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

Zhen-yi XU; Yu KANG; Yang CAO; Yu-xiao YANG

doi:10.1631/FITEE.1700442

Your Location：

Home >

Browse articles >

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

Regular Papers | Updated：2022-05-19

- Man-machine verification of mouse trajectory based on the random forest model
- 基于随机森林模型的滑动轨迹人机识别
- Frontiers of Information Technology & Electronic Engineering Vol. 20, Issue 7, Pages: 925-929(2019)
- Affiliations：
  
  Department of Automation, School of Information Science and Technology, University of Science and Technology of China, Hefei 230022, China
  State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230027, China
- Author bio：
  
  [ "Zhen-yi XU, E-mail: kangduyu@ustc.edu.cn" ]
  Yu KANG, E-mail: kangduyu@ustc.edu.cn
  [ "Yang CAO, E-mail: forrest@ustc.edu.cn" ]
  [ "Yu-xiao YANG, E-mail:yyx531@mail.ustc.edu.cn" ]
- Funds：
- DOI：10.1631/FITEE.1700442
  CLC： TP181
- Received：04 July 2017，
  
  Revised：2019-;7-;3，
  
  Published：2019-07
- Accepted：
Scan QR Code
Zhen-yi XU, Yu KANG, Yang CAO, et al. Man-machine verification of mouse trajectory based on the random forest model[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(7): 925-929.
DOI：

Zhen-yi XU, Yu KANG, Yang CAO, et al. Man-machine verification of mouse trajectory based on the random forest model[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(7): 925-929. DOI： 10.1631/FITEE.1700442.

摘要

识别码在维护网络安全的人机身份验证中得到广泛应用。人机身份验证面临的挑战包括对人与机器滑动轨迹的正确检测。提出一种基于滑动轨迹数据集的人机识别随机森林模型。通过多维性能评价指标，包括识别准确率、识别召回率、识别误报率、识别漏报率、

值和加权准确率，验证该随机森林模型以及基准模型（逻辑回归模型和支持向量机）。随机森林模型多维性能评价指标优于基准模型。

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.

关键词

Keywords

references

M Brown , , , SJ Rogers . . User identification via keystroke characteristics of typed names using neural networks . . Int J Man-Mach Stud , , 1993 . . 39 ( ( 6 ): ): 999 - - 1014 . . DOI: 10.1006/imms.1993.1092 http://doi.org/10.1006/imms.1993.1092 . .

R Caruana , , , A Niculescu-Mizil . . An empirical comparison of supervised learning algorithms . . 23 rd Int Conf on Machine Learning , , 2006 . . p.161 - - 168 . . DOI: 10.1145/1143844.1143865 http://doi.org/10.1145/1143844.1143865 . .

C Chen , , , A Liaw , , , L Breiman . . Using Random Forest to Learn Imbalanced Data . . Technical Report No. 666, University of California, Berkeley , , 2004 . . .

G De'ath , , , KE Fabricius . . Classification and regression trees: a powerful yet simple technique for ecological data analysis . . Ecology , , 2000 . . 81 ( ( 11 ): ): 3178 - - 3192 . . DOI: 10.1890/0012-9658(2000)081[3178:CARTAP]2.0.CO;2 http://doi.org/10.1890/0012-9658(2000)081[3178:CARTAP]2.0.CO;2 . .

LA Gordon , , , MP Loeb , , , W Lucyshyn , , , 等 . . 2006 CSI/FBI Computer Crime and Security Survey . . Computer Security Institute, USA , , 2006 . . .

C Hultquist , , , G Chen , , , KG Zhao . . A comparison of Gaussian process regression, random forests and support vector regression for burn severity assessment in diseased forests . . Remote Sens Lett , , 2014 . . 5 ( ( 8 ): ): 723 - - 732 . . DOI: 10.1080/2150704X.2014.963733 http://doi.org/10.1080/2150704X.2014.963733 . .

L Liu , , , AL Yang , , , WJ Zhou , , , 等 . . Robust dataset classification approach based on neighbor searching and kernel fuzzy c-means . . IEEE/CAA J Autom Sin , , 2015 . . 2 ( ( 3 ): ): 235 - - 247 . . DOI: 10.1109/JAS.2015.7152657 http://doi.org/10.1109/JAS.2015.7152657 . .

B Sanjaa , , , E Chuluun . . Malware detection using linear SVM . . 8 th Int Forum on Strategic Technology , , 2013 . . p.136 - - 138 . . DOI: 10.1109/ifost.2013.6616872 http://doi.org/10.1109/ifost.2013.6616872 . .

T Su . . Application of CAPTCHA with Behavioral Vilification Based on GBDT . . MS Thesis, Central China Normal University, Wuhan, China , , 2016 . . .

SB Taieb , , , RJ Hyndman . . A gradient boosting approach to the Kaggle load forecasting competition . . Int J Forecast , , 2014 . . 30 ( ( 2 ): ): 382 - - 394 . . DOI: 10.1016/j.ijforecast.2013.07.005 http://doi.org/10.1016/j.ijforecast.2013.07.005 . .

A Weiss , , , A Ramapanicker , , , P Shah , , , 等 . . Mouse movements biometric identification: a feasibility study . . Proc Student/Faculty Research Day , , 2007 . . .

Views

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

Dynamic time prediction for electric vehicle charging based on charging pattern recognition

A new feature selection method for handling redundant information in text classification

SVM based layout retargeting for fast and regularized inverse lithography

Related Author

Chunxi LI

Yingying FU

Xiangke CUI

Quanbo GE

You-wei WANG

Li-zhou FENG

Kai-sheng LUO

Zheng SHI

Related Institution

Logistics Engineering College, Shanghai Maritime University

School of Economics and Management, Beijing Jiaotong University

School of Automation, Nanjing University of Information Science and Technology

Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology

Jiangsu Key Laboratory of Big Data Analysis Technology (B-DAT), Nanjing University of Information Science and Technology

Chat

Address：Zhejiang University Press, 148 Tianmushan Road, Hangzhou, China Postal code：310028
Tel：+86-571-88273162 Email：fitee@zju.edu.cn
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰