车联网蠕虫传播与防治研究
|
摘要
蠕虫由于危害严重、攻击范围大、爆发速度快、针对和可控性强已经成为网络所面临最为严重的安全威胁之一,并且有实例表明,目前己经从网络安全隐患上升为国家战略层面的超级网络武器。物联网及车联网技术的快速发展,应用和部署,在为驾乘人员提供各项综合服务,为推广交通智能化做出贡献的同时,也蠕虫的入侵传播提供了平台。面对系统资源、网络资源方面更加稀缺的车联网环境,蠕虫更容易造成网络异常甚至瘫痪,对车联网网络安全造成严重的安全隐患。因此,本文着眼于车联网面临蠕虫入侵威胁这一问题开展工作,研究蠕虫在车联网环境中的传播规律,并在此基础上研究防治蠕虫的有效对策,为日后车联网在应用中实时监控防治蠕虫提供有效的理论依据。
为保障车联网网络安全,针对车联网环境中蠕虫的传播与防治,本文深入地分析和研究了车联网蠕虫在现实道路网络中的传播模型和传播规律,并以此为基础提出了防治蠕虫的新型良性蠕虫策略。
首先,基于车联网不同于传统网络的影响蠕虫传播的特有因素,如道路网络中车辆节点全方向复杂路况行驶,道路环境对无线信号存在阻挡、叠加干扰影响等,针对车联网蠕虫的传播,结合能够反映真实交通流量的智能行驶模型,构建了新型的IOVWPM模型。IOVWPM模型由车辆节点运动性和平均接触率模型、车辆节点信道链接连通率模型和车联网蠕虫传播动力学模型构成,较好地显示了车辆节点平均速度、阴影衰落等交通流量、无线信道因素对蠕虫传播的影响。通过仿真实验证明结果与IOVWPM模型理论一致。IOVWPM模型能较为真实地模拟道路路网环境车联网蠕虫的传播和爆发规律,反映出与早期研究的传统网络环境蠕虫传播的差异,本文将其归结为车辆节点特有的空间运动特性和无线信道环境。IOVWPM模型为设计实时检测策略保护车联网网络安全,防治车联网蠕虫的破坏性传播提供了理论基础；
其次,针对车联网蠕虫不同于传统网络的传播特性及防治需求,提出了一种新型良性蠕虫——基于速度分治的车联网良性蠕虫模型IOVAWM。IOVAWM将空间车辆节点平均速度这一能够反映区域内道路交通行驶状况,并影响蠕虫传播效果的主要制约因素作为混合良性蠕虫中主动和被动良性蠕虫的切换条件,以此调度主动和被动良性蠕虫在IOVAWM中的交替作用,从而扬主动和被动良性蠕虫所长,避它们所短。通过仿真实验证明IOVAWM能够取得预期的良好防治效果,不仅能够快速有效抑制蠕虫的传播,并且能进一步降低良性蠕虫在传播及对抗过程中对网络开销的诉求,为保护车联网网络安全,有效遏制车联网蠕虫的破坏性传播提供了理论依据；
然后,针对高速路网特殊的行驶条件和网络条件,总结了高速路网环境中蠕虫的传播规律,并讨论分析了基于速度分治的车联网良性蠕虫IOVAWM的防治效果。通过仿真实验证明IOVAWM在高速路网环境中也能够取得预期的良好防治效果,不仅能够快速有效抑制蠕虫的传播,并且能进一步降低良性蠕虫在传播及对抗过程中对网络资源开销的诉求,为制定针对高速路网环境的车联网蠕虫防治策略提供了参考；
最后,针对高速路网环境存在的车联网通信集群的动态性和车辆节点间不稳定的连通特性,提出一种基于不稳定连通环境的被动响应车联网良性蠕虫。通过良性蠕虫被动响应机制排除主动良性蠕虫发送探测报文对网络资源的消耗,依靠被动响应机制对蠕虫进行“溯源”式的反向传播良性蠕虫,对蠕虫加以遏制；并通过基于不稳定连通环境的良性蠕虫传输机制确保网络不稳定连通条件下被动响应机制中良性蠕虫的有效传输。通过建模分析和仿真实验,证明该模型在高速路网环境中能够取得预期的良好防治效果：既能保证良性报文传输效率,提高良性蠕虫的防治效果,并且同时能避免主动良性蠕虫造成的额外网络资源开销。为保护高速路网环境车联网网络安全,有效防治车联网蠕虫的破坏性传播做出了有益的探索。
Worm has been became one of the serious security threats of network with its large range attack, burst rapidly, better pertinence and controllability, and examples showed that worm has been upgraded to the super network weapon of national strategy security level. IOT and IOV technology developed, applied, deployed quickly, has provided integrated service for drivers, passengers, and contribution to popularizing traffic intelligentize, meanwhile, has provided platform for worm invasion and propagation. Facing the IOV environment with system resources and network resources scarcity, worm could easily cause network anomaly and even network paralysis, so lead to serious IOV notwork hidden danger. Therefore, with a view to the problem of worm intrusion threat of IOV, this paper developed job, containing worm propagation law research in IOV environment, and worm effective countermeasure research on this basis, and could provide effective theoretical foundation for real-time monitoring and prevention-cure against worm in IOV application in the future.
In order to guarantee the network security of IOV, according to the worm propagation and precaution-cure against worm, IOV worm propagation model and law in realistic road network had been deeply analysed and researched, and a novel anti-worm strategy for precaution-cure has been proposed based on this propagation model and law.
Firstly, based on the different typical factor of IOV compared with traditional networks, which could influence worm propagation, for example, vehicle nodes in complex road conditions omnirange driving, the barrier, interference and superposition influence of road environment on wireless communication signal, etc, according to worm propagation, combined the Intelligent Driver Model, a novel IOV worm propagation model (IOVWPM) was constructed. IOVWPM was constituted with motility and contact probability model, the mobile channel and link probability model and dynamics of IOV worm propagation model of vehicle nodes, could preferably show the influence over worm propagation by these components. Simulation experiment results was consistent with the theory of IOVWPM IOVWPM could actually simulate the worm propagation and provide a theoretical basis for programming real-time detection strategy and preventing worm destructive propagation in IOV.
Secondly, according to the characteristic worm propagation and prevention-cure demand different from traditional networks, a novel anti-worm model in IOV based on divide-and-Conquer with velocity (IOVAWM) was constructed based on divide-and-conquer with velocity. This paper treated the drive velocity of vehicle node as the switch condition between active and passive anti-worms in hybrid anti-worms, and controled alternating action of active and passive anti-worms based on this switch condition, so as to carry forward advantages and to avoid disadvantages of them. Simulation experiment results showed the better effect of IOVAWM for worm prevention-cure could be achieved, could not only better contain the IOV worm propagation, but also could better hold down the network resource spending in anti-worm.
Thirdly, according to the particular driving condition and notwork condition of expressway network, the worm propagation law in expressway network has been summarized, and simulation experiment results showed the better effect of IOVAWM for worm prevention-cure also could be achieved in expressway network, could not only better contain the IOV worm propagation, but also could better hold down the network resource spending in anti-worm.
Finally, according to the dynamic characteristic of IOV communication cluster and unstable connectivity characteristic among vehicle nodes in expressway network, a novel passive pesponse anti-worm model in IOV based on unstable connectivity environment (IOVPRAWM) was constructed. Anti-worm passive pesponse mechanism eliminated the network resource spending by active anti-worm sending round probe packets actively, and could traceability send anti-worm back for containing; and the anti-worm transmission mechanism based on unstable connectivity environment ensured anti-worm's effective transmission in unstable connectivity condition. Simulation experiment results showed IOVAWM could not only better contain the IOV worm propagation, but also could better eliminate the network resource spending by active anti-worm sending round probe packets actively and hold down the network resource spending in anti-worm. Beneficial exploration has been made for network security protection in expressway network IOV environment, and effective prevention-cure worm destructive propagation.
为保障车联网网络安全,针对车联网环境中蠕虫的传播与防治,本文深入地分析和研究了车联网蠕虫在现实道路网络中的传播模型和传播规律,并以此为基础提出了防治蠕虫的新型良性蠕虫策略。
首先,基于车联网不同于传统网络的影响蠕虫传播的特有因素,如道路网络中车辆节点全方向复杂路况行驶,道路环境对无线信号存在阻挡、叠加干扰影响等,针对车联网蠕虫的传播,结合能够反映真实交通流量的智能行驶模型,构建了新型的IOVWPM模型。IOVWPM模型由车辆节点运动性和平均接触率模型、车辆节点信道链接连通率模型和车联网蠕虫传播动力学模型构成,较好地显示了车辆节点平均速度、阴影衰落等交通流量、无线信道因素对蠕虫传播的影响。通过仿真实验证明结果与IOVWPM模型理论一致。IOVWPM模型能较为真实地模拟道路路网环境车联网蠕虫的传播和爆发规律,反映出与早期研究的传统网络环境蠕虫传播的差异,本文将其归结为车辆节点特有的空间运动特性和无线信道环境。IOVWPM模型为设计实时检测策略保护车联网网络安全,防治车联网蠕虫的破坏性传播提供了理论基础；
其次,针对车联网蠕虫不同于传统网络的传播特性及防治需求,提出了一种新型良性蠕虫——基于速度分治的车联网良性蠕虫模型IOVAWM。IOVAWM将空间车辆节点平均速度这一能够反映区域内道路交通行驶状况,并影响蠕虫传播效果的主要制约因素作为混合良性蠕虫中主动和被动良性蠕虫的切换条件,以此调度主动和被动良性蠕虫在IOVAWM中的交替作用,从而扬主动和被动良性蠕虫所长,避它们所短。通过仿真实验证明IOVAWM能够取得预期的良好防治效果,不仅能够快速有效抑制蠕虫的传播,并且能进一步降低良性蠕虫在传播及对抗过程中对网络开销的诉求,为保护车联网网络安全,有效遏制车联网蠕虫的破坏性传播提供了理论依据；
然后,针对高速路网特殊的行驶条件和网络条件,总结了高速路网环境中蠕虫的传播规律,并讨论分析了基于速度分治的车联网良性蠕虫IOVAWM的防治效果。通过仿真实验证明IOVAWM在高速路网环境中也能够取得预期的良好防治效果,不仅能够快速有效抑制蠕虫的传播,并且能进一步降低良性蠕虫在传播及对抗过程中对网络资源开销的诉求,为制定针对高速路网环境的车联网蠕虫防治策略提供了参考；
最后,针对高速路网环境存在的车联网通信集群的动态性和车辆节点间不稳定的连通特性,提出一种基于不稳定连通环境的被动响应车联网良性蠕虫。通过良性蠕虫被动响应机制排除主动良性蠕虫发送探测报文对网络资源的消耗,依靠被动响应机制对蠕虫进行“溯源”式的反向传播良性蠕虫,对蠕虫加以遏制；并通过基于不稳定连通环境的良性蠕虫传输机制确保网络不稳定连通条件下被动响应机制中良性蠕虫的有效传输。通过建模分析和仿真实验,证明该模型在高速路网环境中能够取得预期的良好防治效果：既能保证良性报文传输效率,提高良性蠕虫的防治效果,并且同时能避免主动良性蠕虫造成的额外网络资源开销。为保护高速路网环境车联网网络安全,有效防治车联网蠕虫的破坏性传播做出了有益的探索。
Worm has been became one of the serious security threats of network with its large range attack, burst rapidly, better pertinence and controllability, and examples showed that worm has been upgraded to the super network weapon of national strategy security level. IOT and IOV technology developed, applied, deployed quickly, has provided integrated service for drivers, passengers, and contribution to popularizing traffic intelligentize, meanwhile, has provided platform for worm invasion and propagation. Facing the IOV environment with system resources and network resources scarcity, worm could easily cause network anomaly and even network paralysis, so lead to serious IOV notwork hidden danger. Therefore, with a view to the problem of worm intrusion threat of IOV, this paper developed job, containing worm propagation law research in IOV environment, and worm effective countermeasure research on this basis, and could provide effective theoretical foundation for real-time monitoring and prevention-cure against worm in IOV application in the future.
In order to guarantee the network security of IOV, according to the worm propagation and precaution-cure against worm, IOV worm propagation model and law in realistic road network had been deeply analysed and researched, and a novel anti-worm strategy for precaution-cure has been proposed based on this propagation model and law.
Firstly, based on the different typical factor of IOV compared with traditional networks, which could influence worm propagation, for example, vehicle nodes in complex road conditions omnirange driving, the barrier, interference and superposition influence of road environment on wireless communication signal, etc, according to worm propagation, combined the Intelligent Driver Model, a novel IOV worm propagation model (IOVWPM) was constructed. IOVWPM was constituted with motility and contact probability model, the mobile channel and link probability model and dynamics of IOV worm propagation model of vehicle nodes, could preferably show the influence over worm propagation by these components. Simulation experiment results was consistent with the theory of IOVWPM IOVWPM could actually simulate the worm propagation and provide a theoretical basis for programming real-time detection strategy and preventing worm destructive propagation in IOV.
Secondly, according to the characteristic worm propagation and prevention-cure demand different from traditional networks, a novel anti-worm model in IOV based on divide-and-Conquer with velocity (IOVAWM) was constructed based on divide-and-conquer with velocity. This paper treated the drive velocity of vehicle node as the switch condition between active and passive anti-worms in hybrid anti-worms, and controled alternating action of active and passive anti-worms based on this switch condition, so as to carry forward advantages and to avoid disadvantages of them. Simulation experiment results showed the better effect of IOVAWM for worm prevention-cure could be achieved, could not only better contain the IOV worm propagation, but also could better hold down the network resource spending in anti-worm.
Thirdly, according to the particular driving condition and notwork condition of expressway network, the worm propagation law in expressway network has been summarized, and simulation experiment results showed the better effect of IOVAWM for worm prevention-cure also could be achieved in expressway network, could not only better contain the IOV worm propagation, but also could better hold down the network resource spending in anti-worm.
Finally, according to the dynamic characteristic of IOV communication cluster and unstable connectivity characteristic among vehicle nodes in expressway network, a novel passive pesponse anti-worm model in IOV based on unstable connectivity environment (IOVPRAWM) was constructed. Anti-worm passive pesponse mechanism eliminated the network resource spending by active anti-worm sending round probe packets actively, and could traceability send anti-worm back for containing; and the anti-worm transmission mechanism based on unstable connectivity environment ensured anti-worm's effective transmission in unstable connectivity condition. Simulation experiment results showed IOVAWM could not only better contain the IOV worm propagation, but also could better eliminate the network resource spending by active anti-worm sending round probe packets actively and hold down the network resource spending in anti-worm. Beneficial exploration has been made for network security protection in expressway network IOV environment, and effective prevention-cure worm destructive propagation.
引文
[1]中华人民共和国国家统计局.统计局发布2011年国民经济和社会发展统计公报[EB/OL]. http://www.gov.cn/gzdt/2012-02/22/content_2073982.htm,2012-02-22.
[2]中华人民共和国公安部.公安部、交通运输部联合部署加强客货运驾驶人安全管理工作[EB/OL]. http://www.mps.gov.cn/n16/nl252/n1837/n2557/3129323.html, 2012-02-07.
[3]Exxon Mobil. The Outlook for Energy: A View to 2040[EB/OL]. http://www.exxonmobil.com/Corporate/Files/news_pub_eo2012.pdf,2011-12-06.
[4]Irshad A S, Halabi H, Manan J A, et al. A Novel Vehicular SMS System Approach for Intelligent Transport System [A]. Proceedings of the 2011 11th International Conference on ITS Telecommunications[C]. Tronoh, Malaysia: Comput.& Inf. Sci. Dept.,2010:540-546.
[5]Killat M, Schmidt-Eisenlohr F, Hartenstein H, et al. Enabling Efficient and Accurate Large-scale Simulations of VANETs for Vehicular Traffic Management[A]. Proceedings of the 4th ACM International Workshop on Vehicular Ad hoc Networks[C]. Montreal, QC, Canada: Association for Computing Machinery,2007: 29-38.
[6]Wang Z, Kulik L, Ramamohanarao K. Proactive Traffic Merging Strategies for Sensor-Enabled Cars[A]. Proceedings of the 4th ACM International Workshop on Vehicular Ad hoc Networks[C]. Montreal, QC, Canada: Association for Computing Machinery,2007:39-48.
[7]国家计算机网络应急技术处理协调中心.2011年中国互联网网络安全报告[R].http://www.cert.org.cn/publish/main/46/2012/20120523085533341215471/20120523 085533341215471.html,2012-05-23.
[8]International Telecommunications Union. The Internet of things[R]. Geneva, Swiss: International Telecommunications Union Internet Reports,2005.
[9]吴功宜.智慧的物联网——感知中国和世界的技术[M].北京：机械工业出版社,2010：15-16.
[10]Manyam V N, Chhetri D, Wikner J J. Clockless Asynchronous Delta Modulator Based ADC for Smart Dust Applications[A]. Proceedings of the 2011 IEEE/IFIP 19th International Conference on VLSI and System-on-Chip[C]. Hong Kong, China: IEEE Computer Society Press,2011:331-336.
[11]IBM商业价值研究院.智慧地球赢在中国[EB/OL].http://www.ibm.com/smarterplanet/global/files/cn_zh_cn_overview_decadeofsm art_wininchina.pdf,2009-04-28.
[12]The BLED Declaration: Towards a European Approach to the Future Internet[EB/OL]. http://ec.europa.eu/information_society/activities/foi/library/docs/153-08_foi_manife sto.pdf.
[13]Commission of the European Communites. Internet of Things—An Action Plan for Europe[EB/OL]. http://ec.europa.eu/information_society/policy/rfid/documents/commiot2009.pdf
[14]Nan J, Cui, S, Chai Z. Research on CAN Bus of Pure Electric Vehicles Based on SAEJ1939[A]. Advanced Materials Research[C]. Hohhot, China:Trans Tech Publications,2012,2650-2656.
[15]Esch S, Meyer J, LinnG. Partial Networking - Deactivation of Inactive ECUs[J]. AutoTechnology,2012,12(1):52-57.
[16]唐小淋,林培群,徐建闽.基于云计算和WSN的车联网体系架构及关键技术研究[J].交通信息与安全,2011,29(5)：106-111.
[17]李辉道,廖宇.交通云——云计算在智能交通中的应用探讨[J].中国智能交通,2011,96(3)：56-59.
[18]EIMoustapha O, Douglas M B, Bonnie H F, George F, et al. Distributed Global ID Assignment for Wireless Sensor Networks [J]. Ad hoc networks,2009,7(6): 1194-1216.
[19]Lee U, Magistretti E, Zhou B, et al. MobEyes:Smart Mobs for Urban Monitoring with a Vehicular Sensor Network[J]. IEEE Wireless Communications,2006,13(5): 52-57.
[20]Lee U, Magistretti E, Zhou B, et al. Bio-inspired Multi-Agent Data Harvesting in a Proactive Urban Monitoring Environment[J]. Ad Hoc Networks,2009,7(4):725-741.
[21]FleetNet[EB/OL]. http://www.nw.neclab.eu/Projects/fleetnet.htm.
[22]Hull B, Bychkovsky V, Chen K. CarTel:A Distributed Mobile Sensor Computing System[A]. Proceedings of ACM International Conference on Embedded Networked Sensor Systems[C]. Boulder, Colorado, USA:Association for Computing Machinery, 2006:125-138.
[23]MIT. CarTel[EB/OL]. http://cartel.csail.mit.edu/doku.php.
[24]Conner M. Automobile Sensors may Usher in Self-driving Cars[J]. Journal article, 2011,56(10):38-40.
[25]Thrun S. Self-driving Cars -an AI-robotics Challenge[A]. Proceedings of the Twentieth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2007[C]. Key West, FL, USA:American Association for Artificial Intelligence,2007:12.
[26]Dolgov D, Thrun S. Autonomous Driving in Semi-structured Environments: Mapping and Planning[A]. Proceedings of the Robotics and Automation, ICRA 2009[C]. Kobe, Japan:IEEE Computer Society,2009:3407-3414.
[27]Google Official Blog. The Self-driving Car Logs More Miles on New Wheels [EB/OL]. http://googleblog.blogspot.hu/2012/08/the-self-driving-car-logs-more-miles-on.html, 2012-08-08.
[28]戴斌,聂一鸣,孙振平,等.地面无人驾驶技术现状及应用[J].汽车与安全,2012,3：46-49.
[29]刘建航,孙江明,毕经平,等.基于动态时槽的车联网协助下载方法研究[J].计算机学报,2011,34(8)：1378-1386.
[30]王景欣,王钺,耿军伟,等.基于匿名互换的车联网安全与隐私保护机制[J].清华大学学报,2012,52(5)：592-597.
[31]Szczurek P, Xu B, Wolfson O, et al. A Methodology for the Development of Novel VANET Safety Applications[A]. Proceedings of the 9th ACM International Workshop on VehiculAr Inter-NETworking, Systems, and Applications[C]. Low Wood Bay, Lake District, United kingdom:Association for Computing Machinery, 2012:119-121.
[32]Singh R, Babu R, Chatterjee I. A Reliable Multicast Routing Approach for VANET[J]. International Journal of Computer Science Issues,2012,9(2):575-579.
[33]Yu B, Xu C, Guo M. Adaptive Forwarding Delay Control for VANET Data Aggregation[J]. IEEE Transactions on Parallel and Distributed Systems,2012,23(1): 11-18.
[34]Azimi S R, Bhatia G, Rajkumar R, et al. Vehicular Networks for Collision Avoidance at Intersections[J]. SAE International Journal of Passenger Cars—Mechanical Systems,2011,4(1):406-416.
[35]Cohen F. Computer Viruses:Theory and Experiments[A]. Proceedings of the Second IFIP International Conference on Computer Security[C]. Toronto, Ont, Canada: North-Holland,1984:143-158.
[36]Typel S, Baur G. Theory of the Trojan-Horse Method[J]. Annals of Physics,2003, 305(2):228-265.
[37]Hruska J. Data Viruses, Trojan Horses and Logic Bombs—How to Combat Them?[J]. Computer Fraud & Security Bulletin,1988,10(6):5-7.
[38]Kanthe A M, Simunic D, Djurek M. Denial of Service (DoS) Attacks in Green Mobile Ad-hoc Networks[A]. Proceedings of the 35th International Convention on Information and Communication Technology, Electronics and Microelectronics[C]. Opatija, Croatia:IEEE,2012:21-25.
[39]Nikolaidis I. Malware:Fighting Malicious Code[M]. NewYork:PrentieeHall.2004: 6-10.
[40]Schoch J F, Hupp J A. The "Worm" Programs—Early Experience with a Distributed Computation[J]. Communications of the ACM,1982,25(3):172-180.
[41]Spafford E H. The Internet Worm Program:An Analysis[J]. Computer Communication Review,1989,19(1):17-57.
[42]Moore D, Shannon C, Claffy K. Code-Red:A Case Study on the Spread and Victims of an Internet Worm [A]. Proceedings of the Second Internet Measurement Workshop[C]. Marseille, France: Association for Computing Machinery,2002: 273-284.
[43]Information Security: Code Red, Code Red II, and SirCam Attacks Highlight Need for Proactive Measures[R]. Washington, DC, USA:General Accounting Office, 2001.
[44]Clark E. Centex nixes Nimda at the exits [Web site protection][J]. Network Magazine, 2002,17(9):62-66.
[45]Vaas L. SQL Server repels Slammer[J]. IT Week,2003,6(7):19.
[46]Ferrie P, Perriot F, Szor P. Blast off! [Analysis of the Win32/Blaster Worm][J]. Virus Bulletin,2003, (9):10-11.
[47]张运凯,王长广,王方伟,等.“震荡波”蠕虫分析与防范[J].计算机工程,2005,31(18)：65-67.
[48]Yu Y, Xiang W L, Hao G, et al. Diurnal Forced Models for Worm Propagation Based on Conficker Dataset[A]. Proceedings of the 3rd International Conference on Multimedia Information Networking and Security[C]. Shanghai, China:IEEE Computer Society,2011:431-435.
[49]Sanger D. Confront and Conceal:Obama's Secret Wars and Surprising Use of American Power[M]. New York, NY, USA:Crown Publishers Inc,2012.
[50]卡巴斯基实验室Duqu蠕虫的针对性攻击目标位于伊朗和苏丹[EB/OL].http://www.kaspersky.com.cn/KL-AboutUs/news2011/1 On/111027a.htm, 2011-10-27.
[51]卡巴斯基实验室.关于Flame的问题与回答[EB/OL].http://www.kaspersky.com.cn/KL-AboutUs/news2012/05n/120530.htm,2012-05-30.
[52]金山网络,TechWeb,机锋网.2011-2012年度智能手机安全报告[R].北京：金山网络,2012.
[53]郑辉Internet蠕虫研究[D].天津：南开大学信息技术科学学院,2003.
[54]文伟平,卿斯汉,蒋建春,等.网络蠕虫研究与进展[J].软件学报,2004,15(08)：1208-1219.
[55]Nazario J, Anderson J, Wash R, et al. The Future of Internet Worms[EB/OL]. http://wenku.baidu.com/view/7c06f0a3284ac850ad0242cl.html,2001-06-03.
[56]Pele L, Salour M, Xiao S. A Survey of Internet Worm Detection and Containment[J]. IEEE Communications Surveys & Tutorials,2008,10(1):20-35.
[57]Vogt T. Simulating and Optimizing Worm Propagation Algorithms[EB/OL]. http://web.lemuria.org/security/WormPropagation.pdf,2004-02-16.
[58]Nicholas W, Vern P, Stuart S, et al. A Taxonomy of Computer Worms[A]. Proceedings of the 2003 ACM Workshop on Rapid Malcode[C]. Washington, DC, United states:ACM SIGSAC,2003:11-18.
[59]Streftaris G, Gibson G J. Statistical Inference for Stochastic Epidemic Models[A]. In: Proc. of the 17th Int'l Workshop on Statistical Modelling[C]. Chania, Greece:ACM, 2002:609-616.
[60]Frauenthal J C. Mathematical Modeling in Epidemiology[M]. New York, USA: Springer,1980.
[61]Zou C C, Gong W, Towsley D. Code Red Worm Propagation Modeling and Analysis[A]. Proceedings of the 9th ACM Symp. on Computer and Communication Security[C]. Washington, DC, United states:ACM,2002:138-147.
[62]Kim H A, Karp B. Autograph:Toward Automated, Distributed Worm Signature Detection[A]. Proceedings of the 13th USENIX Security Symposium[C]. Berkeley, CA, USA: USENIX Association,2004:271-286.
[63]Singh S, Estan C, Varghese, G, et al. Automated Worm Fingerprinting [A]. Proceedings of the Sixth Symposium on Operating Systems Design and Implementation[C]. Berkeley, CA, USA:USENIX Association,2004:45-60.
[64]Whyte D, Kranakis E, Oorsehot P. DNS-based Detection of Scanning Worms in an Enterprise Network[A]. Proeeedings of the 12th Annual Network and Distributed System Security Symposium[C] San Diego, USA:ACM,2005:185-195.
[65]Staniford C S, Cheung S, Crawford R, et al. GrIDS:A Graph Based Intrusion Detection System for Large Networks[A]. Proeeedings of the 19th National Information Systems Seeurity[C]. Baltimore USA:ACM,1996:361-370.
[66]Kim B, Bahk S. Relative Entropy-based Filtering of Internet Worms by Inspecting TCP SYN Retry Packets[A]. Proeeedings of 21st International Conference on Advanced Information Networking and Applications Workshops[C]. Niagara Falls, Ont., Canada: IEEE,2007:21-23.
[67]WU J, Vangala S, Gao L, et al. An Efficient Architecture and Algorithm for Detecting Worms with Various Scan Techniques [A]. Proceedings of the 11th Annual Network and Distributed system Security Sylnposium[C].2004:143-156.
[68]Madhusudan B, Lockwood. Design of A System for Real-time Worm Detection[A]. Proceedings.12th Annual IEEE Symposium on High Performance.Interconnects[C]. Stanford, CA, USA:IEEE Comput. Soc,2004:77-83.
[69]Cynthia W, Chenxi W, Dawn S, et al. Dynamic Quarantine of Internet Worms[A]. Proceedings of the International Conference on Dependable Systems and Networks[C]. Florence, Italy: IEEE Computer Society,2004:73-82.
[70]Weaver N, Staniford S, Paxson V. Very Fast Containment of Scanning Worms[A]. Proceedings of the 13th USENIX Security Symposium[C]. San Diego, CA, USA: USENIX Association,2004:29-44.
[71]Chen S, Tang Y. Slowing Down Internet Worms[A]. Proceedings of International Conference on Distributed Computing Systems[C]. Hachioji, Tokyo, Japan:IEEE Computer Society,2004:312-319.
[72]Ahmed M, Zubairi J A, Ismail M, et al. Smart Automated Disinfection System to Counter Worm Propagation in Small Networks Using 'Honeypot Framework'[A]. Proceedings of the 2006 Spring Simulation Multiconference[C]. Huntsville, AL, USA:Society for Modeling and Simulation International,2006:317-322.
[73]苏飞,林昭,文马严,等.IPv6网络环境下的蠕虫传播模型研究[J].通信学报,2011,32(9)：51-60.
[74]Li J, Su F, Lin Z, et al The Research and Analysis of Worm Scanning Strategies in IPv6 Network[A]. Proceedings of the 13th Asia-Pacific Network Operations and Management Symposium:Managing Clouds, Smart Networks and Services, Final Program[C]. Taipei, Chinese Taiwan, IEEE Computer Society,2011:1-4.
[75]刘烃,郑庆华,管晓东,等.IPv6网络中蠕虫传播模型及分析[J].计算机学报[J]2006,29(8)：1337-1345.
[76]Kamra A, Feng H, Misra V, et al. The Effect of DNS Delays on Worm Propagation in an IPv6 Internet[A]. Proceedings of IEEE Infocom 2005[C]. Miami, FL, USA:IEEE, 2005:2405-2414.
[77]Xu Y, Zhou J, Li X, et al. Worm Detection in an IPv6 Internet[A]. Proceedings of the 2009 International Conference on Computational Intelligence and Security[C]. Beijing, China: IEEE Computer Society,2009:366-370.
[78]Kong F, Tan J. A. Collaboration-based Hybrid Vehicular Sensor Network Architecture[A]. Proceedings of International Conference on Information and Automation[C]. Changsha, China: IEEE Computer Society,2008:584-589.
[79]IETF.6LoWPAN (Version 4.34)[EB/OL]. http://datatracker.ietf.org/wg/61owpan/charter,2012-08-22.
[80]IETF. ROLL (Version 4.34)[EB/OL]. http://datatracker.ietf.org/wg/roll/charter, 2012-08-22.
[81]IETF. CoRE (Version 4.34)[EB/OL]. http://datatracker.ietf.org/wg/core/charter, 2012-08-22.
[82]林丰波.IPv6和IPSec概论[EB/OL].http://wenku.baidu.com/view/57481eflba0d4a7302763 aa8.html?from_page=view&f rom_mod=copy_login
[83]MAXIN R, HUBAUX J. Security Aspects of Inter-vehicle Communications[A]. Proceedings of the 5th Swiss Transport Research Conference[C]. Ascona, Swiss: IEEE Computer Society Press,2005:49-55.
[84]Chen T, Robert J M. Worm Epidemics in High-speed Networks[J]. Computer,2004: 37(6) 48-53.
[85]Chen Z, Gao L, Kwiat K. Modeling the Spread of Active Worms[A]. Proceedings of the IEEE INFOCOM,2003[C]. San Francisco, USA:IEEE Computer and Communications Societies,2003:(2) 1890-1990.
[86]Cole R G., Phamdo N, Rajab M A, et al. Requirements on Worm Mitigation Technologies in MANTS[C]. Proceedings of the 19th Workshop on Principles of Advanced and Distributed Simulation. Monterey, USA:IEEE Computer Society, 2005:207-214.
[87]李舒,张栋良,蒋昌俊.车载无线自组网络连通性分析[J].系统仿真学报,2009：21(3),888-893.
[88]Treiber M, Hennecke A, Helbing D. Congested Traffic States in Empirical Observations and Microscopic Simulations[J]. Physical Review E,2000,62(2): 1805-1824.
[89]Helbing D. Traffic and Related Self-driven Many-particle Systems[J]. Reviews of Modern Physics,2001,73(4):1067-1141.
[90]Rhodes C J, Anderson R M. Contact Rate Calculation for a Basic Epidemic Model[J]. Mathematical Biosciences,2008,216(1):56-62.
[91]Ott J, Kutscher D. Drive-thru Internet: IEEE 802.11b for "Automobile" Users[A]. Proceedings of the 23th AnnualJoint Conference of the IEEE[C]. Hong Kong, China: IEEE Computer and Communications Societies,2004, (1):7-11.
[92]Bychkovske V, Hull B, Miu A K, et al. A Measurement Study of Vehicular Internet Access Using in Situ Wi-Fi Networks[A]. Proceedings of the 12th Annual International Conference on Mobile Computing and Communications[C]. New York, USA:Association for Computer Machinery,2006:50-61.
[93]Hadaller D, Keshav S, Brecht T, et al. Vehicular Opportunistic Communication Under the Microscope[A]. Proceedings of the 5th International Conference on Mobile Systems, Applications and Services[C]. New York, USA:Association for Computer Machinery,2007:206-219.
[94]Armstrong L. Dedicated Short Range Communications Project [EB/OL]. http://www.leearmstrong.com/DSRC/DSRCHomeset.htm,2004-10-27
[95]IEEE 802.11p-2008, WAVE-Wireless Access for the Vehicular Environment[S].
[96]Dissanayake S D, Karunasekara P P C, Lakmanaarachchi D D, et al. Zigbee Wireless Vehicular Identification and Authentication System[A] Proceedings of the 4th International Conference on Information and Automation for Sustainability. Sustainable Development through Effective Man-machine Co-existence[C]. Colombo, Sri Lanka:IEEE,2008:256-260.
[97]Chumkamon S, Tuvaphanthaphiphat P, Keeratiwintakorn, P. The Vertical Handoff between GSM and Zigbee Networks for Vehicular Communication[A]. Proceedings of the 7th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology[C]. Chaing Mai, Thailand:IEEE Computer Society,2010:603-606.
[98]Bettstetter C, Hartmann C.Connectivity of Wireless Multihop Networks in a Shadow Fading Environment[A]. Proceedings of the International Workshop on Modeling, Analysis and Simulation of Wireless and Mobile Systems[C]. San Diego, USA: Springer,2003,11(5):571-579.
[99]Helbing D. Traffic and Related Self-driven Many-particle Systems[J]. Reviews of Modern Physics,2001,73(4):1067-1141.
[100]Sim M L, Nekovee M, Ko Y F. Throughput Analysis of WiFi Based Broadband Access for Mobile Users on Highways[A]. Proceedings of the 3th IEEE International Conference on Networks jointly held with the 2005 7th IEEE Malaysia International Conference on Communications [C]. Kuala Lampur, Malaysia: Computer Society, 2005:21-26.
[101]Singh J P, Bambos N, Srinivasan B, et al. Wireless LAN Performance under Varied Stress Conditions in Vehicular Traffic Scenarios[A]. Proceedings of the IEEE Vehicular Technology Conference[C]. Vancouver, Canada: Institute of Electrical and Electronics Engineers Inc,2002,56(2):743-747.
[102]Beretta E, Takeuchi Y. Convergence Results in SIR Epidemic Models with Varying Population Sizes[J]. Nonlinear Analysis:Theory, Methods and Applications,1997, 28(12):1909-1921.
[103]Zou C C, Gao L, Gong W, et al. Monitoring and Early Warning for Internet Worms[A]. Proceedings of the 10th ACM Conference on Computer and Communications Securit[C]. Wshington DC, USA:Association for Computing Machinery,2003:190-199.
[104]Frank C, Emre C S, Xu J. Worm vs. Worm:Preliminary Study of an Active Counter-attack Mechanism [A]. Proceedings of the 2004 ACM workshop on rapid malcode[C]. Wshington DC, USA:Association for Computing Machinery,2004: 83-93
[105]Fang Y, Zheng X, A Revised Benign Worm-Anti-Worm Propagation Model[J]. Applied Mechanics and Materials,2011,121-126:4340-4344.
[106]Liu X, Hu Z, Liu G, et al. Defending P2P Networks Based on Benign Worms[J]. Journal of Computational Information Systems,2011,7(7):2532-2539.
[107]Kammas P, Komninos T, Stamatiou Y C. Modeling the Co-evololution DNS Worms and Anti-worms in IPv6 Networks[A]. Proceedings of the 5th International Conference on Information Assurance and Security[C]. Xian, China:IEEE Computer Society,2009:171-174.
[108]王佰玲,方滨兴,云晓春,等.基于平衡树的良性蠕虫扩散策略[J].计算机研究与发展,2006,43(9)：1593-1602.
[109]周翰逊,赵宏.主动良性蠕虫和混合良性蠕虫的建模与分析[J].计算机研究与 发展,2007,44(6)：958-954.
[110]Nazario J, Anderson J, Wash R, et al. The Future of Internet Worms[EB/OL]. http://wenku.baidu.com/view/7c06f0a3284ac850ad0242cl.html,2001-06-03.
[111]Bailey M, Cooke E, Jahanian F, et al. The Internet Motion Sensor: A Distributed Blackhole Monitoring System[A]. Proceedings of the 12th ISOC Symposium on Network and Distributed Systems Security[C]. San Diego, USA:IEEE Computer Society Press,2005:167-179.
[112]Wu H, Fujimoto R, Guensler R, et al. Analytical Models for Information Propagation in Vehicle-to-vehicle Networks [A]. Proceedings of the IEEE Vehicular Technology Conference 2004[C]. Los Angles, USA:IEEE Computer Society Press,2004: 4548-4552.
[113]Nzoonta J, C. Borcea. STEID:A Protocl for Emergency Information Dissemination In Vehicular Networks[R]. Newark, USA:New Jersey Institute of Technology,2006.
[114]Little T D, Agarwal A. An information propagation scheme for VANETs[A]. Proceedings of the 2005 IEEE Intelligent Transportation Systems Conference[C] Vienna, Austria:IEEE Computer Society Press,2005:155-160.
[115]Eugster P, Guerraoui R, Kermarrec A. From Epidemics to Distributed Computing[J]. IEEE Computer,2004,59(4):60-67.
[2]中华人民共和国公安部.公安部、交通运输部联合部署加强客货运驾驶人安全管理工作[EB/OL]. http://www.mps.gov.cn/n16/nl252/n1837/n2557/3129323.html, 2012-02-07.
[3]Exxon Mobil. The Outlook for Energy: A View to 2040[EB/OL]. http://www.exxonmobil.com/Corporate/Files/news_pub_eo2012.pdf,2011-12-06.
[4]Irshad A S, Halabi H, Manan J A, et al. A Novel Vehicular SMS System Approach for Intelligent Transport System [A]. Proceedings of the 2011 11th International Conference on ITS Telecommunications[C]. Tronoh, Malaysia: Comput.& Inf. Sci. Dept.,2010:540-546.
[5]Killat M, Schmidt-Eisenlohr F, Hartenstein H, et al. Enabling Efficient and Accurate Large-scale Simulations of VANETs for Vehicular Traffic Management[A]. Proceedings of the 4th ACM International Workshop on Vehicular Ad hoc Networks[C]. Montreal, QC, Canada: Association for Computing Machinery,2007: 29-38.
[6]Wang Z, Kulik L, Ramamohanarao K. Proactive Traffic Merging Strategies for Sensor-Enabled Cars[A]. Proceedings of the 4th ACM International Workshop on Vehicular Ad hoc Networks[C]. Montreal, QC, Canada: Association for Computing Machinery,2007:39-48.
[7]国家计算机网络应急技术处理协调中心.2011年中国互联网网络安全报告[R].http://www.cert.org.cn/publish/main/46/2012/20120523085533341215471/20120523 085533341215471.html,2012-05-23.
[8]International Telecommunications Union. The Internet of things[R]. Geneva, Swiss: International Telecommunications Union Internet Reports,2005.
[9]吴功宜.智慧的物联网——感知中国和世界的技术[M].北京：机械工业出版社,2010：15-16.
[10]Manyam V N, Chhetri D, Wikner J J. Clockless Asynchronous Delta Modulator Based ADC for Smart Dust Applications[A]. Proceedings of the 2011 IEEE/IFIP 19th International Conference on VLSI and System-on-Chip[C]. Hong Kong, China: IEEE Computer Society Press,2011:331-336.
[11]IBM商业价值研究院.智慧地球赢在中国[EB/OL].http://www.ibm.com/smarterplanet/global/files/cn_zh_cn_overview_decadeofsm art_wininchina.pdf,2009-04-28.
[12]The BLED Declaration: Towards a European Approach to the Future Internet[EB/OL]. http://ec.europa.eu/information_society/activities/foi/library/docs/153-08_foi_manife sto.pdf.
[13]Commission of the European Communites. Internet of Things—An Action Plan for Europe[EB/OL]. http://ec.europa.eu/information_society/policy/rfid/documents/commiot2009.pdf
[14]Nan J, Cui, S, Chai Z. Research on CAN Bus of Pure Electric Vehicles Based on SAEJ1939[A]. Advanced Materials Research[C]. Hohhot, China:Trans Tech Publications,2012,2650-2656.
[15]Esch S, Meyer J, LinnG. Partial Networking - Deactivation of Inactive ECUs[J]. AutoTechnology,2012,12(1):52-57.
[16]唐小淋,林培群,徐建闽.基于云计算和WSN的车联网体系架构及关键技术研究[J].交通信息与安全,2011,29(5)：106-111.
[17]李辉道,廖宇.交通云——云计算在智能交通中的应用探讨[J].中国智能交通,2011,96(3)：56-59.
[18]EIMoustapha O, Douglas M B, Bonnie H F, George F, et al. Distributed Global ID Assignment for Wireless Sensor Networks [J]. Ad hoc networks,2009,7(6): 1194-1216.
[19]Lee U, Magistretti E, Zhou B, et al. MobEyes:Smart Mobs for Urban Monitoring with a Vehicular Sensor Network[J]. IEEE Wireless Communications,2006,13(5): 52-57.
[20]Lee U, Magistretti E, Zhou B, et al. Bio-inspired Multi-Agent Data Harvesting in a Proactive Urban Monitoring Environment[J]. Ad Hoc Networks,2009,7(4):725-741.
[21]FleetNet[EB/OL]. http://www.nw.neclab.eu/Projects/fleetnet.htm.
[22]Hull B, Bychkovsky V, Chen K. CarTel:A Distributed Mobile Sensor Computing System[A]. Proceedings of ACM International Conference on Embedded Networked Sensor Systems[C]. Boulder, Colorado, USA:Association for Computing Machinery, 2006:125-138.
[23]MIT. CarTel[EB/OL]. http://cartel.csail.mit.edu/doku.php.
[24]Conner M. Automobile Sensors may Usher in Self-driving Cars[J]. Journal article, 2011,56(10):38-40.
[25]Thrun S. Self-driving Cars -an AI-robotics Challenge[A]. Proceedings of the Twentieth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2007[C]. Key West, FL, USA:American Association for Artificial Intelligence,2007:12.
[26]Dolgov D, Thrun S. Autonomous Driving in Semi-structured Environments: Mapping and Planning[A]. Proceedings of the Robotics and Automation, ICRA 2009[C]. Kobe, Japan:IEEE Computer Society,2009:3407-3414.
[27]Google Official Blog. The Self-driving Car Logs More Miles on New Wheels [EB/OL]. http://googleblog.blogspot.hu/2012/08/the-self-driving-car-logs-more-miles-on.html, 2012-08-08.
[28]戴斌,聂一鸣,孙振平,等.地面无人驾驶技术现状及应用[J].汽车与安全,2012,3：46-49.
[29]刘建航,孙江明,毕经平,等.基于动态时槽的车联网协助下载方法研究[J].计算机学报,2011,34(8)：1378-1386.
[30]王景欣,王钺,耿军伟,等.基于匿名互换的车联网安全与隐私保护机制[J].清华大学学报,2012,52(5)：592-597.
[31]Szczurek P, Xu B, Wolfson O, et al. A Methodology for the Development of Novel VANET Safety Applications[A]. Proceedings of the 9th ACM International Workshop on VehiculAr Inter-NETworking, Systems, and Applications[C]. Low Wood Bay, Lake District, United kingdom:Association for Computing Machinery, 2012:119-121.
[32]Singh R, Babu R, Chatterjee I. A Reliable Multicast Routing Approach for VANET[J]. International Journal of Computer Science Issues,2012,9(2):575-579.
[33]Yu B, Xu C, Guo M. Adaptive Forwarding Delay Control for VANET Data Aggregation[J]. IEEE Transactions on Parallel and Distributed Systems,2012,23(1): 11-18.
[34]Azimi S R, Bhatia G, Rajkumar R, et al. Vehicular Networks for Collision Avoidance at Intersections[J]. SAE International Journal of Passenger Cars—Mechanical Systems,2011,4(1):406-416.
[35]Cohen F. Computer Viruses:Theory and Experiments[A]. Proceedings of the Second IFIP International Conference on Computer Security[C]. Toronto, Ont, Canada: North-Holland,1984:143-158.
[36]Typel S, Baur G. Theory of the Trojan-Horse Method[J]. Annals of Physics,2003, 305(2):228-265.
[37]Hruska J. Data Viruses, Trojan Horses and Logic Bombs—How to Combat Them?[J]. Computer Fraud & Security Bulletin,1988,10(6):5-7.
[38]Kanthe A M, Simunic D, Djurek M. Denial of Service (DoS) Attacks in Green Mobile Ad-hoc Networks[A]. Proceedings of the 35th International Convention on Information and Communication Technology, Electronics and Microelectronics[C]. Opatija, Croatia:IEEE,2012:21-25.
[39]Nikolaidis I. Malware:Fighting Malicious Code[M]. NewYork:PrentieeHall.2004: 6-10.
[40]Schoch J F, Hupp J A. The "Worm" Programs—Early Experience with a Distributed Computation[J]. Communications of the ACM,1982,25(3):172-180.
[41]Spafford E H. The Internet Worm Program:An Analysis[J]. Computer Communication Review,1989,19(1):17-57.
[42]Moore D, Shannon C, Claffy K. Code-Red:A Case Study on the Spread and Victims of an Internet Worm [A]. Proceedings of the Second Internet Measurement Workshop[C]. Marseille, France: Association for Computing Machinery,2002: 273-284.
[43]Information Security: Code Red, Code Red II, and SirCam Attacks Highlight Need for Proactive Measures[R]. Washington, DC, USA:General Accounting Office, 2001.
[44]Clark E. Centex nixes Nimda at the exits [Web site protection][J]. Network Magazine, 2002,17(9):62-66.
[45]Vaas L. SQL Server repels Slammer[J]. IT Week,2003,6(7):19.
[46]Ferrie P, Perriot F, Szor P. Blast off! [Analysis of the Win32/Blaster Worm][J]. Virus Bulletin,2003, (9):10-11.
[47]张运凯,王长广,王方伟,等.“震荡波”蠕虫分析与防范[J].计算机工程,2005,31(18)：65-67.
[48]Yu Y, Xiang W L, Hao G, et al. Diurnal Forced Models for Worm Propagation Based on Conficker Dataset[A]. Proceedings of the 3rd International Conference on Multimedia Information Networking and Security[C]. Shanghai, China:IEEE Computer Society,2011:431-435.
[49]Sanger D. Confront and Conceal:Obama's Secret Wars and Surprising Use of American Power[M]. New York, NY, USA:Crown Publishers Inc,2012.
[50]卡巴斯基实验室Duqu蠕虫的针对性攻击目标位于伊朗和苏丹[EB/OL].http://www.kaspersky.com.cn/KL-AboutUs/news2011/1 On/111027a.htm, 2011-10-27.
[51]卡巴斯基实验室.关于Flame的问题与回答[EB/OL].http://www.kaspersky.com.cn/KL-AboutUs/news2012/05n/120530.htm,2012-05-30.
[52]金山网络,TechWeb,机锋网.2011-2012年度智能手机安全报告[R].北京：金山网络,2012.
[53]郑辉Internet蠕虫研究[D].天津：南开大学信息技术科学学院,2003.
[54]文伟平,卿斯汉,蒋建春,等.网络蠕虫研究与进展[J].软件学报,2004,15(08)：1208-1219.
[55]Nazario J, Anderson J, Wash R, et al. The Future of Internet Worms[EB/OL]. http://wenku.baidu.com/view/7c06f0a3284ac850ad0242cl.html,2001-06-03.
[56]Pele L, Salour M, Xiao S. A Survey of Internet Worm Detection and Containment[J]. IEEE Communications Surveys & Tutorials,2008,10(1):20-35.
[57]Vogt T. Simulating and Optimizing Worm Propagation Algorithms[EB/OL]. http://web.lemuria.org/security/WormPropagation.pdf,2004-02-16.
[58]Nicholas W, Vern P, Stuart S, et al. A Taxonomy of Computer Worms[A]. Proceedings of the 2003 ACM Workshop on Rapid Malcode[C]. Washington, DC, United states:ACM SIGSAC,2003:11-18.
[59]Streftaris G, Gibson G J. Statistical Inference for Stochastic Epidemic Models[A]. In: Proc. of the 17th Int'l Workshop on Statistical Modelling[C]. Chania, Greece:ACM, 2002:609-616.
[60]Frauenthal J C. Mathematical Modeling in Epidemiology[M]. New York, USA: Springer,1980.
[61]Zou C C, Gong W, Towsley D. Code Red Worm Propagation Modeling and Analysis[A]. Proceedings of the 9th ACM Symp. on Computer and Communication Security[C]. Washington, DC, United states:ACM,2002:138-147.
[62]Kim H A, Karp B. Autograph:Toward Automated, Distributed Worm Signature Detection[A]. Proceedings of the 13th USENIX Security Symposium[C]. Berkeley, CA, USA: USENIX Association,2004:271-286.
[63]Singh S, Estan C, Varghese, G, et al. Automated Worm Fingerprinting [A]. Proceedings of the Sixth Symposium on Operating Systems Design and Implementation[C]. Berkeley, CA, USA:USENIX Association,2004:45-60.
[64]Whyte D, Kranakis E, Oorsehot P. DNS-based Detection of Scanning Worms in an Enterprise Network[A]. Proeeedings of the 12th Annual Network and Distributed System Security Symposium[C] San Diego, USA:ACM,2005:185-195.
[65]Staniford C S, Cheung S, Crawford R, et al. GrIDS:A Graph Based Intrusion Detection System for Large Networks[A]. Proeeedings of the 19th National Information Systems Seeurity[C]. Baltimore USA:ACM,1996:361-370.
[66]Kim B, Bahk S. Relative Entropy-based Filtering of Internet Worms by Inspecting TCP SYN Retry Packets[A]. Proeeedings of 21st International Conference on Advanced Information Networking and Applications Workshops[C]. Niagara Falls, Ont., Canada: IEEE,2007:21-23.
[67]WU J, Vangala S, Gao L, et al. An Efficient Architecture and Algorithm for Detecting Worms with Various Scan Techniques [A]. Proceedings of the 11th Annual Network and Distributed system Security Sylnposium[C].2004:143-156.
[68]Madhusudan B, Lockwood. Design of A System for Real-time Worm Detection[A]. Proceedings.12th Annual IEEE Symposium on High Performance.Interconnects[C]. Stanford, CA, USA:IEEE Comput. Soc,2004:77-83.
[69]Cynthia W, Chenxi W, Dawn S, et al. Dynamic Quarantine of Internet Worms[A]. Proceedings of the International Conference on Dependable Systems and Networks[C]. Florence, Italy: IEEE Computer Society,2004:73-82.
[70]Weaver N, Staniford S, Paxson V. Very Fast Containment of Scanning Worms[A]. Proceedings of the 13th USENIX Security Symposium[C]. San Diego, CA, USA: USENIX Association,2004:29-44.
[71]Chen S, Tang Y. Slowing Down Internet Worms[A]. Proceedings of International Conference on Distributed Computing Systems[C]. Hachioji, Tokyo, Japan:IEEE Computer Society,2004:312-319.
[72]Ahmed M, Zubairi J A, Ismail M, et al. Smart Automated Disinfection System to Counter Worm Propagation in Small Networks Using 'Honeypot Framework'[A]. Proceedings of the 2006 Spring Simulation Multiconference[C]. Huntsville, AL, USA:Society for Modeling and Simulation International,2006:317-322.
[73]苏飞,林昭,文马严,等.IPv6网络环境下的蠕虫传播模型研究[J].通信学报,2011,32(9)：51-60.
[74]Li J, Su F, Lin Z, et al The Research and Analysis of Worm Scanning Strategies in IPv6 Network[A]. Proceedings of the 13th Asia-Pacific Network Operations and Management Symposium:Managing Clouds, Smart Networks and Services, Final Program[C]. Taipei, Chinese Taiwan, IEEE Computer Society,2011:1-4.
[75]刘烃,郑庆华,管晓东,等.IPv6网络中蠕虫传播模型及分析[J].计算机学报[J]2006,29(8)：1337-1345.
[76]Kamra A, Feng H, Misra V, et al. The Effect of DNS Delays on Worm Propagation in an IPv6 Internet[A]. Proceedings of IEEE Infocom 2005[C]. Miami, FL, USA:IEEE, 2005:2405-2414.
[77]Xu Y, Zhou J, Li X, et al. Worm Detection in an IPv6 Internet[A]. Proceedings of the 2009 International Conference on Computational Intelligence and Security[C]. Beijing, China: IEEE Computer Society,2009:366-370.
[78]Kong F, Tan J. A. Collaboration-based Hybrid Vehicular Sensor Network Architecture[A]. Proceedings of International Conference on Information and Automation[C]. Changsha, China: IEEE Computer Society,2008:584-589.
[79]IETF.6LoWPAN (Version 4.34)[EB/OL]. http://datatracker.ietf.org/wg/61owpan/charter,2012-08-22.
[80]IETF. ROLL (Version 4.34)[EB/OL]. http://datatracker.ietf.org/wg/roll/charter, 2012-08-22.
[81]IETF. CoRE (Version 4.34)[EB/OL]. http://datatracker.ietf.org/wg/core/charter, 2012-08-22.
[82]林丰波.IPv6和IPSec概论[EB/OL].http://wenku.baidu.com/view/57481eflba0d4a7302763 aa8.html?from_page=view&f rom_mod=copy_login
[83]MAXIN R, HUBAUX J. Security Aspects of Inter-vehicle Communications[A]. Proceedings of the 5th Swiss Transport Research Conference[C]. Ascona, Swiss: IEEE Computer Society Press,2005:49-55.
[84]Chen T, Robert J M. Worm Epidemics in High-speed Networks[J]. Computer,2004: 37(6) 48-53.
[85]Chen Z, Gao L, Kwiat K. Modeling the Spread of Active Worms[A]. Proceedings of the IEEE INFOCOM,2003[C]. San Francisco, USA:IEEE Computer and Communications Societies,2003:(2) 1890-1990.
[86]Cole R G., Phamdo N, Rajab M A, et al. Requirements on Worm Mitigation Technologies in MANTS[C]. Proceedings of the 19th Workshop on Principles of Advanced and Distributed Simulation. Monterey, USA:IEEE Computer Society, 2005:207-214.
[87]李舒,张栋良,蒋昌俊.车载无线自组网络连通性分析[J].系统仿真学报,2009：21(3),888-893.
[88]Treiber M, Hennecke A, Helbing D. Congested Traffic States in Empirical Observations and Microscopic Simulations[J]. Physical Review E,2000,62(2): 1805-1824.
[89]Helbing D. Traffic and Related Self-driven Many-particle Systems[J]. Reviews of Modern Physics,2001,73(4):1067-1141.
[90]Rhodes C J, Anderson R M. Contact Rate Calculation for a Basic Epidemic Model[J]. Mathematical Biosciences,2008,216(1):56-62.
[91]Ott J, Kutscher D. Drive-thru Internet: IEEE 802.11b for "Automobile" Users[A]. Proceedings of the 23th AnnualJoint Conference of the IEEE[C]. Hong Kong, China: IEEE Computer and Communications Societies,2004, (1):7-11.
[92]Bychkovske V, Hull B, Miu A K, et al. A Measurement Study of Vehicular Internet Access Using in Situ Wi-Fi Networks[A]. Proceedings of the 12th Annual International Conference on Mobile Computing and Communications[C]. New York, USA:Association for Computer Machinery,2006:50-61.
[93]Hadaller D, Keshav S, Brecht T, et al. Vehicular Opportunistic Communication Under the Microscope[A]. Proceedings of the 5th International Conference on Mobile Systems, Applications and Services[C]. New York, USA:Association for Computer Machinery,2007:206-219.
[94]Armstrong L. Dedicated Short Range Communications Project [EB/OL]. http://www.leearmstrong.com/DSRC/DSRCHomeset.htm,2004-10-27
[95]IEEE 802.11p-2008, WAVE-Wireless Access for the Vehicular Environment[S].
[96]Dissanayake S D, Karunasekara P P C, Lakmanaarachchi D D, et al. Zigbee Wireless Vehicular Identification and Authentication System[A] Proceedings of the 4th International Conference on Information and Automation for Sustainability. Sustainable Development through Effective Man-machine Co-existence[C]. Colombo, Sri Lanka:IEEE,2008:256-260.
[97]Chumkamon S, Tuvaphanthaphiphat P, Keeratiwintakorn, P. The Vertical Handoff between GSM and Zigbee Networks for Vehicular Communication[A]. Proceedings of the 7th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology[C]. Chaing Mai, Thailand:IEEE Computer Society,2010:603-606.
[98]Bettstetter C, Hartmann C.Connectivity of Wireless Multihop Networks in a Shadow Fading Environment[A]. Proceedings of the International Workshop on Modeling, Analysis and Simulation of Wireless and Mobile Systems[C]. San Diego, USA: Springer,2003,11(5):571-579.
[99]Helbing D. Traffic and Related Self-driven Many-particle Systems[J]. Reviews of Modern Physics,2001,73(4):1067-1141.
[100]Sim M L, Nekovee M, Ko Y F. Throughput Analysis of WiFi Based Broadband Access for Mobile Users on Highways[A]. Proceedings of the 3th IEEE International Conference on Networks jointly held with the 2005 7th IEEE Malaysia International Conference on Communications [C]. Kuala Lampur, Malaysia: Computer Society, 2005:21-26.
[101]Singh J P, Bambos N, Srinivasan B, et al. Wireless LAN Performance under Varied Stress Conditions in Vehicular Traffic Scenarios[A]. Proceedings of the IEEE Vehicular Technology Conference[C]. Vancouver, Canada: Institute of Electrical and Electronics Engineers Inc,2002,56(2):743-747.
[102]Beretta E, Takeuchi Y. Convergence Results in SIR Epidemic Models with Varying Population Sizes[J]. Nonlinear Analysis:Theory, Methods and Applications,1997, 28(12):1909-1921.
[103]Zou C C, Gao L, Gong W, et al. Monitoring and Early Warning for Internet Worms[A]. Proceedings of the 10th ACM Conference on Computer and Communications Securit[C]. Wshington DC, USA:Association for Computing Machinery,2003:190-199.
[104]Frank C, Emre C S, Xu J. Worm vs. Worm:Preliminary Study of an Active Counter-attack Mechanism [A]. Proceedings of the 2004 ACM workshop on rapid malcode[C]. Wshington DC, USA:Association for Computing Machinery,2004: 83-93
[105]Fang Y, Zheng X, A Revised Benign Worm-Anti-Worm Propagation Model[J]. Applied Mechanics and Materials,2011,121-126:4340-4344.
[106]Liu X, Hu Z, Liu G, et al. Defending P2P Networks Based on Benign Worms[J]. Journal of Computational Information Systems,2011,7(7):2532-2539.
[107]Kammas P, Komninos T, Stamatiou Y C. Modeling the Co-evololution DNS Worms and Anti-worms in IPv6 Networks[A]. Proceedings of the 5th International Conference on Information Assurance and Security[C]. Xian, China:IEEE Computer Society,2009:171-174.
[108]王佰玲,方滨兴,云晓春,等.基于平衡树的良性蠕虫扩散策略[J].计算机研究与发展,2006,43(9)：1593-1602.
[109]周翰逊,赵宏.主动良性蠕虫和混合良性蠕虫的建模与分析[J].计算机研究与 发展,2007,44(6)：958-954.
[110]Nazario J, Anderson J, Wash R, et al. The Future of Internet Worms[EB/OL]. http://wenku.baidu.com/view/7c06f0a3284ac850ad0242cl.html,2001-06-03.
[111]Bailey M, Cooke E, Jahanian F, et al. The Internet Motion Sensor: A Distributed Blackhole Monitoring System[A]. Proceedings of the 12th ISOC Symposium on Network and Distributed Systems Security[C]. San Diego, USA:IEEE Computer Society Press,2005:167-179.
[112]Wu H, Fujimoto R, Guensler R, et al. Analytical Models for Information Propagation in Vehicle-to-vehicle Networks [A]. Proceedings of the IEEE Vehicular Technology Conference 2004[C]. Los Angles, USA:IEEE Computer Society Press,2004: 4548-4552.
[113]Nzoonta J, C. Borcea. STEID:A Protocl for Emergency Information Dissemination In Vehicular Networks[R]. Newark, USA:New Jersey Institute of Technology,2006.
[114]Little T D, Agarwal A. An information propagation scheme for VANETs[A]. Proceedings of the 2005 IEEE Intelligent Transportation Systems Conference[C] Vienna, Austria:IEEE Computer Society Press,2005:155-160.
[115]Eugster P, Guerraoui R, Kermarrec A. From Epidemics to Distributed Computing[J]. IEEE Computer,2004,59(4):60-67.