Identifying Soft Biometric Features from a Combination of Keystroke and Mouse Dynamics

Sally Earl; James Campbell; Oliver Buckley

doi:10.1007/978-3-030-79997-7_23

Identifying Soft Biometric Features from a Combination of Keystroke and Mouse Dynamics

Sally Earl, James Campbell, Oliver Buckley

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

6 Citations (Scopus)

14 Downloads (Pure)

Abstract

In this preliminary paper, we investigate the use of keystroke and mouse dynamics as a means of identifying soft biometric features. We present evidence that combining features from both provides a more accurate means of identifying all of the soft biometric traits investigated, regardless of the machine learning method used. The data presented in this paper gives a thorough breakdown of accuracy scores from multiple machine learning methods and numbers of features used.

Original language	English
Title of host publication	Advances in Human Factors in Robots, Unmanned Systems and Cybersecurity - Proceedings of the AHFE 2021
Editors	Matteo Zallio, Carlos Raymundo Ibañez, Jesus Hechavarria Hernandez
Publisher	Springer
Chapter	23
Pages	184-190
Number of pages	7
ISBN (Electronic)	978-3-030-79997-7
ISBN (Print)	978-3-030-79996-0
DOIs	https://doi.org/10.1007/978-3-030-79997-7_23
Publication status	Published - 27 Jun 2021

Publication series

Name	Lecture Notes in Networks and Systems
Volume	268
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Keywords

Keystroke dynamics
Mouse dynamics
Soft biometrics

Access to Document

10.1007/978-3-030-79997-7_23

KDMDAccepted author manuscript, 134 KB

https://link.springer.com/10.1007/978-3-030-79997-7_23

Cite this

Earl, S., Campbell, J., & Buckley, O. (2021). Identifying Soft Biometric Features from a Combination of Keystroke and Mouse Dynamics. In M. Zallio, C. Raymundo Ibañez, & J. H. Hernandez (Eds.), Advances in Human Factors in Robots, Unmanned Systems and Cybersecurity - Proceedings of the AHFE 2021 (pp. 184-190). (Lecture Notes in Networks and Systems; Vol. 268). Springer. https://doi.org/10.1007/978-3-030-79997-7_23

Earl, Sally ; Campbell, James ; Buckley, Oliver. / Identifying Soft Biometric Features from a Combination of Keystroke and Mouse Dynamics. Advances in Human Factors in Robots, Unmanned Systems and Cybersecurity - Proceedings of the AHFE 2021. editor / Matteo Zallio ; Carlos Raymundo Ibañez ; Jesus Hechavarria Hernandez. Springer, 2021. pp. 184-190 (Lecture Notes in Networks and Systems).

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abstract = "In this preliminary paper, we investigate the use of keystroke and mouse dynamics as a means of identifying soft biometric features. We present evidence that combining features from both provides a more accurate means of identifying all of the soft biometric traits investigated, regardless of the machine learning method used. The data presented in this paper gives a thorough breakdown of accuracy scores from multiple machine learning methods and numbers of features used.",

keywords = "Keystroke dynamics, Mouse dynamics, Soft biometrics",

author = "Sally Earl and James Campbell and Oliver Buckley",

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Earl, S , Campbell, J & Buckley, O 2021, Identifying Soft Biometric Features from a Combination of Keystroke and Mouse Dynamics. in M Zallio, C Raymundo Ibañez & JH Hernandez (eds), Advances in Human Factors in Robots, Unmanned Systems and Cybersecurity - Proceedings of the AHFE 2021. Lecture Notes in Networks and Systems, vol. 268, Springer, pp. 184-190. https://doi.org/10.1007/978-3-030-79997-7_23

Identifying Soft Biometric Features from a Combination of Keystroke and Mouse Dynamics. / Earl, Sally ; Campbell, James; Buckley, Oliver.

Advances in Human Factors in Robots, Unmanned Systems and Cybersecurity - Proceedings of the AHFE 2021. ed. / Matteo Zallio; Carlos Raymundo Ibañez; Jesus Hechavarria Hernandez. Springer, 2021. p. 184-190 (Lecture Notes in Networks and Systems; Vol. 268).

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

TY - GEN

T1 - Identifying Soft Biometric Features from a Combination of Keystroke and Mouse Dynamics

AU - Earl, Sally

AU - Campbell, James

AU - Buckley, Oliver

PY - 2021/6/27

Y1 - 2021/6/27

N2 - In this preliminary paper, we investigate the use of keystroke and mouse dynamics as a means of identifying soft biometric features. We present evidence that combining features from both provides a more accurate means of identifying all of the soft biometric traits investigated, regardless of the machine learning method used. The data presented in this paper gives a thorough breakdown of accuracy scores from multiple machine learning methods and numbers of features used.

AB - In this preliminary paper, we investigate the use of keystroke and mouse dynamics as a means of identifying soft biometric features. We present evidence that combining features from both provides a more accurate means of identifying all of the soft biometric traits investigated, regardless of the machine learning method used. The data presented in this paper gives a thorough breakdown of accuracy scores from multiple machine learning methods and numbers of features used.

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M3 - Conference contribution

SN - 978-3-030-79996-0

T3 - Lecture Notes in Networks and Systems

SP - 184

EP - 190

BT - Advances in Human Factors in Robots, Unmanned Systems and Cybersecurity - Proceedings of the AHFE 2021

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A2 - Raymundo Ibañez, Carlos

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Earl S , Campbell J, Buckley O. Identifying Soft Biometric Features from a Combination of Keystroke and Mouse Dynamics. In Zallio M, Raymundo Ibañez C, Hernandez JH, editors, Advances in Human Factors in Robots, Unmanned Systems and Cybersecurity - Proceedings of the AHFE 2021. Springer. 2021. p. 184-190. (Lecture Notes in Networks and Systems). https://doi.org/10.1007/978-3-030-79997-7_23

Identifying Soft Biometric Features from a Combination of Keystroke and Mouse Dynamics

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