Research on abnormal data detection method of web browser in cloud computing environment

被引：2
|
作者：
Duan, Xindong [1 ,2 ]
机构：
[1] Nanyang Inst Technol, Dept Software, Nanyang 473000, Peoples R China
[2] Zhoukou Normal Univ, Sch Comp Sci & Technol, Zhoukou 466001, Peoples R China
来源：
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS | 2019年 / 22卷 / Suppl 1期
关键词：
Network channel; Interference frequency; Spatio-temporal correlation; Cloud computing environment; ALGORITHM;
D O I：
10.1007/s10586-017-1221-9
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The traditional abnormal data detection method uses the simplified gradient method to detect the abnormal data of web browser, which can not accurately remove the web abnormal data with the interference frequency components, and has low detection performance. Therefore, this paper proposed a distributed web browser abnormal data detection method based on the improved genetic algorithm and spatio-temporal correlation. Based on analyzing the principle of the abnormal data detection in the web browser, we select the abnormal data points of web browser through the deviation function and centralized algorithm, and determine the anomaly factor of web browser using the spatio-temporal distribution, and introduce the improved genetic algorithm to realize the detection of abnormal data of web browser. Simulation results show that the proposed method can reduce the energy consumption of the web data, and the signal amplitude is larger than the amplitude of the interference noise data, which has good anti-jamming performance.
引用
收藏
页码：1229 / 1238
页数：10
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