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一种蚁群分布进化的高效网络安全防护方法 被引量:1

Method of High Efficient Network Security Protection Based on Ant Distribution Evolutionary
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摘要 网络系统的安全访问是网络技术发展中必须解决的首要问题。传统的网络系统安全访问控制方法采用基于统计学的数据分析方法,在对网络访问数据进行统计分析的基础上,分类识别网络系统的正常访问数据和异常数据,此方法网络防护效率低。提出一种蚁群分布进化的高效网络安全防护方法,首先将所有的访问数据分层,然后采用蚁群分布算法对每个单独的数据群进行处理,在蚁群分布处理的基础上,迭代实现系统的数据识别,达到高效网络防护的目的。最后采用实际访问数据进行测试实验,结果显示,采用蚁群分布进化算法,网络系统的安全性能提高,具有很好的应用价值。 The network security access was the most key problem that must be solved in the network technology develop-ment. In traditional network security access control method, the statistical data analysis method was used in network access data for statistical analysis, with the classification system, the normal network access data and the abnormal data was divid-ed, but which worked poor with much data access. A method of high efficient network security protection was proposed based on ant distribution evolutionary, first of all, the access data was treated with stratification, then ant distribution algo-rithm was used in the data group for each individual processing, with the basis of distributed processing, the data was identi-fied to achieve efficient network protection purposes. Finally, an actually access experiment was done for testing, and the re-sult shows that with ant distribution evolutionary algorithm, the network security performance is greatly improved, it has good application value.
作者 王崇科 刘丹
出处 《科技通报》 北大核心 2014年第8期104-106,共3页 Bulletin of Science and Technology
基金 河南省教育厅自然科学研究计划项目(2011B520010)
关键词 蚁群分布进化 网络安全 高效网络防护 ant distribution evolutionary network security high efficient network protection
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  • 1Qu BY,Liang JJ,Suganthan PN.Niching particle swarm opti.mization with local search for multi-modal optimization[J].Information Sciences,2012,197:131-143.
  • 2郭亚周,高德远,高翔.模糊聚类分析在入侵检测系统中的应用研究[J].沈阳理工大学学报,2005,24(4):26-28. 被引量:26
  • 3Liu Y.Mining frequent patterns from univariate uncertain data.Data and Knowledge Engineering.2012,71(1):47-68.
  • 4Deng CW,Lin WS.Performance analysis,parameter selec.tion and extensions to H.264/AVC FRExt for high resolu.tion video coding[J].Journal of visual communication&im.age representation,2011,22(8):749-759.
  • 5朱映映,吴锦锋,明仲.基于网络事件和深度协议分析的入侵检测研究[J].通信学报,2011,32(8):171-178. 被引量:14
  • 6Du QS,Jiang BH:.Design and Implementation of the Em.bedded Based Web Camera System[J].Journal of Software,2012,7(11):2560-2566.

二级参考文献20

  • 1徐明,陈纯,应晶.一个两层马尔可夫链异常入侵检测模型(英文)[J].软件学报,2005,16(2):276-285. 被引量:7
  • 2[1]Eskin E.Anomaly detection over noisy data using learned probability distributions,In Proceedings of the International Conference on Machine Learning[C],2000.
  • 3[2]Luo J.Intergration fuzzy logic with date mining methods for intrusion detection[D].Mississippi State University,1999.
  • 4[3]The third international knowledge discovery and data mining tools competition dataset kdd99-cup[DB/OL],http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html,1999.
  • 5WHITMA N, MMICHAE L, MATTORD H. Principles of Information Security[M]. Canada: Thomson, 2009. 290 -301.
  • 6JAN N Y, LIN S C, TSENT S S, et al. A decision support system for onstructing an alert classification model[J]. Journals of Expert Systems with Applications, 2009, 36(8): 11145-11155.
  • 7LINDQIVST U, PORRAS P A. Detecting Computer and Network Misuse Through the Production-Based Expert System Toolset (P-BEST)[R]. IEEE Symposium on Security and Privacy, Oakland, 1999. 146-161.
  • 8JOAO B D, RAVICHANDRAN B. Statistical traffic modeling for network intrusion detection[A]. Eighth IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems (MASCOTS'00)[C]. San Francisco, 2000.
  • 9WUU L C, HUNG C H, CHEN S F. Building intrusion pattern miner for Snort network intrusion detection system[J]. Journal of Systems and Software, 2007,80(10):1699-1715.
  • 10NEIMNBE J O. Automated technique for debugging network intrusion detection systems[A]. IEEE 2010 International Conference on Intelligent Systems, Modelling and Simulation (ISMS)[C]. Liverpool, 2010. 362-367.

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