期刊文献+

网络域名用户异常访问行为信息精准识别仿真 被引量:1

Network Domain Named User Abnormal Access Behavior Information Precision Identification Emulation
下载PDF
导出
摘要 对用户访问行为的研究往往依托于历史访问记录,而网络管理系统统计的历史访问记录中含有大量的异常访问行为信息,严重影响了对用户访问行为规律特性的研究。当前识别方法所设定的分类器置信度低、无法有效提取异常访问行为特征,致使所设定的识别阈值难以准确识别异常访问行为。对此,提出一种基于选择性协同学习的网络域名用户异常访问行为信息精准识别方法。上述方法采用时间窗函数与Bootstrap重采样构建网络域名用户访问行为状态信息簇,利用随机加权网络的有监督学习获得访问行为状态信息模型,对模型进行稀疏化处理,获得异常访问行为信息特征。利用混合扰动生成方法建立分类器对访问行为信息样本子集进行协同学习,在学习过程中利用选择性集成进行置信度计算与访问行为信息更新,在此基础上基于准确性选取构造异常访问行为识别阈值,用于实际用户异常访问行为识别。实验结果表明,所提方法有效提高了异常访问行为信息识别精度。 A method for precisely identifying information of abnormal access behavior of network domain user based on selective collaborative learning is proposed. This method used time window function and Bootstrap resam- pling to construct the status information cluster of access behavior of network domain user. Then, we used supervised learning of the random weighted network to obtain the information model of access behavior state, and sparsely pro- cessed the model to obtain the information characteristic of abnormal access behavior. Moreover, we used the method of mixed disturbance classifier to establish a classifier and carry out cooperative learning on the subset of access be- havior information samples. In the learning process, we used selective ensemble to calculate the confidence level and update the access behavior information. On this basic, we constructed the recognition threshold value for abnormal ac- cess behavior based on accuracy selection, which was used for identification of actual access behavior of users. Simu- lation results prove that the proposed method effectively improves the accuracy of information recognition in abnormal accessing behavior.
作者 穆荣 MU Rong(Xi' an University of Science and Technology,Xi' an Shanxi 710054,China)
机构地区 西安科技大学
出处 《计算机仿真》 北大核心 2018年第7期339-342,376,共5页 Computer Simulation
关键词 网络域名用户 异常访问 行为信息 精准识别 Network domain name user Abnormal access Behavior information Precise identification
  • 相关文献

参考文献10

二级参考文献88

  • 1李林,崔志明.用户Web日志序列模式挖掘研究[J].微机发展,2005,15(5):119-121. 被引量:4
  • 2田润涛,谢培山.色谱指纹图谱相似度评价方法的规范化研究(一)[J].中药新药与临床药理,2006,17(1):40-42. 被引量:47
  • 3邬书跃,田新广.基于隐马尔可夫模型的用户行为异常检测新方法[J].通信学报,2007,28(4):38-43. 被引量:20
  • 4WEINLAND D, RONFARD R, BOYER E. A survey of vision-based methods for action representation, segmentation and recognition [J]. Computer vision and image understanding, 2011, 115(2): 224-241.
  • 5NIEBLES J C, WANG H, LI F. Unsupervised learning of human action categories using spatial-temporal words [J]. International journal of computer vision, 2008, 79(3): 299-318.
  • 6LAPTEV I. On space-time interest points [J]. International journal of computer vision, 2005, 64(2/3): 107-123.
  • 7WANG H, KLASER A, SCHMID C, et al. Action recognition by dense trajectories [C]// CVPR 2011: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Washington, D.C.: IEEE Computer Society, 2011: 3169-3176.
  • 8OLSHAUSEN B A, FIELD D J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images [J]. Nature, 1996, 381(6583): 607-609.
  • 9OLSHAUSEN B A, FIELD D J. Sparse coding with an overcomplete basis set: a strategy employed by V1? [J]. Vision research, 1997, 37(23): 3311-3325.
  • 10LAPTEV I, MARSZALEK M, SCHMID C, et al. Learning realistic human actions from movies [C]// CVPR 2008: Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition. Washington, D.C.: IEEE Computer Society, 2008: 1-8.

共引文献83

同被引文献3

引证文献1

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