestone,2011. Available from: http://www.techsnapr.com/2011/07/07/mark-zuckerberg-officially-confirmed-that-facebook-reached-750-million-active-users-milestone/
[8] Lazer D, Pentland A, Adamic L, et al. Computational Social Science. Science,2009,vol.323(6):721～723
[9] Steven SH. Exploring complex networks. Nature,2001, vol.410(6825):268～276
[10]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
[11] Watts D J. Six degrees: the science of a connected age. W. W. Norton&Co.,2004.2
[12] Parameswaran M, Whinston A B. Socail Computing: An Overview, Communications of theAssociation for Information Systems.2007(19):762～780
[13] Wang F. Social computing: Concept s, content s and methods. International Journal ofIntelligent Control and Systems,2004, vol.9(2):91～96
[14] Wang F, Carley K M, Zeng D, et al. Social Computing: From Social Informatics to SocialIntelligence. Intelligent Systems,2007,22(2):79～83
[15] Abdul-Rahman A, Hailes S. Supporting Trust in Virtual Communities, Proceedings of the33rd Hawaii International Conference on System Sciences. Maui,2000.1～9
[16] Dwyer C，Hiltz S R，Passerini K. Trust and privacy concern within social networking sites:A comparison of Facebook and MySpace. Proceedings of the Thirteenth AmericasConference on Information Systems. Keystone, Colorado:2007. paper339(13)
[17] Golbeck J. Weaving a Web of Trust. Science Magazine, AAAS,2008, Vol.321(5896):1640～1641
[18] Kuter U, Golbeck J. SUNNY: A New Algorithm for Trust Inference in Social Networksusing Probabilistic Confidence Models. Proceedings of the Twenty-Second NationalConference on Artificial Intelligence. Vancouver, British Columbia:2007.1377～1382
[19] Wang E. A Survey of Web-based Social Network Trust. ITEC810Information TechnologyProject Unit. ITEC810Final Report,2009
[20] Avesani P, Massa P, Tiella R. Proceedings of the2005Association for ComputingMachinery Symposium on Applied Computing. New York: Association for ComputingMachinery,2005.1589～1593
[21] Abdul-Rahman A, Hailes S. Supporting trust in virtual communities. Proceedings of the33rd Hawaii International Conference on System Sciences, Maui, Hawaii,2000.4～7
[22] Ziegler C N, Lausen G. Analyzing correlation between trust and user similarity in onlinecommunities. Lecture Notes in Computer Science,2004, Vol.2995:251～265
[23] Sinha R, Swearingen K. Comparing recommendations made by online systems and friends.Proceedings of the DELOS-NSF Workshop on Personalization and Recommender Systemsin Digital Libraries Dublin, Ireland,2001
[24] O’Donovan J, Smyth B. Trust in recommender systems. IUI’05: Proceedings of the10thInternational Conference on Intelligent User Interfaces, New York, NY, USA: ACM,2005.167～174
[25] Banks L, Bhattacharyya P, Spear M, et al. Davis Social Links: Leveraging SocialNetworks for Future Internet Communication, Proceedings of SAINT'2009.165~168
[26] Boykin P O, Roychowdhury V. Personal email networks: An effective anti-spam tool. IEEEComputer,2005, vol.38(4):61～68
[27] Kong J S, Rezaei B A, Sarshar N, et al. Collaborative Spam Filtering Using e-mailNetworks. IEEE Computer,2006, vol.39(8):67～73
[28] Sirivianos M, Kim K, Yang X. SocialFilter: Introducing Social Trust to Collaborative SpamMitigation. IEEE INFOCOM,2011.2300～2308
[29] Altmann J, Bedane Z B. A P2P File Sharing Network Topology Formation AlgorithmBased on Social Network Information. Proc.28th IEEE Int’l conf. ComputerCommunications Workshops, IEEE Press,2009.242～247
[30] Zifa F, Altmann J. An Empirically Validated Framework for Limiting Free-Riding in P2PNetworks Through the Use of Social Network Information. PACIS2010. Taipei, Taiwan:Pacific Asia Conference on Information Systems,2010
[31] Yu H, Kaminsky M, Gibbons P B, et al. Sybilguard: Defending against sybil attacks viasocial networks. In Proc. ACM SIGCOMM,2006.576～589
[32] Marti S. Trust and Reputation in Peer to Peer Networks:[dissertation]. Stanford University,2005
[33]张富国,徐升华.推荐系统安全问题及技术研究综述.计算机应用研究,2008, Vol.25(3):656～659
[34]张强,骆源,翁楚良等.安全推荐系统中基于信任的检测模型.微计算机信息,2010,vol.26(1-3):68～70
[35] Zhu Z, Cao G, Zhu S, et al. A Social Network Based Patching Scheme for WormContainment in Cellular Networks. IEEE INFOCOM,2009.1476～1484
[36] Levien R L. Attack resistant trust metrics:[dissertation]. Berkeley: Department ofComputer Science, University of California,2004
[37] Golbeck J. Computing and Applying Trust in Web-based Social Networks:[dissertation].College Park: University of Maryland,2005
[38] Srivatsa M, Xiong L, Liu L. TrustGuard: Countering Vulnerabilities in ReputationManagement for Decentralized Overlay Networks, WWW2005, Chiba, Japan,2005.422～431
[39] Golbeck J, Hendler J. Inferring Binary Trust Relationships in Web-Based Social Networks.ACM Transactions on Internet Technology,2006, Vol.6(4):497～529
[40] Caverlee J, Liu L, Webb S. SocialTrust: Tamper-Resilient Trust Establishment in OnlineCommunities. USA, Proceedings of the8th ACM/IEEE-CS joint conference on Digitallibraries, ACM New York,2008.104～114
[41]余建平,周新民,陈明.群体智能典型算法研究综述.计算机工程与应用,2010,vol.46(25):1～4
[42] Segaran T. Programming collective intelligence: building smart web2.0applications.O'Reilly Media,2007
[43] Liu Y, Zhang H, Ma Y, et al. Collective Intelligence and Uncertain KnowledgeRepresentation in Cloud Computing. China Communications.2011(6):58～66
[44] Henry Jenkins. Fans, Bloggers, and Gamers: Exploring Participatory Culture. New York:New York University Press,2006
[45] Ahn L V. Human Computation:[dissertation], Carnegie Mellon University, Pittsburgh,2005
[46] Colorni A, Dorigo M, Maniezzo V. Distributed optimization by ant colonies. Proc of theEuropean Conf on Artificial Life, Paris:1991.134～142
[47] Kennedy J，Eberhart R. Particle swarm optimization. Proc of the4th IEEE International onfon Neural Networks, Perth:1995.1942～1948
[48]沈昌祥.基于积极防御的安全保障框架.中国信息导报,2003(10):50～51
[49] Gammon K. Networking: Four ways to reinvent the Internet.2010. Available from:http://www.nature.com/news/2010/100203/full/463602a.html
[50] Corritore C L, Kracher B, Wiedenbeck S. On-line trust: Concepts, evolving themes, amodel. International Journal of Human-Computer Studies,2003, vol.58(6):737～758
[51]李勇军,代亚非.对等网络信任机制研究.计算机学报,2010(3):390～405
[52]艾瑞咨询.中国即时通讯年度监测报告.2011. Available from: www.iresearch.com.cn
[53] Golbeck J, Rothstein M. Linking Social Networks on the Web with FOAF. AAAI2008.1138～1143
[54]王远,吕建,徐锋等.一个适用于网构软件的信任度量及演化模型.软件学报,2006,Vol.17(4):682～690
[55] Gambetta D. Trust: Making and Breaking Cooperative Relations. Basil Blackwell.University of Oxford,1990. Available from: http://www.sociology.ox.ac.uk/papers/gambetta213-237.pdf
[56] Golbeck J, Kuter U. The Ripple Effect: Change in Trust and Its Impact Over a SocialNetwork. Computing with Social Trust,2009:169～181
[57] Bichsel P, Müller S, Preiss F, et al. Security and Trust through Electronic SocialNetwork-based Interactions.2009International Conference on Computational Science andEngineering, Vancouver, BC,2009.1002～1007
[58] APWG. Anti phishing work group.2011. Available from: http://www.antiphishing.org/
[59] Google Chrome and Google Safe Browsing,2011. Available from: http://www.google.com/chrome/intl/zh-cn/more/security.html
[60] SmartScreen Filter,2011. Available from: http://windows.microsoft.com/en-US/internet-explorer/products/ie-9/features/smartscreen-filter
[61] Resnick P, Zeckhauser R. Trust among strangers in internet transactions: Empirical analysisof ebay’s reputation system. in: M.R. Baye (Ed.), The Economics of the Internet andE-Commerce, Advances in Applied Microeconomics, vol.11, Elsevier Science,2002.127～157
[62] Page L, Brin S, Motwani R, et al. The pagerank citation ranking: Bringing order to theweb. Technical Report, Stanford University,1998
[63] Jean Camp L. Net Trust: Signaling Malicious Web Sites. I/S A Journal of Law and Policyin the Information Society,2007, Vol.3(2):211～235
[64] Safe Browsing Tool: WOT (web of Trust).2011. Available from: http://www.mywot.com/
[65] Aaron G, Rasmussen R. Global Phishing Survey2H2010: Trends and Domain Name Use.April2011. Available from: http://www.antiphishing.org/reports/APWG_GlobalPhishingSurvey_2H2010.pdf
[66] Google Safe Browsing API.2010. Available from: http://code.google.com/intl/en/apis/safebrowsing/
[67] Kienzle D M, Elder M C. Recent worms: A survey and trends. In: Staniford S, ed. Proc. ofthe ACM CCS Workshop on RapidMalcode (WORM2003). Washington D.C.,2003.1～10
[68] Weaver N, Paxson V, Staniford S, et al. A taxonomy of computer worms. In: Staniford S, ed.Proc. of the ACM CCS Workshop on RapidMalcode (WORM2003). Washington D.C.,2003.11～18
[69] Moore D, Shannon C, Brown J. Code red: A case study on the spread and victims of aninternet worm. in Proc.2nd ACM/USENIX Internet Measurement Workshop, ACM Press,2002.273～284
[70] Eichin M, Rochlis J. With microscope and tweezers: An analysis of the Internet virus ofNovember1988. in: Proc. of the IEEE’89Computer Society Symp. on Security andPrivacy. Washington: Computer Society Press of the IEEE,1989.326～343
[71] Arce I, Levy E. An analysis of the Slapper worm. IEEE Security&Privacy,2003, vol.1(1):82～87
[72] Iresearch.2010-2011年中国即时通讯年度监测报告简版.2011. Available from:http://report.iresearch.cn/1595.html
[73] hugo.腾点：腾讯2011年同时在线用户达1.5亿，注册用户达7亿.2011.11.17. Availablefrom: http://www.tendow.com/?p=2653
[74] iResearch. iResearch China Instant Messaging Research Report.2010. Available from:http://www.2chinable.com/china-internet-market-free-reptort-download/cat_view/45-china-social-media-market-free-reports
[75] Smith R D. Instant messaging as a scale-free network.2002. Available from:http://arxiv.org/abs/cond-mat/0206378
[76] Holme P, Kim B J. Growing scale-free networks with tunable clustering. Phys.Rev.E,Melville,2002,65:026107～026109
[77] Naraine R. Researchers Say Automated IM Worm Is Inevitable.2005. Available from:http://www.eweek.com/c/a/Security/Researchers-Say-Automated-IM-Worm-Is-Inevitable/
[78] McAfee, W32/Hello.worm.2003. Available from: http://www.mcafee.com/threat-intelligence/malware/default.aspx?id=99077
[79] Rising,爱情森林病毒.2002. Available from: http://it.rising.com.cn/newSite/Channels/Anti_Virus/Antivirus_Base/TopicDatabasePackage/27-155400316.htm
[80]卿斯汉,王超,何建波等.即时通信蠕虫研究与发展.软件学报,2006, Vo1．17(10):2118～2130
[81] iResearch.2009China IM Communication Security Study Report.2010. Available from:http://www.2chinable.com/china-internet-market-free-reptort-download/cat_view/45-china-social-media-market-free-reports
[82] Wu Y, Yang Y, Wu H, et al. Modeling and Simulation on Information Propagation onInstant Messaging Network Based on Two-layer Scale-free Networks with TunableClustering, Proceedings of the2009IEEE International Conference on Systems, Man, andCybernetics, San Antonio, TX, USA,2009.5184～5188
[83]姚文斌,杨松涛.复杂网络中即时通信蠕虫病毒传播的研究.计算机工程与应用,2009,vol.45（18）:129～131
[84]冯朝胜,邓婕,秦志光等.即时通信蠕虫传播建模.计算机工程,2010, Vo1．36(5):143～145
[85] Williamson M M. Throttling viruses: Restricting propagation to defeat malicious mobilecode. In Proceedings of ACSAC Security Conference, Las Vegas, Nevada,2002.61～68
[86] Williamson M M, Parry A, Byde A. Virus Throttling for Instant Messaging. Proc. of VirusBulletin Conference. Chicago, USA,2004.1～10
[87] Mannan M, Oorschot P C. On Instant Messaging Worms, Analysis and Countermeasures.Proc. of ACM CCS Workshop on Rapid Malcode. Fairfax, Virginia, USA: ACM Press,2005.2～11
[88]赵彬彬，张玉清，刘宇. IM蠕虫检测方案的设计与实现.计算机工程,2009, vol.35(21):147～150
[89]张静,胡华平,刘波.基于行为分析的IM蠕虫检测方法.通信学报,2007, vol.28(8A):154～157
[90]贾春福,刘昕,刘国友等.一种基于社会信任的恶意网页协防机制.通信学报,2012,(已录用)
[91] Costa M, Crowcroft J, Castro M, et al. Vigilante: End-to-End Containment of InternetWorms. in20th ACM Symposium on Operating Systems Principles (SOSP), Brighton,United Kingdom,2005.133～147
[92] Yang S, Jin H, Li B, et al. Worm Containment in Peer-to-Peer Networks. Proceedings of the2009International Conference on Scalable Computing and Communications; EighthInternational Conference on Embedded Computing. Dalian, China,2009.308～313
[93] Zhou L, Zhang L, McSherry F, et al. A first look at peer-to-peer worms: Threats anddevenses. in Proceedings of the IPTPS. Santa Barbara, USA: International workshop onPeer-To-Peer Systems,2005.24～35
[94] AOL/NCSA, Online Safety Study, Produced by America Online and the National CyberSecurity Association,2005. Available from: http://www.staysafeonline.org/pdf/safety_study_2005.pdf
[95] Mcilwraith D, Paquier M, Kotsovinos E. Di-Jest: Autonomic Neighbour Management forWorm Resilience in P2P Systems. Proc. IEEE Int. Symp. on a World of Wireless, Mobileand Multimedia Networks. Newport Beach, USA,2008.1～6
[96] Freitas F, Rodrigues R, Ribeiro C, et al. Verme: Worm containment in peer-to-peer overlays.in6th International Workshop on Peer-to-Peer Systems (IPTPS’07), Bellevue, WA, USA
[97] Freitas F, Marques E, Rodrigues R, et al. Verme: Worm containment in overlay networks.IEEE/IFIP International Conference on Dependable Systems&Networks(DSN), Estoril,Lisbon, Portugal,2009.155～164
[98] Wang H J, Guo C, Simon D R, et al. Shield: Vulnerability Driven Network Filters forPreventing Known Vulnerability Exploits. Computer Communication Review,2004, vol.34(4):193～204
[99] Fan X, Xiang Y. Propagation Modeling of Peer-to-Peer Worms.24th IEEE InternationalConference on Advanced Information Networking and Applications (AINA), Perth,Australia,2010.1128～1135
[100]中国国家信息安全漏洞库.查询统计.2012. Available from: http://www.cnnvd.org.cn/vulnerability/statistics
[101] Liang J, Kumar R, Ross K W. The KaZaA Overlay: A Measurement Study. ComputerNetworks Journal,2005,vol.50(6):842～858
[102] Moore D, Shannon C, Voelker G M, et al. Internet quarantine: requirements for containingselfpropagating code. Proc. IEEE INFOCOM’03. San Francisco, CA,2003.1901～1910
[103] Farrow R. Reverse-engineering new exploits. CMP Network Magazine,2004(3):67～68
[104] Shannon C, Moore D. The spread of the witty worm. IEEE Security&Privacy,2004, vol.2(4):46～50
[105] ANI worm.2007. Available from: http://www.f-secure.com/weblog/archives/00001158.html
[106] Vojnovic M, Ganesh A J. On the effectiveness of automatic patching, Proceedings of the2005ACM Workshop on Rapid Malcode. Fairfax, VA, USA: WORM,2005.41～50
[107] Serenyi D, Witten B. RapidUpdate: Peer-Assisted Distribution of Security Content, inIPTPS. Tampa, FL, USA,2008.20～26
[108] Symantec. W32.Welchia.Worm.2003. Available from: http://www.updatexp.com/nachi-worm.html
[109] Kaspersky Lab ZAO. Net-Worm.Win32.CodeGreen.a.2001. Available from:http://www.securelist.com/en/descriptions/6882349/Net-Worm.Win32.CodeGreen.a
[110] Wang B L, Yun X C, Fang B X. Worms Containment Modeling and Analysis under ActiveWorm Countermeasure. WSEAS Transactions on Information Science and Applications,2004, vol.1(5):958～966
[111] Liu Y X, Yun X C, Wang B L, et al. QBTP Worm: An Anti-worm with Balanced Tree basedSpreading Strategy. Intenrational Conference on Machine Learning and Cybernetics,2005.3955～3964
[112] Luo W, Liu J, Fan C. Research of policy defending against P2P worm based on benignworm. Application Research of Computers,2009,12:4764～4767
[113] Wang B, Ding P, Sheng J. P2P Anti-worm: Modeling and Analysis of a New WormCounter-measurement Strategy. The9th International Conference for Young ComputerScientists, Zhang Jia Jie, Hunan, China,2008.1553～1558
[114] Castaneda F, Sezer E C, Xu J. Worm vs. worm: preliminary study of an activecounter-attack mechanism. Proceedings of the2004ACM Workshop on Rapid Malcode,Washington, DC, USA,2004.83～93
[115]王佰玲,基于良性蠕虫的网络蠕虫主动遏制技术研究:[博士学位论文].哈尔滨:哈尔滨工业大学,2006
[116]周翰逊,赵宏.主动良性蠕虫和混合良性蠕虫的建模与分析.计算机研究与发展,2007, vol.44(6):958～964
[117]罗卫敏,刘井波,范成瑜.基于良性蠕虫对抗P2P蠕虫的策略研究.计算机应用研究,2009, Vol.26(12):4764～4767
[118]罗卫敏.基于良性蠕虫的蠕虫防御机制研究:[硕士学位论文].成都:电子科技大学，2009
[119] Jia C F, Liu X, Liu G Y, et al. Worm Containment Based on Double-neighbor Lists in P2POverlay Networks.2010International Conference on Information Theory and InformationSecurity. Beijing,2010.558～562
[120] Microsoft Data Access Components (MDAC)功能中的漏洞可能允许执行代码(911562).2006. Available from: http://technet.microsoft.com/zh-cn/security/bulletin/ms06-014#section0
[121] Vojnovic M, Ganesh A. On the Effectiveness of Automatic Patching. workshop on rapidmalcode. Fairfax, Virginia, USA,2005.41～50
[122] Xie L, Zhu S. A Feasibility Study on Defending Against Ultra-Fast Topological Worms.Seventh IEEE International Conference on Peer-to-Peer Computing. Galway, Ireland,2007.61～70
[123] Shakkottai S, Srikant R. Peer to Peer Networks for Defense against Internet Worms. IEEEJournal on Selected Areas in Communications,2007, vol.25(9):1745～1752
[124] Friedman A. Good Neighbors Can Make Good Fences: A Peer-to-Peer User SecuritySystem. IEEE Technology and Society Magazine,2007, vol.26(1):17～24
[125]王林,江秀萍,柯熙政.关于无标度网络中Hub节点的研究.计算机应用,2010,Vol.30(11):3062～3064
[2]国家计算机病毒应急处理中心.2010年全国信息网络安全状况与计算机及移动终端病毒疫情调查分析报告.2011.Available from: http://www.antivirus-china.org.cn/
[3] AhnLab.安博士六月社交网站恶意代码攻击报告.2011. Available from: http://www.ahnlabchina.com/article_128.html
[4] Liu P, Shi Y, Francis C. M., et al. Grid Demo Proposal: AntiSpamGrid, IEEE InternationalConference on Cluster Computing, Hong Kong: Dec1,2003
[5] Liu P, Chen G, Ye L, et al. Anti-Spam Grid: A Dynamically Organized Spam FilteringInfrastructure, Proceedings of the5th WSEAS Int. Conf. on Simulation, Modeling andOptimization, Corfu, Greece: August17,2005.61～66
[6]瑞星.瑞星2011年上半年安全报告.2011. Available from: http://www.rising.com.cn/about/news/rising/2011-07-20/9802.html
[7] Mark Zuckerberg officially confirmed that Facebook reached750million active usersmilestone,2011. Available from: http://www.techsnapr.com/2011/07/07/mark-zuckerberg-officially-confirmed-that-facebook-reached-750-million-active-users-milestone/
[8] Lazer D, Pentland A, Adamic L, et al. Computational Social Science. Science,2009,vol.323(6):721～723
[9] Steven SH. Exploring complex networks. Nature,2001, vol.410(6825):268～276
[10]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
[11] Watts D J. Six degrees: the science of a connected age. W. W. Norton&Co.,2004.2
[12] Parameswaran M, Whinston A B. Socail Computing: An Overview, Communications of theAssociation for Information Systems.2007(19):762～780
[13] Wang F. Social computing: Concept s, content s and methods. International Journal ofIntelligent Control and Systems,2004, vol.9(2):91～96
[14] Wang F, Carley K M, Zeng D, et al. Social Computing: From Social Informatics to SocialIntelligence. Intelligent Systems,2007,22(2):79～83
[15] Abdul-Rahman A, Hailes S. Supporting Trust in Virtual Communities, Proceedings of the33rd Hawaii International Conference on System Sciences. Maui,2000.1～9
[16] Dwyer C，Hiltz S R，Passerini K. Trust and privacy concern within social networking sites:A comparison of Facebook and MySpace. Proceedings of the Thirteenth AmericasConference on Information Systems. Keystone, Colorado:2007. paper339(13)
[17] Golbeck J. Weaving a Web of Trust. Science Magazine, AAAS,2008, Vol.321(5896):1640～1641
[18] Kuter U, Golbeck J. SUNNY: A New Algorithm for Trust Inference in Social Networksusing Probabilistic Confidence Models. Proceedings of the Twenty-Second NationalConference on Artificial Intelligence. Vancouver, British Columbia:2007.1377～1382
[19] Wang E. A Survey of Web-based Social Network Trust. ITEC810Information TechnologyProject Unit. ITEC810Final Report,2009
[20] Avesani P, Massa P, Tiella R. Proceedings of the2005Association for ComputingMachinery Symposium on Applied Computing. New York: Association for ComputingMachinery,2005.1589～1593
[21] Abdul-Rahman A, Hailes S. Supporting trust in virtual communities. Proceedings of the33rd Hawaii International Conference on System Sciences, Maui, Hawaii,2000.4～7
[22] Ziegler C N, Lausen G. Analyzing correlation between trust and user similarity in onlinecommunities. Lecture Notes in Computer Science,2004, Vol.2995:251～265
[23] Sinha R, Swearingen K. Comparing recommendations made by online systems and friends.Proceedings of the DELOS-NSF Workshop on Personalization and Recommender Systemsin Digital Libraries Dublin, Ireland,2001
[24] O’Donovan J, Smyth B. Trust in recommender systems. IUI’05: Proceedings of the10thInternational Conference on Intelligent User Interfaces, New York, NY, USA: ACM,2005.167～174
[25] Banks L, Bhattacharyya P, Spear M, et al. Davis Social Links: Leveraging SocialNetworks for Future Internet Communication, Proceedings of SAINT'2009.165~168
[26] Boykin P O, Roychowdhury V. Personal email networks: An effective anti-spam tool. IEEEComputer,2005, vol.38(4):61～68
[27] Kong J S, Rezaei B A, Sarshar N, et al. Collaborative Spam Filtering Using e-mailNetworks. IEEE Computer,2006, vol.39(8):67～73
[28] Sirivianos M, Kim K, Yang X. SocialFilter: Introducing Social Trust to Collaborative SpamMitigation. IEEE INFOCOM,2011.2300～2308
[29] Altmann J, Bedane Z B. A P2P File Sharing Network Topology Formation AlgorithmBased on Social Network Information. Proc.28th IEEE Int’l conf. ComputerCommunications Workshops, IEEE Press,2009.242～247
[30] Zifa F, Altmann J. An Empirically Validated Framework for Limiting Free-Riding in P2PNetworks Through the Use of Social Network Information. PACIS2010. Taipei, Taiwan:Pacific Asia Conference on Information Systems,2010
[31] Yu H, Kaminsky M, Gibbons P B, et al. Sybilguard: Defending against sybil attacks viasocial networks. In Proc. ACM SIGCOMM,2006.576～589
[32] Marti S. Trust and Reputation in Peer to Peer Networks:[dissertation]. Stanford University,2005
[33]张富国,徐升华.推荐系统安全问题及技术研究综述.计算机应用研究,2008, Vol.25(3):656～659
[34]张强,骆源,翁楚良等.安全推荐系统中基于信任的检测模型.微计算机信息,2010,vol.26(1-3):68～70
[35] Zhu Z, Cao G, Zhu S, et al. A Social Network Based Patching Scheme for WormContainment in Cellular Networks. IEEE INFOCOM,2009.1476～1484
[36] Levien R L. Attack resistant trust metrics:[dissertation]. Berkeley: Department ofComputer Science, University of California,2004
[37] Golbeck J. Computing and Applying Trust in Web-based Social Networks:[dissertation].College Park: University of Maryland,2005
[38] Srivatsa M, Xiong L, Liu L. TrustGuard: Countering Vulnerabilities in ReputationManagement for Decentralized Overlay Networks, WWW2005, Chiba, Japan,2005.422～431
[39] Golbeck J, Hendler J. Inferring Binary Trust Relationships in Web-Based Social Networks.ACM Transactions on Internet Technology,2006, Vol.6(4):497～529
[40] Caverlee J, Liu L, Webb S. SocialTrust: Tamper-Resilient Trust Establishment in OnlineCommunities. USA, Proceedings of the8th ACM/IEEE-CS joint conference on Digitallibraries, ACM New York,2008.104～114
[41]余建平,周新民,陈明.群体智能典型算法研究综述.计算机工程与应用,2010,vol.46(25):1～4
[42] Segaran T. Programming collective intelligence: building smart web2.0applications.O'Reilly Media,2007
[43] Liu Y, Zhang H, Ma Y, et al. Collective Intelligence and Uncertain KnowledgeRepresentation in Cloud Computing. China Communications.2011(6):58～66
[44] Henry Jenkins. Fans, Bloggers, and Gamers: Exploring Participatory Culture. New York:New York University Press,2006
[45] Ahn L V. Human Computation:[dissertation], Carnegie Mellon University, Pittsburgh,2005
[46] Colorni A, Dorigo M, Maniezzo V. Distributed optimization by ant colonies. Proc of theEuropean Conf on Artificial Life, Paris:1991.134～142
[47] Kennedy J，Eberhart R. Particle swarm optimization. Proc of the4th IEEE International onfon Neural Networks, Perth:1995.1942～1948
[48]沈昌祥.基于积极防御的安全保障框架.中国信息导报,2003(10):50～51
[49] Gammon K. Networking: Four ways to reinvent the Internet.2010. Available from:http://www.nature.com/news/2010/100203/full/463602a.html
[50] Corritore C L, Kracher B, Wiedenbeck S. On-line trust: Concepts, evolving themes, amodel. International Journal of Human-Computer Studies,2003, vol.58(6):737～758
[51]李勇军,代亚非.对等网络信任机制研究.计算机学报,2010(3):390～405
[52]艾瑞咨询.中国即时通讯年度监测报告.2011. Available from: www.iresearch.com.cn
[53] Golbeck J, Rothstein M. Linking Social Networks on the Web with FOAF. AAAI2008.1138～1143
[54]王远,吕建,徐锋等.一个适用于网构软件的信任度量及演化模型.软件学报,2006,Vol.17(4):682～690
[55] Gambetta D. Trust: Making and Breaking Cooperative Relations. Basil Blackwell.University of Oxford,1990. Available from: http://www.sociology.ox.ac.uk/papers/gambetta213-237.pdf
[56] Golbeck J, Kuter U. The Ripple Effect: Change in Trust and Its Impact Over a SocialNetwork. Computing with Social Trust,2009:169～181
[57] Bichsel P, Müller S, Preiss F, et al. Security and Trust through Electronic SocialNetwork-based Interactions.2009International Conference on Computational Science andEngineering, Vancouver, BC,2009.1002～1007
[58] APWG. Anti phishing work group.2011. Available from: http://www.antiphishing.org/
[59] Google Chrome and Google Safe Browsing,2011. Available from: http://www.google.com/chrome/intl/zh-cn/more/security.html
[60] SmartScreen Filter,2011. Available from: http://windows.microsoft.com/en-US/internet-explorer/products/ie-9/features/smartscreen-filter
[61] Resnick P, Zeckhauser R. Trust among strangers in internet transactions: Empirical analysisof ebay’s reputation system. in: M.R. Baye (Ed.), The Economics of the Internet andE-Commerce, Advances in Applied Microeconomics, vol.11, Elsevier Science,2002.127～157
[62] Page L, Brin S, Motwani R, et al. The pagerank citation ranking: Bringing order to theweb. Technical Report, Stanford University,1998
[63] Jean Camp L. Net Trust: Signaling Malicious Web Sites. I/S A Journal of Law and Policyin the Information Society,2007, Vol.3(2):211～235
[64] Safe Browsing Tool: WOT (web of Trust).2011. Available from: http://www.mywot.com/
[65] Aaron G, Rasmussen R. Global Phishing Survey2H2010: Trends and Domain Name Use.April2011. Available from: http://www.antiphishing.org/reports/APWG_GlobalPhishingSurvey_2H2010.pdf
[66] Google Safe Browsing API.2010. Available from: http://code.google.com/intl/en/apis/safebrowsing/
[67] Kienzle D M, Elder M C. Recent worms: A survey and trends. In: Staniford S, ed. Proc. ofthe ACM CCS Workshop on RapidMalcode (WORM2003). Washington D.C.,2003.1～10
[68] Weaver N, Paxson V, Staniford S, et al. A taxonomy of computer worms. In: Staniford S, ed.Proc. of the ACM CCS Workshop on RapidMalcode (WORM2003). Washington D.C.,2003.11～18
[69] Moore D, Shannon C, Brown J. Code red: A case study on the spread and victims of aninternet worm. in Proc.2nd ACM/USENIX Internet Measurement Workshop, ACM Press,2002.273～284
[70] Eichin M, Rochlis J. With microscope and tweezers: An analysis of the Internet virus ofNovember1988. in: Proc. of the IEEE’89Computer Society Symp. on Security andPrivacy. Washington: Computer Society Press of the IEEE,1989.326～343
[71] Arce I, Levy E. An analysis of the Slapper worm. IEEE Security&Privacy,2003, vol.1(1):82～87
[72] Iresearch.2010-2011年中国即时通讯年度监测报告简版.2011. Available from:http://report.iresearch.cn/1595.html
[73] hugo.腾点：腾讯2011年同时在线用户达1.5亿，注册用户达7亿.2011.11.17. Availablefrom: http://www.tendow.com/?p=2653
[74] iResearch. iResearch China Instant Messaging Research Report.2010. Available from:http://www.2chinable.com/china-internet-market-free-reptort-download/cat_view/45-china-social-media-market-free-reports
[75] Smith R D. Instant messaging as a scale-free network.2002. Available from:http://arxiv.org/abs/cond-mat/0206378
[76] Holme P, Kim B J. Growing scale-free networks with tunable clustering. Phys.Rev.E,Melville,2002,65:026107～026109
[77] Naraine R. Researchers Say Automated IM Worm Is Inevitable.2005. Available from:http://www.eweek.com/c/a/Security/Researchers-Say-Automated-IM-Worm-Is-Inevitable/
[78] McAfee, W32/Hello.worm.2003. Available from: http://www.mcafee.com/threat-intelligence/malware/default.aspx?id=99077
[79] Rising,爱情森林病毒.2002. Available from: http://it.rising.com.cn/newSite/Channels/Anti_Virus/Antivirus_Base/TopicDatabasePackage/27-155400316.htm
[80]卿斯汉,王超,何建波等.即时通信蠕虫研究与发展.软件学报,2006, Vo1．17(10):2118～2130
[81] iResearch.2009China IM Communication Security Study Report.2010. Available from:http://www.2chinable.com/china-internet-market-free-reptort-download/cat_view/45-china-social-media-market-free-reports
[82] Wu Y, Yang Y, Wu H, et al. Modeling and Simulation on Information Propagation onInstant Messaging Network Based on Two-layer Scale-free Networks with TunableClustering, Proceedings of the2009IEEE International Conference on Systems, Man, andCybernetics, San Antonio, TX, USA,2009.5184～5188
[83]姚文斌,杨松涛.复杂网络中即时通信蠕虫病毒传播的研究.计算机工程与应用,2009,vol.45（18）:129～131
[84]冯朝胜,邓婕,秦志光等.即时通信蠕虫传播建模.计算机工程,2010, Vo1．36(5):143～145
[85] Williamson M M. Throttling viruses: Restricting propagation to defeat malicious mobilecode. In Proceedings of ACSAC Security Conference, Las Vegas, Nevada,2002.61～68
[86] Williamson M M, Parry A, Byde A. Virus Throttling for Instant Messaging. Proc. of VirusBulletin Conference. Chicago, USA,2004.1～10
[87] Mannan M, Oorschot P C. On Instant Messaging Worms, Analysis and Countermeasures.Proc. of ACM CCS Workshop on Rapid Malcode. Fairfax, Virginia, USA: ACM Press,2005.2～11
[88]赵彬彬，张玉清，刘宇. IM蠕虫检测方案的设计与实现.计算机工程,2009, vol.35(21):147～150
[89]张静,胡华平,刘波.基于行为分析的IM蠕虫检测方法.通信学报,2007, vol.28(8A):154～157
[90]贾春福,刘昕,刘国友等.一种基于社会信任的恶意网页协防机制.通信学报,2012,(已录用)
[91] Costa M, Crowcroft J, Castro M, et al. Vigilante: End-to-End Containment of InternetWorms. in20th ACM Symposium on Operating Systems Principles (SOSP), Brighton,United Kingdom,2005.133～147
[92] Yang S, Jin H, Li B, et al. Worm Containment in Peer-to-Peer Networks. Proceedings of the2009International Conference on Scalable Computing and Communications; EighthInternational Conference on Embedded Computing. Dalian, China,2009.308～313
[93] Zhou L, Zhang L, McSherry F, et al. A first look at peer-to-peer worms: Threats anddevenses. in Proceedings of the IPTPS. Santa Barbara, USA: International workshop onPeer-To-Peer Systems,2005.24～35
[94] AOL/NCSA, Online Safety Study, Produced by America Online and the National CyberSecurity Association,2005. Available from: http://www.staysafeonline.org/pdf/safety_study_2005.pdf
[95] Mcilwraith D, Paquier M, Kotsovinos E. Di-Jest: Autonomic Neighbour Management forWorm Resilience in P2P Systems. Proc. IEEE Int. Symp. on a World of Wireless, Mobileand Multimedia Networks. Newport Beach, USA,2008.1～6
[96] Freitas F, Rodrigues R, Ribeiro C, et al. Verme: Worm containment in peer-to-peer overlays.in6th International Workshop on Peer-to-Peer Systems (IPTPS’07), Bellevue, WA, USA
[97] Freitas F, Marques E, Rodrigues R, et al. Verme: Worm containment in overlay networks.IEEE/IFIP International Conference on Dependable Systems&Networks(DSN), Estoril,Lisbon, Portugal,2009.155～164
[98] Wang H J, Guo C, Simon D R, et al. Shield: Vulnerability Driven Network Filters forPreventing Known Vulnerability Exploits. Computer Communication Review,2004, vol.34(4):193～204
[99] Fan X, Xiang Y. Propagation Modeling of Peer-to-Peer Worms.24th IEEE InternationalConference on Advanced Information Networking and Applications (AINA), Perth,Australia,2010.1128～1135
[100]中国国家信息安全漏洞库.查询统计.2012. Available from: http://www.cnnvd.org.cn/vulnerability/statistics
[101] Liang J, Kumar R, Ross K W. The KaZaA Overlay: A Measurement Study. ComputerNetworks Journal,2005,vol.50(6):842～858
[102] Moore D, Shannon C, Voelker G M, et al. Internet quarantine: requirements for containingselfpropagating code. Proc. IEEE INFOCOM’03. San Francisco, CA,2003.1901～1910
[103] Farrow R. Reverse-engineering new exploits. CMP Network Magazine,2004(3):67～68
[104] Shannon C, Moore D. The spread of the witty worm. IEEE Security&Privacy,2004, vol.2(4):46～50
[105] ANI worm.2007. Available from: http://www.f-secure.com/weblog/archives/00001158.html
[106] Vojnovic M, Ganesh A J. On the effectiveness of automatic patching, Proceedings of the2005ACM Workshop on Rapid Malcode. Fairfax, VA, USA: WORM,2005.41～50
[107] Serenyi D, Witten B. RapidUpdate: Peer-Assisted Distribution of Security Content, inIPTPS. Tampa, FL, USA,2008.20～26
[108] Symantec. W32.Welchia.Worm.2003. Available from: http://www.updatexp.com/nachi-worm.html
[109] Kaspersky Lab ZAO. Net-Worm.Win32.CodeGreen.a.2001. Available from:http://www.securelist.com/en/descriptions/6882349/Net-Worm.Win32.CodeGreen.a
[110] Wang B L, Yun X C, Fang B X. Worms Containment Modeling and Analysis under ActiveWorm Countermeasure. WSEAS Transactions on Information Science and Applications,2004, vol.1(5):958～966
[111] Liu Y X, Yun X C, Wang B L, et al. QBTP Worm: An Anti-worm with Balanced Tree basedSpreading Strategy. Intenrational Conference on Machine Learning and Cybernetics,2005.3955～3964
[112] Luo W, Liu J, Fan C. Research of policy defending against P2P worm based on benignworm. Application Research of Computers,2009,12:4764～4767
[113] Wang B, Ding P, Sheng J. P2P Anti-worm: Modeling and Analysis of a New WormCounter-measurement Strategy. The9th International Conference for Young ComputerScientists, Zhang Jia Jie, Hunan, China,2008.1553～1558
[114] Castaneda F, Sezer E C, Xu J. Worm vs. worm: preliminary study of an activecounter-attack mechanism. Proceedings of the2004ACM Workshop on Rapid Malcode,Washington, DC, USA,2004.83～93
[115]王佰玲,基于良性蠕虫的网络蠕虫主动遏制技术研究:[博士学位论文].哈尔滨:哈尔滨工业大学,2006
[116]周翰逊,赵宏.主动良性蠕虫和混合良性蠕虫的建模与分析.计算机研究与发展,2007, vol.44(6):958～964
[117]罗卫敏,刘井波,范成瑜.基于良性蠕虫对抗P2P蠕虫的策略研究.计算机应用研究,2009, Vol.26(12):4764～4767
[118]罗卫敏.基于良性蠕虫的蠕虫防御机制研究:[硕士学位论文].成都:电子科技大学，2009
[119] Jia C F, Liu X, Liu G Y, et al. Worm Containment Based on Double-neighbor Lists in P2POverlay Networks.2010International Conference on Information Theory and InformationSecurity. Beijing,2010.558～562
[120] Microsoft Data Access Components (MDAC)功能中的漏洞可能允许执行代码(911562).2006. Available from: http://technet.microsoft.com/zh-cn/security/bulletin/ms06-014#section0
[121] Vojnovic M, Ganesh A. On the Effectiveness of Automatic Patching. workshop on rapidmalcode. Fairfax, Virginia, USA,2005.41～50
[122] Xie L, Zhu S. A Feasibility Study on Defending Against Ultra-Fast Topological Worms.Seventh IEEE International Conference on Peer-to-Peer Computing. Galway, Ireland,2007.61～70
[123] Shakkottai S, Srikant R. Peer to Peer Networks for Defense against Internet Worms. IEEEJournal on Selected Areas in Communications,2007, vol.25(9):1745～1752
[124] Friedman A. Good Neighbors Can Make Good Fences: A Peer-to-Peer User SecuritySystem. IEEE Technology and Society Magazine,2007, vol.26(1):17～24
[125]王林,江秀萍,柯熙政.关于无标度网络中Hub节点的研究.计算机应用,2010,Vol.30(11):3062～3064
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |