![](bg8.png)
7 CONCLUSION AND OUTLOOK
With the increasing demand for stronger authentica-
tion methods, biometric authentication systems are
being improved and implemented. Due to the grow-
ing number of sensors and diverse set of user-driven
features, keystroke dynamics is an on-the-go authen-
tication method, which does not hinder the user. But
is it good enough? In order to analyze this, we first es-
tablish several requirements leading to an evaluation
scheme. This evaluation scheme was applied to com-
pare three selected approaches. Based on the compar-
ison, keystroke dynamics and the gathered require-
ments are discussed. While the pure typing rhythm
is too inaccurate, the incorporation of other factors
such as pressure strength and size helps to improve
the method. In the next step, we plan to extend the
current evaluation and analyze and compare the se-
curity of keystroke dynamics with traditional authen-
tication methods. A real-world user study helps to
evaluate data acquisition under different environmen-
tal conditions. This may lead to a common dataset,
improving the comparison of different proposed ap-
proaches. Last but not least, we evaluate the addition
of emojis and other symbols.
REFERENCES
Al-Obaidi, N. M. and Al-Jarrah, M. M. (2016). Statisti-
cal Keystroke Dynamics System on Mobile Devices
for Experimental Data Collection and User Authenti-
cation. In Proceedings of the 9th International IEEE
Conference on Developments in eSystems Engineer-
ing (DeSE), pages 123–129.
Alghamdi, S. J. and Elrefaei, L. A. (2015). Dynamic User
Verification Using Touch Keystroke Based on medi-
ans Vector Proximity. In Proceedings of the 7th In-
ternational IEEE Conference on Computational Intel-
ligence, Communication Systems and Networks (CIC-
SyN), pages 121–126.
Alt, F. and Schneegass, S. (2022). Beyond passwords –
challenges and opportunities of future authentication.
IEEE Security & Privacy, 20(1):82–86.
Campobasso, M. and Allodi, L. (2020). Impersonation-
as-a-Service: Characterizing the Emerging Criminal
Infrastructure for User Impersonation at Scale. In
Proceedings of the 2020 ACM SIGSAC Conference
on Computer and Communications Security (CCS),
pages 1665–1680.
Crawford, H. (2010). Keystroke dynamics: Characteristics
and opportunities. In Proceedings of the 8th Inter-
national IEEE Conference on Privacy, Security and
Trust (PST), pages 205–212.
de Marcos, L., Mart
´
ınez-Herr
´
aiz, J.-J., Junquera-S
´
anchez,
J., Cilleruelo, C., and Pages-Ar
´
evalo, C. (2021). Com-
paring Machine Learning Classifiers for Continuous
Authentication on Mobile Devices by Keystroke Dy-
namics. Electronics, 10(14).
Kim, D. I., Lee, S., and Shin, J. S. (2020). A New Feature
Scoring Method in Keystroke Dynamics-Based User
Authentications. IEEE Access, 8:27901–27914.
Krishnamoorthy, S., Rueda, L., Saad, S., and Elmiligi, H.
(2018). Identification of User Behavioral Biometrics
for Authentication Using Keystroke Dynamics and
Machine Learning. In Proceedings of the 2nd Inter-
national ACM Conference on Biometric Engineering
and Applications (ICBEA), pages 50–57.
Lee, H., Hwang, J. Y., Kim, D. I., Lee, S., Lee, S.-H.,
and Shin, J. S. (2018). Understanding Keystroke
Dynamics for Smartphone Users Authentication and
Keystroke Dynamics on Smartphones Built-In Mo-
tion Sensors. Security and Communication Networks,
2018:2567463.
Li, S. Z. and Jain, A. (2009). Encyclopedia of Biometrics.
Springer.
Maalej, A. and Kallel, I. (2020). Does Keystroke Dynam-
ics tell us about Emotions? A Systematic Literature
Review and Dataset Construction. In Proceedings of
the 16th International IEEE Conference on Intelligent
Environments (IE), pages 60–67.
Mahfouz, A., Mahmoud, T. M., and Sharaf Eldin, A.
(2017). A Behavioral Biometric Authentication
Framework on Smartphones. In Proceedings of the
2017 ACM on Asia Conference on Computer and
Communications Security (ASIA CCS), pages 923–
925.
Makrushin, A., Kauba, C., Kirchgasser, S., Seidlitz, S.,
Kraetzer, C., Uhl, A., and Dittmann, J. (2021).
General Requirements on Synthetic Fingerprint Im-
ages for Biometric Authentication and Forensic In-
vestigations. In Proceedings of the ACM Work-
shop on Information Hiding and Multimedia Security
(IH&MMSec), pages 93–104.
Mayrhofer, R. and Sigg, S. (2021). Adversary Models for
Mobile Device Authentication. ACM Comput. Surv.,
54(9).
Monrose, F. and Rubin, A. D. (2000). Keystroke dynamics
as a biometric for authentication. Future Generation
Computer Systems, 16(4):351–359.
Pisani, P. H. and Lorena, A. C. (2013). A systematic re-
view on keystroke dynamics. Journal of the Brazilian
Computer Society, 19(4):573–587.
Preibusch, S. and Bonneau, J. (2010). The Password Game:
Negative Externalities from Weak Password Practices.
In Alpcan, T., Butty
´
an, L., and Baras, J. S., editors,
Decision and Game Theory for Security, pages 192–
207. Springer Berlin Heidelberg.
Schiavone, E., Ceccarelli, A., Bondavalli, A., and Car-
valho, A. M. (2016). Usability assessment in a multi-
biometric continuous authentication system. In Pro-
ceedings of the 7th IEEE Latin-American Symposium
on Dependable Computing (LADC), pages 43–50.
Shekhawat, K. and Bhatt, D. P. (2019). Recent Advances
and Applications of Keystroke Dynamics. In Proceed-
ings of the International IEEE Conference on Com-
ICISSP 2023 - 9th International Conference on Information Systems Security and Privacy
364