Behavioral biometric authentication on smartphone using keystroke dynamics

Vaishnav, Pragya; Kaushik, Manju; Raja, Linesh

doi:10.47974/JDMSC-1645

Open Access Research Article

Behavioral biometric authentication on smartphone using keystroke dynamics

Pragya Vaishnavpragya.vaishnav23@gmail.comAffiliation 1Amity Institute of Information TechnologyJaipur 303007, RajasthanAmity University RajasthanIndiaAffiliation 2Department of Computer ApplicationsJaipur 303007, RajasthanManipal University JaipurIndiaView full profile → , Manju Kaushikmkaushik@jpr.amity.eduAmity Institute of Information TechnologyJaipur 303007, RajasthanAmity University RajasthanIndiaView full profile → , ^*Linesh RajaCorresponding authorlineshraja@gmail.comDepartment of Computer ApplicationsJaipur 303007, RajasthanManipal University JaipurIndiaView full profile →

* Corresponding author · click or hover a name for details

pp. 591–600Vol. 26Issue 2March 2022DOI: 10.47974/JDMSC-1645 Crossmark XML

Received:: 05 Jun 2021
Accepted:: 05 Jul 2022
Published Online:: 30 Mar 2023
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-1645
Pages:: 591–600

Abstract

Over the past few years, smartphones have revolutionized our life. Authentication plays important role as all private and business data are stored in smartphone. Password authentication is not secure technique as password can be easily stolen by hackers. Hence, need to use advance technology to increase the security of authentication. Keystroke Dynamics is behavioral based authentication technique. It identifies the user identity through their typing behavior to check user is genuine or imposter. In this paper author is presenting a KDSP system for Smart devices to increase the security of authentication at login time. On the basis of timing feature set, typing speed, flight time and error rate of keystroke dynamics author is checking the identity of the user. Proposed system works on all the Android smartphones and tablets. All experimental work was performed on 268 subjects to capture keystroke data on server. In Statistical analysis, Equal-error-rate (EER) is measured 7.415% of the 60 subjects for False acceptance rate (FAR) and 208 subjects for False rejection rate (FRR).

Keywords

KDSP Pass-Value EER FRR FAR SD CV

Subject Classifications

4A62

References

[1] Teh, Pin Shen, et al. “A survey on touch dynamics authentication in mobile devices.” Computers & Security 59 (2016): 210-235.
[2] Mondal, Soumik, and Patrick Bours. “Combining keystroke and mouse dynamics for continuous user authentication and identification.” 2016 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA). IEEE, 2016.
[3] Tasia, Cheng‐Jung, et al. “Two novel biometric features in keystroke dynamics authentication systems for touch screen devices.” Security and Communication Networks 7.4 (2014): 750-758.
[4] Mathur, Bhawana, and Manju Kaushik. “Data analysis utilizing principal component analysis.” Int J Eng Res Technol 11.2 (2018): 333-348.
[5] Anusas-Amornkul, Tanapat. “Strengthening password authentication using keystroke dynamics and smartphone sensors.” Proceedings of the 9th International Conference on Information Communication and Management. 2019.
[6] Al-Obaidi, Noor Mahmood, and Mudhafar M. Al-Jarrah. “Statistical keystroke dynamics system on mobile devices for experimental data collection and user authentication.” 2016 9th International Conference on Developments in eSystems Engineering (DeSE). IEEE, 2016.
[7] Salem, Asma, et al. “Analysis of strong password using keystroke dynamics authentication in touch screen devices.” 2016 Cybersecurity and Cyberforensics Conference (CCC). IEEE, 2016.
[8] Draffin, Benjamin, Jiang Zhu, and Joy Zhang. “Keysens: Passive user authentication through micro-behavior modeling of soft keyboard interaction.” International Conference on Mobile Computing, Applications, and Services. Springer, Cham, 2013.
[9] Mathur, Bhawana, and Manju Kaushik. “Empirical Analysis of Metrics Using UML Class Diagram.” International Journal of Advanced Computer Science and Applications 7.5 (2016).
[10] Jia, Weichen, et al. “High Security User Authentication Based on Piezoelectric Keystroke Dynamics Applying to Multiple Emotional Responses.” IEEE Sensors Journal 22.3 (2021): 2814-2822.
[11] Chang, Ting-Yi, Cheng-Jung Tsai, and Jyun-Hao Lin. “A graphical-based password keystroke dynamic authentication system for touch screen handheld mobile devices.” Journal of Systems and Software 85.5 (2012): 1157-1165.
[12] Cilia, Darren, and Frankie Inguanez. “Multi-model authentication using keystroke dynamics for smartphones.” 2018 IEEE 8th International Conference on Consumer Electronics-Berlin (ICCE-Berlin). IEEE, 2018.
[13] Mathur, Bhawana, and Manju Kaushik. “Comparing Different Classification Techniques Using Data Mining Tools.” Journal Homepage: http://www. ijmra. us 8.9 (2018).
[14] Baljit Singh Saini, Navdeep Kaur, Kamaljit Singh Bhatia & Ashish Kumar Luhach (2019) “Analyzing user typing behaviour in different positions using keystroke dynamics for mobile phones”, Journal of Discrete Mathematical Sciences and Cryptography, 22:4, 591-603, DOI: 10.1080/09720529.2019.1638580.
[15] Vishnu Shankar & Karan Singh (2019) “An improved user authentication scheme on smartphone using dominating attribute of touch data”, Journal of Discrete Mathematical Sciences and Cryptography, 22:8, 1549-1561, DOI: 10.1080/09720529.2019.1695903.
[16] Karatzouni, Sevasti, and Nathan Clarke. “Keystroke analysis for thumb-based keyboards on mobile devices.” IFIP International Information Security Conference. Springer, Boston, MA, 2007.
[17] Corpus, Kyle R., et al. “Mobile user identification through authentication using keystroke dynamics and accelerometer biometrics.” Proceedings of the International Conference on Mobile Software Engineering and Systems. 2016.
[18] Temper, Marlies, and Simon Tjoa. “The applicability of fuzzy rough classifier for continuous person authentication.” 2016 International Conference on Software Security and Assurance (ICSSA). IEEE, 2016.

Views: 488Downloads: 128Citations: 2