Handedness recognition through keystroke-typing behavior in computer forensics analysis

Chao Shen; Haodi Xu; Haijun Wang; Xiaohong Guan

doi:10.1109/TrustCom.2016.0175

Handedness recognition through keystroke-typing behavior in computer forensics analysis

Faculty of Electronic and Information Engineering

Science and Technology on Electronic Information Control Laboratory
Xi'an Jiaotong University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

Abstract

Recognizing computer users' handedness provides important clues for profiling computer criminals in digital forensic analysis. Existing technologies for handedness recognition have two main problems that limit their applicability in the scenario of digital crimes: they can be intrusive, and they require costly equipment. Our solution is to infer users' handedness by analyzing keystroke-typing behavior. Field studies are first conducted to gather users' keystroke-typing data during their interaction with computers. Timing features are extracted to characterize users' typing rhythms, and the correlation between keystroke features and handedness is analyzed. Classification techniques are then developed for handedness recognition. Experimental results show that the handedness could be efficiently and accurately inferred from users' keystroke-typing behavior, with recognition rates expressed by the Area Under the ROC Curve (AUC) of 87.75%. To our knowledge, this is the first work that infers users' handedness based on their keystroke-typing biometric during interaction with computers, without dedicated and explicit actions that require attention from users.

Original language	English
Title of host publication	Proceedings - 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1054-1060
Number of pages	7
ISBN (Electronic)	9781509032051
DOIs	https://doi.org/10.1109/TrustCom.2016.0175
State	Published - 2016
Event	Joint 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016 - Tianjin, China Duration: 23 Aug 2016 → 26 Aug 2016

Publication series

Name	Proceedings - 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016

Conference

Conference	Joint 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016
Country/Territory	China
City	Tianjin
Period	23/08/16 → 26/08/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

Keywords

Computer forensics analysis
Handedness recognition
Keystroke-typing behavior

Access to Document

10.1109/TrustCom.2016.0175

Cite this

Shen, C., Xu, H., Wang, H., & Guan, X. (2016). Handedness recognition through keystroke-typing behavior in computer forensics analysis. In Proceedings - 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016 (pp. 1054-1060). Article 7847058 (Proceedings - 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TrustCom.2016.0175

Shen, Chao ; Xu, Haodi ; Wang, Haijun et al. / Handedness recognition through keystroke-typing behavior in computer forensics analysis. Proceedings - 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1054-1060 (Proceedings - 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016).

@inproceedings{e310301d4fb944e594dada1bcb59f703,

title = "Handedness recognition through keystroke-typing behavior in computer forensics analysis",

abstract = "Recognizing computer users' handedness provides important clues for profiling computer criminals in digital forensic analysis. Existing technologies for handedness recognition have two main problems that limit their applicability in the scenario of digital crimes: they can be intrusive, and they require costly equipment. Our solution is to infer users' handedness by analyzing keystroke-typing behavior. Field studies are first conducted to gather users' keystroke-typing data during their interaction with computers. Timing features are extracted to characterize users' typing rhythms, and the correlation between keystroke features and handedness is analyzed. Classification techniques are then developed for handedness recognition. Experimental results show that the handedness could be efficiently and accurately inferred from users' keystroke-typing behavior, with recognition rates expressed by the Area Under the ROC Curve (AUC) of 87.75\%. To our knowledge, this is the first work that infers users' handedness based on their keystroke-typing biometric during interaction with computers, without dedicated and explicit actions that require attention from users.",

keywords = "Computer forensics analysis, Handedness recognition, Keystroke-typing behavior",

author = "Chao Shen and Haodi Xu and Haijun Wang and Xiaohong Guan",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; Joint 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016 ; Conference date: 23-08-2016 Through 26-08-2016",

year = "2016",

doi = "10.1109/TrustCom.2016.0175",

language = "英语",

series = "Proceedings - 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1054--1060",

booktitle = "Proceedings - 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016",

}

Shen, C, Xu, H, Wang, H & Guan, X 2016, Handedness recognition through keystroke-typing behavior in computer forensics analysis. in Proceedings - 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016., 7847058, Proceedings - 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016, Institute of Electrical and Electronics Engineers Inc., pp. 1054-1060, Joint 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016, Tianjin, China, 23/08/16. https://doi.org/10.1109/TrustCom.2016.0175

Handedness recognition through keystroke-typing behavior in computer forensics analysis. / Shen, Chao; Xu, Haodi; Wang, Haijun et al.
Proceedings - 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1054-1060 7847058 (Proceedings - 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Handedness recognition through keystroke-typing behavior in computer forensics analysis

AU - Shen, Chao

AU - Xu, Haodi

AU - Wang, Haijun

AU - Guan, Xiaohong

PY - 2016

Y1 - 2016

N2 - Recognizing computer users' handedness provides important clues for profiling computer criminals in digital forensic analysis. Existing technologies for handedness recognition have two main problems that limit their applicability in the scenario of digital crimes: they can be intrusive, and they require costly equipment. Our solution is to infer users' handedness by analyzing keystroke-typing behavior. Field studies are first conducted to gather users' keystroke-typing data during their interaction with computers. Timing features are extracted to characterize users' typing rhythms, and the correlation between keystroke features and handedness is analyzed. Classification techniques are then developed for handedness recognition. Experimental results show that the handedness could be efficiently and accurately inferred from users' keystroke-typing behavior, with recognition rates expressed by the Area Under the ROC Curve (AUC) of 87.75%. To our knowledge, this is the first work that infers users' handedness based on their keystroke-typing biometric during interaction with computers, without dedicated and explicit actions that require attention from users.

AB - Recognizing computer users' handedness provides important clues for profiling computer criminals in digital forensic analysis. Existing technologies for handedness recognition have two main problems that limit their applicability in the scenario of digital crimes: they can be intrusive, and they require costly equipment. Our solution is to infer users' handedness by analyzing keystroke-typing behavior. Field studies are first conducted to gather users' keystroke-typing data during their interaction with computers. Timing features are extracted to characterize users' typing rhythms, and the correlation between keystroke features and handedness is analyzed. Classification techniques are then developed for handedness recognition. Experimental results show that the handedness could be efficiently and accurately inferred from users' keystroke-typing behavior, with recognition rates expressed by the Area Under the ROC Curve (AUC) of 87.75%. To our knowledge, this is the first work that infers users' handedness based on their keystroke-typing biometric during interaction with computers, without dedicated and explicit actions that require attention from users.

KW - Computer forensics analysis

KW - Handedness recognition

KW - Keystroke-typing behavior

UR - https://www.scopus.com/pages/publications/85015237195

U2 - 10.1109/TrustCom.2016.0175

DO - 10.1109/TrustCom.2016.0175

M3 - 会议稿件

AN - SCOPUS:85015237195

T3 - Proceedings - 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016

SP - 1054

EP - 1060

BT - Proceedings - 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - Joint 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016

Y2 - 23 August 2016 through 26 August 2016

ER -

Shen C, Xu H, Wang H , Guan X. Handedness recognition through keystroke-typing behavior in computer forensics analysis. In Proceedings - 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1054-1060. 7847058. (Proceedings - 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016). doi: 10.1109/TrustCom.2016.0175

Handedness recognition through keystroke-typing behavior in computer forensics analysis

Abstract

Publication series

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UN SDGs

Keywords

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