Human-Bot Detection with One-Class Classifiers

被引：0
|
作者：
Niu H. [1 ,2 ]
Zhu R. [1 ,2 ]
Li Y. [1 ,2 ]
Ding J. [1 ,2 ]
Cai Z. [1 ,2 ]
机构：
[1] Ministry of Education Key Laboratory for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an
[2] Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an
来源：
Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University | 2019年 / 53卷 / 11期
关键词：
Human-bot detection; Mouse behavior; One-class classifier; Scripted bot;
D O I：
10.7652/xjtuxb201911017
中图分类号：
学科分类号：
摘要：
A behavior-based human-bot detection technology is proposed to address the problem of a variety of scripted bots in the field of human-bot detection, where one-class classifier is considered to establish a human mouse behavior model to detect sophisticated scripted bots with behavioral simulation functions. JavaScript is installed on the web page to collect the normal users' mouse behaviors, the feature vectors describing the users' behaviors are obtained and sent to the one-class classifiers to construct the human-bot detection model after the data preprocessing and feature extraction process. Statistically analyzing the mouse behaviors of normal users on the web, three types of scripted bots with behavior simulation function are designed to simulate the normal user to complete the task of logging into the web. According to the feature subset selected by the linear scripted bots participated in the feature selection process, the performances of one-class classifiers in human-bot detection are evaluated. Experimental results show that the best equal error rates of one-class classifiers for linear, regular, and irregular curves scripted bots reach 7.25%, 9.45%, and 12.7%, respectively. Compared with two-class method whose best equal error rate is 20.56%, the one-class method is endowed with excellent generalization property for unknown scripted bots detection. © 2019, Editorial Office of Journal of Xi'an Jiaotong University. All right reserved.
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页码：118 / 124
页数：6
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