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云环境下软件定义入侵检测系统设计 被引量:5

Design of Software Defined Intrusion Detection System in Cloud
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摘要 云计算技术在近十年的发展中得到了学术界与产业界的广泛关注,其安全问题制约着云计算技术的发展,针对云中所面临的安全问题,往往采用多种安全手段结合的解决方案来保障其安全。在这些安全手段中,入侵检测是云安全解决方案中不可缺少的重要环节。文章针对使用软件定义网络(software defined network,SDN)技术的云平台,分析总结了入侵检测系统在云上部署时所面临的问题和对应的解决方案,提出了入侵检测系统的设计目标。同时基于SDN思想,设计了一个软件定义的入侵检测系统,该系统具有鲁棒性,可以降低云中的资源消耗,还能在虚拟机迁移后,使其依然处于系统的保护之下。 The technology of cloud computing has received the attention of academia and industry in the development of the last ten years, but the security problem restricts its development. Towards the security issues faced by the cloud, the cloud often use a variety of security means the combination of solutions to ensure its security. In these security measures, intrusion detection system (IDS) is an important and indispensable link in cloud security solutions. In this paper, towards to the cloud platform which used software deifned network (SDN), and the intrusion detection system deployed on it. We analysis the issues the IDS faced and conclude the correspond solutions, put forward the design goal of the IDS, and designed a software deifned IDS based on SDN. The system has robustness, it can save the cloud resource consumption, and after the virtual machine migration, it's still under the protection of the IDS, ifnally realize the important modules of the system.
出处 《信息网络安全》 2015年第9期191-195,共5页 Netinfo Security
关键词 云计算 入侵检测系统 软件定义网络 cloud computing intrusion detection system software deifned network
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  • 11,Bishop M. A model of security monitoring. In: Proceedings of the 5th Annual Computer Security Applications Conference. 1989. 46~52. http://seclab.cs. ucdavis.edu/papers.html
  • 22,Staniford-Chen S, Cheung S, Crawford R et al. GrIDS: a graph based intru sion detection system for large networks. In: Proceedings of the 19th National Information Systems Security Conference, Vol 1. National Institute of Standards a nd Technology, 1996. 361~370
  • 33,Hochberg J, Jackson K, Stallings C et al. NADIR: an automated system for detecting network intrusion and misuse. Computers and Security, 1993,12(3):235~2 48
  • 44,White G B, Fisch E A, Pooch U W. Cooperating security managers: a peer-based intrusion detection system. IEEE Network, 1996,10(1):20~23
  • 55,Forrest S, Hofmeyr S A, Somayaji A. Computer immunology. Communications of th e ACM, 1997,40(10):88~96
  • 66,Hunteman W. Automated information system alarm system. In: Proceedings of the 20th National Information Systems Security Conference. National Institute of Standards and Technology, 1997
  • 77,Porras P A, Neumann P G. EMERALD: event monitoring enabling responses to anom alous live disturbances. In: Proceedings of the 20th National Information System s Security Conference. National Institute of Standards and Technology, 1997
  • 8IBM虚拟化与云计算小组.虚拟化与云计算[M].北京:电子工业出版社,2010.
  • 9廖小飞,金海,刘海坤,等.桌面虚拟化[J].中国计算机学会通讯,2011,7(9):26-35.
  • 10Mckeown N, Anderson T, Balakrishnan H, Parulkar G, Peterson L, Rexford J, Shenker S, Turner J. OpenFlow: Enabling innovation in campus networks. ACM SIGCOMM Computer Communication Review, 2008,38(2):69-74. [doi: 10.1145/1355734. 1355746].

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  • 1SUFATRIO, YAP R H C,Improving Host-based IDS with Argument Abstraction to Prevent Mimicry Attacks[J]. Recent Advances in Intrusion Detection,2006(3858):146-164.
  • 2CREECH G, HU J K.A Semantic Approach to Host-Based IntrusionDetection Systems Using Contiguous and Discontiguous System Call Patterns[J].IEEE Transactions on Computers, 2014,4(63): 807-819.
  • 3JOO D, HONG T, HAN I.The Neural Network Models for IDS Based on the Asymmetric Costs of False Negative Errors and False Positive Errors[J]. Expert Systems with Applications, 2003,1(25): 69-75.
  • 4ANDREOLINI M, COLAJANNI M,MARCHETTI M.A Collaborative Framework for Intrusion Detection in Mobile Networks[J]. Information Sciences, 2015(321): 179-192.
  • 5CORONA,G1ACINTO G,ROLI F.Adversarial Attacks Against Intrusion Detection Systems: Taxonomy, Solutions and Open Issues[J]. Information Sciences, 2013(239):201-225.
  • 6GANAPATHY S, KULOTHUNGAN K,MUTHU1KAJKUMAR S,et al. Intelligent Feature Selection and Classification Techniques for Intrusion Detection in Networks: a Survey[J].Eurasip Journal on Wireless Communications and Networking, 2013(1):1-16.
  • 7BROUMAND, ESFAHANI M S,YOON B J, et al. Discrete Optimal Bayesian Classification with Error-conditioned Sequential Sampling[J], Pattern Recognition, 2015,11 (48):3766-3782.
  • 8KRISTJANPOLLER W, MINUTOLO M C. Gold Price Volatility: A Forecasting Approach Using the Artificial Neural Network-GARCH Model [.]].Expert Systems with Applications,2015, 42(20):7245-7251.
  • 9CICHOCKI A,AMAP,.I S I. Families of Alpha- Beta- and Gamma- Divergences: Flexible and Robust Measures of Similarities[J]. Entropy, 2010(12):1532-1568.
  • 10KOMPASS R. A Generalized Divergence Measure for Nonnegative Matrix Factorization [J]. Neural Computation 2007,3(19):780-791.

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