基于鼠标行为时频联合分析的用户可信认证

doi:10.13190/j.jbupt.2020-244

北京邮电大学学报 ›› 2021, Vol. 44 ›› Issue (4): 121-128.doi: 10.13190/j.jbupt.2020-244

基于鼠标行为时频联合分析的用户可信认证

易茜¹, 黎伟², 易树平², 谢俊东²

1. 重庆大学机械传动国家重点实验室, 重庆 400044;
2. 重庆大学机械与运载工程学院, 重庆 400044

收稿日期:2020-11-20 发布日期:2021-07-13
作者简介:易茜(1986-),女,讲师,E-mail:yiqian@cqu.edu.cn.
基金资助:
国家自然科学基金面上项目（71671020）；重庆市技术创新与应用发展专项重点项目（cstc2019jscx-mbdxX0049）

Trustworthy Identity Authentication Based on Joint Time-Frequency Analysis of Mouse Behavior

YI Qian¹, LI Wei², YI Shu-ping², XIE Jun-dong²

1. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China;
2. College of Mechanical Engineering, Chongqing University, Chongqing 400044, China

Received:2020-11-20 Published:2021-07-13

摘要/Abstract

摘要： 提出了一种基于时频联合分析的鼠标动力学认证方法.对用户操作鼠标行为的连续时序信号进行小波包变换，依据不同频带的子信号提取其时频联合分布特征，并使用递归特征消除法筛选出特征.采用随机森林算法建立用户独特的鼠标行为模式，据此进行用户身份认证.为验证方法的有效性，采取单因素实验设计，以特征分析方法为唯一因素，采集真实网络环境中40个用户在31个月内的鼠标行为数据，对其中18个用户分别采用时序分析和时频联合分析提取并筛选特征；使用相同算法建立用户鼠标行为模式，对比了基于2种特征分析方法的可信身份认证系统的性能差异.结果显示，相比时序分析方法，所提方法将可信身份认证的操作特性曲线下的面积从97.02%提升为99.10%.

关键词: 时频联合分析, 可信身份认证, 鼠标行为, 小波包变换, 随机森林

Abstract: A joint time-frequency analysis (JTFA) based method is proposed to improve the performance of the mouse dynamic trustworthy identity authentication system. Multi-resolution analysis of temporal signals based on wavelet packet transformation (WPT) is used to explore the joint time-frequency distribution characteristics of mouse behavior signals, and the recursive feature elimination method is applied to filter features extracted from users' long-term mouse behavior data. The random forest algorithm isemployed to establish the unique mouse behavior patterns for web users. which can be used to implement trustworthy identity authentication. In the case study, a single factor experiment is conducted, which sets the feature analysis method as the only factor. The mouse behavior data for thirty-one months of forty users is collected, among which the data of eighteen users are usedfor modeling and analysis. Both time series analysis and JTFA are adopted for feature extraction and the same algorithm is used to establish the model. The performance results show that the proposed JTFA based method increases the average value of area under receiver operating characteristic curve from 97.02% to 99.10% compared with the time series analysis based method.

Key words: joint time-frequency analysis, trustworthy identity authentication, mouse behavior, wavelet packet transformation, random forest

中图分类号:

TP273

易茜, 黎伟, 易树平, 谢俊东. 基于鼠标行为时频联合分析的用户可信认证[J]. 北京邮电大学学报, 2021, 44(4): 121-128.

YI Qian, LI Wei, YI Shu-ping, XIE Jun-dong. Trustworthy Identity Authentication Based on Joint Time-Frequency Analysis of Mouse Behavior[J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(4): 121-128.

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基于鼠标行为时频联合分析的用户可信认证

Trustworthy Identity Authentication Based on Joint Time-Frequency Analysis of Mouse Behavior

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