User Behavior Analysis Based on Decomposition of Time-Stamp Sequence

被引：0
作者：
Chang Huijun [1 ]
Shan Hong [1 ]
Yuan Yuming [2 ]
机构：
[1] PLA, Inst Elect Engn, Hefei, Peoples R China
[2] Nanchang Mil Acad, Small Arms Fire Dept, Nanchang, Jiangxi, Peoples R China
来源：
2013 22ND WIRELESS AND OPTICAL COMMUNICATIONS CONFERENCE (WOCC 2013) | 2013年
关键词：
user behavior analysis; time-stamp sequence; low-pass filter; periodic sequence extraction;
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
A decomposition method of packet time-stamp sequence is proposed. Firstly, the method samples the information time sequence and utilizes a low-pass filter to find and extract the burst component based on the different attenuation characteristics of different types of sampled signals. Secondly, it uses a traversal and matching method to extract the periodic sub-sequence based on Euclid distance. Finally, it decomposes the encrypted packet sequence into the burst, periodic, and random components. The method need not parse the packet contents, where the periodic component could be used to analyze the user's routine behavior and the burst component could be used for burst abnormality detection. Simulation results show that the method has good decomposition performance.
引用
收藏
页码：469 / 474
页数：6
相关论文
共 12 条
[1]  
[Anonymous], 2004, Proceedings of the 2004 ACM SIGMOD international conference on Management of data
[2]  
Bin Song, 2010, 2010 INT C INT TECHN, P1
[3]  
Castiglione A., 2011, Proceedings of the 2011 Fifth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), P520, DOI 10.1109/IMIS.2011.65
[4]  
Cheneau T., 2011, Proceedings of the 2011 5th International Conference on Network and System Security (NSS 2011), P249, DOI 10.1109/ICNSS.2011.6060009
[5]   Users' Behavior Character Analysis and Classification Approaches in Enterprise Networks [J].
Qin, Tao ;
Guan, Xiaohong ;
Long, Yi ;
Li, Wei .
PROCEEDINGS OF THE 8TH IEEE/ACIS INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE, 2009, :323-328
[6]  
Radinsky Kira, 2012, P 21 INT C WORLD WID, P599, DOI [10.1145/2187836.2187918, DOI 10.1145/2187836.2187918]
[7]  
Shafer Ilari., 2012, P 18 ACM SIGKDD INT, P1158
[8]  
Spiegel S., 2011, The fifth international workshop on knowledge discovery from sensor data, P34, DOI DOI 10.1145/2003653.2003657
[9]  
Sridevi S., 2010, IITM 10, P163
[10]  
Wan Li., 2009, Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data, SensorKDD'09, P87
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