User authentication of industrial internet based on HHT transform of mouse behavior

Yigong ZHANG; Qian YI; Jian LI; Congbo LI; Aijun YIN; Shuping YI

doi:10.11959/j.issn.2096-3750.2022.00268

您当前的位置：

首页 >

文章列表页 >

User authentication of industrial internet based on HHT transform of mouse behavior

Theory and Technology | 更新时间：2024-06-05

- User authentication of industrial internet based on HHT transform of mouse behavior
- Chinese Journal on Internet of Things Vol. 6, Issue 2, Pages: 77-87(2022)
- 作者机构：
  
  1. 重庆大学机械传动国家重点实验室，重庆 400044
  2. 重庆大学输配电装备及系统安全与新技术国家重点实验室，重庆 400044
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2022.00268
  CLC： TP273
- Published：30 June 2022，
  
  Published Online：2022-06，
- 稿件说明：
移动端阅览
YIGONG ZHANG, QIAN YI, JIAN LI, et al. User authentication of industrial internet based on HHT transform of mouse behavior. [J]. Chinese journal on internet of things, 2022, 6(2): 77-87.
DOI：

YIGONG ZHANG, QIAN YI, JIAN LI, et al. User authentication of industrial internet based on HHT transform of mouse behavior. [J]. Chinese journal on internet of things, 2022, 6(2): 77-87. DOI： 10.11959/j.issn.2096-3750.2022.00268.

摘要

工业互联网的快速发展引发了对网络安全的广泛关注，终端用户身份认证技术成为研究热点。根据工业互联网人机交互特点，设计了实验网站，收集了该网站 24 名用户两年半的非受控环境下鼠标行为数据作实例，采用希尔伯特黄变换（HHT

Hilbert-Huang transform）提取鼠标行为信号频域特征，结合时域特征，形成163维时频域联合特征矩阵，用于表征用户鼠标行为模式特征。使用 Bagged tree、支持向量机（SVM

support vector machine）、Boost tree和K最邻近（KNN

K-nearest neighbor）算法构建网络用户身份认证模型，对比数据测试结果表明，Bagged tree算法在本案例中内部检测效果最佳，平均错误接受率（FAR

false acceptance rate）为0.12%、平均错误拒绝率（FRR

false rejection rate）为0.28%；外部检测中，平均FAR为1.47%。相较于传统鼠标动力学方法，使用HHT提取鼠标行为频域信息能更好地实现终端用户身份认证，为保障工业互联网安全提供有效的技术支撑。

Abstract

The rapid development of the industrial internet had caused widespread concern about the network security

and the end-user authentication technology was considered a research hotspot.According to the characteristics of human-computer interaction in industrial internet

an experimental website was designed.24 users' mouse behavior data in an uncontrolled environment were collected within 2.5 years to conduct case studies.Hilbert-Huang transform (HHT) was used to extract frequency domain features of mouse behavior signals

combined with time domain features to form a time-frequency joint domain feature matrix of 163-dimensional to characterize user mouse behavior patterns.Bagged tree

support vector machine (SVM)

Boost tree and K-nearest neighbor (KNN) were used to build a user authentication model， and the comparison result showed that the Bagged tree had the best internal detection effect in this case

with an average false acceptance rate (FAR) of 0.12% and an average false rejection rate (FRR) of 0.28%.In external detection

the FAR was 1.47%.Compared with the traditional mouse dynamics method

the frequency domain information of mouse behavior extracted by HHT can better realize the user authentication

and provide technical support the security of the industrial internet.

关键词

工业互联网身份认证鼠标行为希尔伯特黄变换Baggedtree

Keywords

industrial internetidentity authenticationmouse behaviorHilbert-Huang transformBagged tree

references

中国国务院. 国务院关于深化“互联网+先进制造业”发展工业互联网的指导意见[EB]. 2017.

The Chinese State Council. Guiding opinions on deepening the "Internet plus advanced manufacturing industry" to develop industrial Internet[EB]. 2017.

人民政协报. 深入实施工业互联网创新发展战略为建设制造强国和现代化经济体系提供有力支撑:全国政协“加快推进工业互联网建设”双周协商座谈会发言摘登(上)[N]. 人民政协报, 2020-05-07(4).

People's Political Consultative Conference. In-depth implementation of the Industrial Internet innovation development strategy provides strong support for the construction of a strong manufacturing country and a modern economic system-The CPPCC National Committee "accelerate the promotion of industrial Internet construction" bi-weekly consultation forum to replace the excerpt[N]. CPPCC Daily, 2020.

工业互联网产业联盟. 工业互联网安全总体要求[EB]. 2018.

Alliance of Industrial Internet. General requirements for industrial Internet security[EB]. 2018.

互联网产业联盟. 中国工业互联网安全态势报告[EB]. 2019.

Alliance of Industry Internet. China industrial internet security situation report（2018）[EB]. 2019.

KUMAR R, GOYAL R . On cloud security requirements,threats,vulnerabilities and countermeasures:a survey[J]. Computer Science Review, 2019(33): 1-48.

易树平, 李嘉佳, 易茜 . 基于行为流图的可信交互检测方法[J]. 控制与决策, 2020,35(11): 2715-2722.

YI S P, LI J J, YI Q . Trustworthy interaction detection method based onuser behavior flow diagram[J]. Control and Decision, 2020,35(11): 2715-2722.

MALATHI R, JEBERSON RETNARAJ R . An integrated approach of physical biometric authentication system[J]. Procedia Computer Science, 2016(85): 820-826.

PUSARA M, BRODLEY C E . User re-authentication via mouse movements[C]// Proceedings of the 2004 ACM Workshop on Visualization and Data Mining for Computer Security-VizSEC/DMSEC '04. NewYork:ACM Press, 2004: 1-8.

AHMED A A E, TRAORE I . A new biometric technology based on mouse dynamics[J]. IEEE Transactions on Dependable and Secure Computing, 2007,4(3): 165-179.

FEHER C, ELOVICI Y, MOSKOVITCH R ,et al. User identity verification via mouse dynamics[J]. Information Sciences, 2012,201: 19-36.

ZHENG N, PALOSKI A, WANG H N . An efficient user verification system using angle-based mouse movement biometrics[J]. ACM Transactions on Information and System Security, 2016,18(3): 1-27.

沈超, 蔡忠闽, 管晓宏 ,等. 基于鼠标行为特征的用户身份认证与监控[J]. 通信学报, 2010,31(7): 68-75.

SHEN C, CAI Z M, GUAN X H ,et al. User authentication and monitoring based on mouse behavioral features[J]. Journal on Communications, 2010,31(7): 68-75.

徐剑, 李明洁, 周福才 ,等. 基于用户鼠标行为的身份认证方法[J]. 计算机科学, 2016,43(2): 148-154.

XU J, LI M J, ZHOU F C ,et al. Identity authentication method based on user's mouse behavior[J]. Computer Science, 2016,43(2): 148-154.

YI Q, XIONG S Q, WANG B ,et al. Identification of trusted interactive behavior based on mouse behavior considering Web user's emotions[J]. International Journal of Industrial Ergonomics, 2020(76):102903.

CHONG P, ELOVICI Y, BINDER A . User authentication based on mouse dynamics using deep neural networks:a comprehensive study[J]. IEEE Transactions on Information Forensics and Security, 2020(15): 1086-1101.

NOY L, ALON U, FRIEDMAN J . Corrective jitter motion shows similar individual frequencies for the arm and the finger[J]. Experimental Brain Research, 2015,233(4): 1307-1320.

ALPAR O . Frequency spectrograms for biometric keystroke authentication using neural network based classifier[J]. Knowledge-Based Systems, 2017(116): 163-171.

ALPAR O . TAPSTROKE:a novel intelligent authentication system using tap frequencies[J]. Expert Systems With Applications, 2019(136): 426-438.

易茜, 黎伟, 易树平 ,等. 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 Q, LI W, YI S P ,et al. 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.

BREIMAN L . Bagging predictors[J]. Machine Learning, 1996,24(2): 123-140.

MISHRA P K, YADAV A, PAZOKI M . A novel fault classification scheme for series capacitor compensated transmission line based on bagged tree ensemble classifier[J]. IEEE Access, 2018(6): 27373-27382.

LE TT H, KANG H, KIM H . Household appliance classification using lower odd-numbered harmonics and the bagging decision tree[J]. IEEE Access, 2020(8): 55937-55952.

工业互联网产业联盟. 工业互联网平台白皮书（2017）[EB]. 2017.

Alliance of Industrial Internet. Industrial internet platform white paper (2017)[EB]. 2017.

MORASSO P, MUSSA IVALDI F A . Trajectory formation and handwriting:a computational model[J]. Biological Cybernetics, 1982,45(2): 131-142.

UNO Y, KAWATO M, SUZUKI R . Formation and control of optimal trajectory in human multijoint arm movement[J]. Biological Cybernetics, 1989,61(2): 89-101.

LEE D, PORT N L, GEORGOPOULOS A P . Manual interception of moving targets.II.On-line control of overlapping submovements[J]. Experimental Brain Research, 1997,116(3): 421-433.

NOVAK K E, MILLER L E, HOUK J C . The use of overlapping submovements in the control of rapid hand movements[J]. Experimental Brain Research, 2002,144(3): 351-364.

郇战, 陈学杰, 吕士云 ,等. 基于多分类器融合的步态识别方法[J]. 计算机应用, 2019,39(3): 712-718.

HUAN Z, CHEN X J, LYU S Y ,et al. Gait recognition method based on multiple classifier fusion[J]. Journal of Computer Applications, 2019,39(3): 712-718.

HUANG N E, SHEN Z, LONG S R ,et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London Series A:Mathematical,Physical and Engineering Sciences, 1998,454(1971): 903-995.

AYENU-PRAH A, ATTOH-OKINE N, . A criterion for selecting relevant intrinsic mode functions in empirical mode decomposition[J]. Advances in Adaptive Data Analysis, 2010,2(1): 1-24.

MANN K A, WERNERE F W, PALMER A K . Frequency spectrum analysis of wrist motion for activities of daily living[J]. Journal of Orthopaedic Research, 1989,7(2): 304-306.

HARILAL A, TOFFALINI F, CASTELLANOS J ,et al. TWOS:adataset of malicious insider threat behavior based on a gamified competition[C]// Proceedings of the 2017 International Workshop on Managing Insider Security Threats. New York:ACM, 2017: 45-56.

HAN H, WANG W Y, MAO B H . Borderline-SMOTE:anew over-sampling method in imbalanced data sets learning[C]// Advances in Intelligent Computing, 2005: 878-887.

GAMBOA H, FRED A L N, JAIN A K . Webbiometrics:user verification via web interaction[C]// Proceedings of 2007 Biometrics Symposium. Piscataway:IEEE Press, 2007: 1-6.

SHEN C, CAI Z M, GUAN X H . Continuous authentication for mouse dynamics:a pattern-growth approach[C]// Proceedings of IEEE/IFIP International Conference on Dependable Systems and Networks (DSN 2012). Piscataway:IEEE Press, 2012: 1-12.

ZHENG N, PALOSKI A, WANG H N . An efficient user verification system via mouse movements[C]// CCS '11:Proceedings of the 18th ACM Conference on Computer and Communications Security. 2011: 139-150.

LINZ . Research on agent-based human-information system trusted interaction in distributed cooperative work environment[J]. The Open Automation and Control Systems Journal, 2011,3(1): 1-7.

Views

189

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on trust measurement method for initial access of industrial internet edge terminals

Concept, architecture and key technologies of industrial internet

Research on a lightweight framework of industrial Internet-oriented manufacturing service collaboration level agreement

Research on distributed identity authentication mechanism of IoT device based on blockchain

Related Author

Ya YU

Yusun FU

Jin QI

Wei WANG

Mengxi CHEN

Bin XU

Zhenjiang DONG

Yanfei SUN

Related Institution

Ningbo Artificial Intelligence Institute of Shanghai Jiao Tong University

School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University

Key Laboratory of System Control and Information Processing, Ministry of Education of China

Shanghai Engineering Research Center of Intelligent Control and Management

School of Internet of Things, Nanjing University of Posts and Telecommunications

⁰