Full Text:
<2431>
Summary: 
<1710>
CLC number: TP181
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2019-07-03
Cited: 0
Clicked: 6405
Man-machine verification of mouse trajectory based on the random forest model
| ||||||||||||||
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.
@article{title="Man-machine verification of mouse trajectory based on the random forest model",
author="Zhen-yi Xu, Yu Kang, Yang Cao, Yu-xiao Yang",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="20",
number="7",
pages="925-929",
year="2019",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1700442"
}
%0 Journal Article
%T Man-machine verification of mouse trajectory based on the random forest model
%A Zhen-yi Xu
%A Yu Kang
%A Yang Cao
%A Yu-xiao Yang
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 7
%P 925-929
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700442
TY - JOUR
T1 - Man-machine verification of mouse trajectory based on the random forest model
A1 - Zhen-yi Xu
A1 - Yu Kang
A1 - Yang Cao
A1 - Yu-xiao Yang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 7
SP - 925
EP - 929
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1700442
Abstract: 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.
[1]Brown M, Rogers SJ, 1993. User identification via keystroke characteristics of typed names using neural networks. Int J Man-Mach Stud, 39(6):999-1014.
[2]Caruana R, Niculescu-Mizil A, 2006. An empirical comparison of supervised learning algorithms. 23rd Int Conf on Machine Learning, p.161-168.
[3]Chen C, Liaw A, Breiman L, 2004. Using Random Forest to Learn Imbalanced Data. Technical Report No. 666, University of California, Berkeley.
[4]De’ath G, Fabricius KE, 2000. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology, 81(11):3178-3192.
[5]Gordon LA, Loeb MP, Lucyshyn W, et al., 2006. 2006 CSI/FBI Computer Crime and Security Survey. Computer Security Institute, USA.
[6]Hultquist C, Chen G, Zhao KG, 2014. A comparison of Gaussian process regression, random forests and support vector regression for burn severity assessment in diseased forests. Remote Sens Lett, 5(8):723-732.
[7]Liu L, Yang AL, Zhou WJ, et al., 2015. Robust dataset classification approach based on neighbor searching and kernel fuzzy c-means. IEEE/CAA J Autom Sin, 2(3):235-247.
[8]Sanjaa B, Chuluun E, 2013. Malware detection using linear SVM. 8th Int Forum on Strategic Technology, p.136-138.
[9]Su T, 2016. Application of CAPTCHA with Behavioral Vilification Based on GBDT. MS Thesis, Central China Normal University, Wuhan, China (in Chinese).
[10]Taieb SB, Hyndman RJ, 2014. A gradient boosting approach to the Kaggle load forecasting competition. Int J Forecast, 30(2):382-394.
[11]Weiss A, Ramapanicker A, Shah P, et al., 2007. Mouse movements biometric identification: a feasibility study. Proc Student/Faculty Research Day.
ORCID: 
Open peer comments: Debate/Discuss/Question/Opinion
<1>