Human-machine Behavior Recognition for CAPTCHA Based on Gradient Boosting Model

doi:10.3969/j.issn.1671-1122.2017.09.033

Abstract

Abstract:

By using the abnormal means to simulate human behavior operation and bypass the CAPTCHA system, hacking tools can then sent a large batch of requests to the background system to achieved the hacking goals, which may bring to big risk of delay response of system operation, or even produce huge economic losses. However, the traditional verification code method has shortcomings in both ease of use and man-machine recognition rate. In this paper, a new behavior trajectory of the CAPTCHA system based feature engineering, with utilizes the gradient boosting models, for human-machine behavior recognition is proposed. Performance in 100000 samples of real CAPTCHA system can obtain a more than 90% recognition accuracy.

Key words: gradient boosting, CAPTCHA, machine learning, human-machine recognition

CLC Number:

TP399

Zhiyou OUYANG, Xiaokui SUN. Human-machine Behavior Recognition for CAPTCHA Based on Gradient Boosting Model[J]. Netinfo Security, 2017, 17(9): 143-146.

Figures/Tables 8

References 12

[1]	文晓阳, 高能, 夏鲁宁,等. 高效的验证码识别技术与验证码分类思想[J]. 计算机工程, 2009, 35(8):186-188.
[2]	文伟平,郭荣华,孟正,等. 信息安全风险评估关键技术研究与实现[J]. 信息网络安全,2015(2):7-14.
[3]	文晓阳, 高能, 荆继武. 论坛验证码技术的安全性分析[C]// 中国计算机学会计算机安全专业委员会,中国电子学会计算机工程与应用分会计算机安全保密学组. 第22次全国计算机安全学术交流会, 9月16-19,2007, 河北省张家界.北京: 《信息网络安全》杂志社, 2007:45-50.
[4]	张平,陈长松,胡红钢. 基于分组密码的认证加密工作模式[J]. 信息网络安全,2014(11):8-17.
[5]	HERNANDEZ C C J, BARRERO D F, RMORENO M D. Machine Learning and Empathy: the Civil Rights CAPTCHA[J]. Concurrency & Computation Practice & Experience, 2016, 28(4):1310-1323.
[6]	KHEMCHANDANI R, SHARMA S.Robust Parametric Twin Support Vector Machine and Its Application in Human Activity Recognition[C] // Department of Computer Science and Engineering. Indian Institute of Technology. Proceedings of International Conference on Computer Vision and Image Processing, February 26-28, 2016, Roorkee, Uttarakhand, India. Singapore: Springer, 2017: 193-203.
[7]	苏涛. 基于梯度提升树的行为式验证码的人机识别的研究[D].武汉:华中师范大学, 2016.
[8]	KWAK N J, SONG T S.Android-Based Human Action Recognition Alarm Service Using Action Recognition Parameter and Decision Tree[J]. International Journal of Security & Its Applications, 2013, 7(4):277-286.
[9]	GAN L, CHEN F.Human Action Recognition Using APJ3D and Random Forests[J]. Journal of Software, 2013, 8(9):412-423.
[10]	MAZAAR H, EMARY E, ONSI H.Ensemble Based-Feature Selection on Human Activity Recognition[C] // Cairo University. Proceedings of the 10th International Conference on Informatics and Systems, May 9-11, 2016, Cairo, Egypt. America: ACM, 2016: 81-87.
[11]	FAROOQ F, TANDON S, PARASHAR P, et al.Vectorized code implementation of Logistic Regression and Artificial Neural Networks to recognize handwritten digit[C] //Delhi Technological University. Power Electronics. Intelligent Control and Energy Systems (ICPEICES), July 04-06, 2016, Delhi, India. New York: IEEE, 2016: 1-5.
[12]	FLACH P A, LACHICHE N.Naive Bayesian Classification of Structured Data[J]. Machine Learning, 2004, 57(3):233-269.

[1]	Chun GUO, Changqing CHEN, Guowei SHEN, Chaohui JIANG. A Ransomware Classification Method Based on Visualization [J]. Netinfo Security, 2020, 20(4): 31-39.
[2]	Yifeng DU, Yuanbo GUO. A Dynamic Access Control Method for Fog Computing Based on Trust Value [J]. Netinfo Security, 2020, 20(4): 65-72.
[3]	Zewen MA, Yang LIU, Hongping XU, Hang YI. DoS Traffic Identification Technology Based on Integrated Learning [J]. Netinfo Security, 2019, 19(9): 115-119.
[4]	Guanheng CHEN, Jinshu SU. Abnormal Traffic Detection Algorithm Based on Deep Neural Network [J]. Netinfo Security, 2019, 19(6): 68-75.
[5]	Chunqi TIAN, Jing LI, Wei WANG, Liqing ZHANG. A Method for Improving the Performance of Spark on Container Cluster Based on Machine Learning [J]. Netinfo Security, 2019, 19(4): 11-19.
[6]	Jianwei HU, Wei ZHAO, Zheng YAN, Rui ZHANG. Analysis and Implementation of SQL Injection Vulnerability Mining Technology Based on Machine Learning [J]. Netinfo Security, 2019, 19(11): 36-42.
[7]	Jian ZHANG, Bohan CHEN, Liangyi GONG, Zhaojun GU. Research on Malware Detection Technology Based on Image Analysis [J]. Netinfo Security, 2019, 19(10): 24-31.
[8]	Weiping WEN, Jingwei LI, Yingnan JIAO, Hailin LI. A Vulnerability Detection Method Based on Random Detection Algorithm and Information Aggregation [J]. Netinfo Security, 2019, 19(1): 1-7.
[9]	Yingchao YU, Lin DING, Zuoning CHEN. Research on Attacks and Defenses towards Machine Learning Systems [J]. Netinfo Security, 2018, 18(9): 10-18.
[10]	Yang ZHANG, Yuangang YAO. Research on Network Intrusion Detection Based on Xgboost [J]. Netinfo Security, 2018, 18(9): 102-105.
[11]	Weiping WEN, Bozhi WU, Yingnan JIAO, Yongqiang HE. Design and Implementation on Malicious Documents Detection Tool Based on Machine Learning [J]. Netinfo Security, 2018, 18(8): 1-7.
[12]	Xiang HE, Sheng LIU, Jiguo JIANG. Comparative Study of Intrusion Detection Methods Based on Machine Learning [J]. Netinfo Security, 2018, 18(5): 1-11.
[13]	Hongsong CHEN, Gang WANG, Jianlin SONG. Research on Anomaly Behavior Classification Algorithm of Internal Network User Based on Cloud Computing Intrusion Detection Data Set [J]. Netinfo Security, 2018, 18(3): 1-7.
[14]	Guihua DUAN, Zhuoxiang SHEN, Dongjie SHEN, Zhi LI. Research on a Download Link Recognition Scheme Based on Feature Extraction [J]. Netinfo Security, 2018, 18(10): 31-36.
[15]	SUN Jingchao. A Classification Method of Web Page Using Machine Learning [J]. 信息网络安全, 2017, 17(9): 45-48.