Full Text:   <456>

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CLC number: TP181

On-line Access: 2019-08-05

Received: 2017-07-04

Revision Accepted: 2017-12-25

Crosschecked: 2019-07-03

Cited: 0

Clicked: 890

Citations:  Bibtex RefMan EndNote GB/T7714


Zhen-yi Xu


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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.7 P.925-929


Man-machine verification of mouse trajectory based on the random forest model

Author(s):  Zhen-yi Xu, Yu Kang, Yang Cao, Yu-xiao Yang

Affiliation(s):  Department of Automation, School of Information Science and Technology, University of Science and Technology of China, Hefei 230022, China; more

Corresponding email(s):   xuzhenyi@mail.ustc.edu.cn, kangduyu@ustc.edu.cn, forrest@ustc.edu.cn, yyx531@mail.ustc.edu.cn

Key Words:  Man-machine verification, Random forest, Support vector machine, Logistic regression, Performance metrics

Zhen-yi Xu, Yu Kang, Yang Cao, Yu-xiao Yang. Man-machine verification of mouse trajectory based on the random forest model[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(7): 925-929.

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DOI - 10.1631/FITEE.1700442

Identifying code has been widely used in man-machine verification to maintain network security. The challenge in engaging man-machine verification involves the correct classification of man and machine tracks. In this study, we propose a random forest (RF) model for man-machine verification based on the mouse movement trajectory dataset. We also compare the RF model with the baseline models (logistic regression and support vector machine) based on performance metrics such as precision, recall, false positive rates, false negative rates, F-measure, and weighted accuracy. The performance metrics of the RF model exceed those of the baseline models.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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