基于随机森林模型的滑动轨迹人机识别

许镇义; 康宇; 曹洋; 杨钰潇

doi:10.1631/FITEE.1700442

Your Location：

Home >

Browse articles >

基于随机森林模型的滑动轨迹人机识别

常规文章 | Updated：2022-05-19

- 基于随机森林模型的滑动轨迹人机识别
  Enhanced Publication
- Man-machine verification of mouse trajectory based on the random forest model
- 信息与电子工程前沿（英文） 2019年20卷第7期页码：925-929
- 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：
  
  Project supported by the National Natural Science Foundation of China (Nos. 61673361 and 61422307)
- DOI：10.1631/FITEE.1700442
  中图分类号： TP181
- 纸质出版日期：2019-07，
  
  收稿日期：2017-07-04，
  
  修回日期：2019-07-03，
- Accepted：
Scan QR Code
许镇义, 康宇, 曹洋, 等. 基于随机森林模型的滑动轨迹人机识别[J]. 信息与电子工程前沿（英文）, 2019,20(7):925-929.

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.
许镇义, 康宇, 曹洋, 等. 基于随机森林模型的滑动轨迹人机识别[J]. 信息与电子工程前沿（英文）, 2019,20(7):925-929. DOI： 10.1631/FITEE.1700442.

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

人机识别随机森林支持向量机逻辑回归多维性能评价指标

Keywords

Man-machine verificationRandom forestSupport vector machineLogistic regressionPerformance metrics

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.1993.1092http://doi.org/10.1006/imms.1993.1092..

R Caruana, , , A Niculescu-Mizil. . An empirical comparison of supervised learning algorithms. . 23rd Int Conf on Machine Learning, , 2006. . p.161--168. . DOI:10.1145/1143844.1143865http://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;2http://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.2014.963733http://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.2015.7152657http://doi.org/10.1109/JAS.2015.7152657..

B Sanjaa, , , E Chuluun. . Malware detection using linear SVM. . 8th Int Forum on Strategic Technology, , 2013. . p.136--138. . DOI:10.1109/ifost.2013.6616872http://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.2013.07.005http://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. ..

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

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