期刊文献+

引入病毒传播方向聚类的攻击容忍扫频方法

Method of Attack Tolerance Frequency Sweep Introduced Virus Propagation Direction Clustering
在线阅读 下载PDF
导出
摘要 攻击容忍系统通过对病毒入侵路径和频率进行扫频实现对病毒的挖掘,传统的扫频方法采用相位和频率特征匹配方法进行,对网络病毒非规则方向入侵不能起到较好的掩盖作用,扫频效果不好。提出一种采用病毒传播方向聚类分析的攻击容忍系统扫频方法,构建病毒传播路径分析模型,建立病毒传播的方向性空间搜索属性序列,分析病毒传播方向聚类演化模型,实现对病毒传播和入侵传播方向聚类过程的建模,求解病毒传播方向的聚类属性的信息熵,实现攻击容忍扫频改进算法。实验结果表明,算法能有效实现对病毒传播和扩散的时间和频率等信息进行扫频分析,病毒时频特征得到有效挖掘,通过扫频检测准确率较传统该方法提高13.6%。 Attack tolerant system based on virus intrusion path and frequency sweep is used for mining the virus, sweep fre.quency by using the traditional method of phase and frequency feature matching method, the network virus irregular direc.tion of intrusion cannot play a good masking effects, sweep the effect is not good. The analysis using a virus propagation di.rection clustering attack tolerant system frequency sweep method, constructing the analysis model of virus propagationpath, a search attribute sequence directional space to establish the transmission of the virus, virus propagation direction ofcluster evolution model is constructed, modeling of the spread of the virus and intrusion propagation direction of the cluster.ing process is obtained, information entropy clustering attribute for virus propagation direction the attack tolerance, sweepalgorithm is improved. The experimental results show that the algorithm can effectively realize the time and frequency onthe spread of the virus and the diffusion of information such as frequency analysis, virus time-frequency features can be ef.fectively mined, by sweeping detection the accuracy is improved 13.6%.than traditional the method.
作者 殷玉明
出处 《科技通报》 北大核心 2014年第12期175-177,共3页 Bulletin of Science and Technology
关键词 病毒 网络 攻击 扫频 virus network attack frequency sweep
  • 相关文献

参考文献6

二级参考文献50

  • 1殷丽华,方滨兴.入侵容忍系统安全属性分析[J].计算机学报,2006,29(8):1505-1512. 被引量:27
  • 2李革新.网络数据包捕获工具的开发与实现[J].计算机工程与设计,2007,28(8):1834-1836. 被引量:11
  • 3滕少华,王琳.径向基神经网络在入侵检测中的应用[J].江西师范大学学报(自然科学版),2007,31(3):297-301. 被引量:5
  • 4Wang J, Wang Z, Da I K. A network intrusion detection system based on the artificial neural networks[A]. Proceedings of the 3rd international conference on Information security [C]. 2004.
  • 5Haralambos Sarimveis, Alex Alexandridis, Stefanos Mazarakis, George Bafas. A new algorithm for developing dynamic radial basis function neural network mode based on genetic algorithms[J]. Computers and Chemical Engineering (S0098 - 1354), 2004, 28 (1-2): 209-217.
  • 6Hofmann A, Schmitz C, Sick B. Rule extraction from neural networks for intrusion detection in computer networks Systems [A]. IEEE International Conference onMan and Cybernetics [C]. 2003. 1259 - 1265.
  • 7Chatterjee A, Siarry P. Nonlinear Inertia Weight Variation for Dynamic Adaptation in Particle Swarm Optimization [ J ]. Computers&OperationsResearch, 2006, 33 (3): 859-871.
  • 8Qin Zheng, Yu Fan, Shi Zhewen, et al. Adaptive Inertia Weight Particle Swarm Optimization [C] //Proc. of ICAISC' 06. Kun ruing, China:[s. n.], 2006.
  • 9Carmillet V, Jourdain G. Wideband sonar detection in reverberation using autoregressive models. MTS/IEEE Conference Proceedings on OCEANS 96 Prospects for the 21st Century, 1996; 3:23-26.
  • 10Carmillet V, Amblard P O, Jourdain G. Detection of phase-or frequency-modulated signals in reverberation noise. J Acoust Soc Amer, 1999; 105(6) : 3 375-3 389.

共引文献156

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部