Abstract
As of now, the performance of keystroke dynamics biometric in user recognition is not acceptable in practice due to intra-class variations, high failure to enroll rate (FER) or various troubles in data acquisition methods or diverse use of sensing devices. As per the previous study, the performance of this technique can be improved by incorporation of gender information, a soft biometric characteristic, extracted from the typing pattern on a computer keyboard that provides some additional information about the user. This soft biometric trait has low user discriminating power but can be used to enhance the performance of user recognition in accuracy and time efficiency. Furthermore, it has been observed that the age group (18β30/30+βorβ<18/18+), gender (male/female), handedness (left-handed/right-handed), hand(s) used (one hand/both hands), typing skill (touch/others), and emotional states (anger/excitation) can be extracted from the way of typing on a computer keyboard for single predefined text. In this paper, we are interested in identifying multiple soft biometric traits using two leading machine learning methods: support vector machine with radial basis function (SVM-RBF) and fuzzy-rough nearest neighbor with vaguely quantified rough set (FRNN-VQRS) on multiple publicly available authentic and recognized keystroke dynamics datasets collected through a computer keyboard as well as touchscreen phone. The performance of machine learning methods are changed significantly in changing dataset in keystroke dynamics domain, but the evaluation performance of FRNN-VQRS in our experiment is promising and consistent in identifying traits. At the end, the impacts of the incorporation of soft biometric traits with primary biometric characteristics in user recognition are presented and compared the evaluation performance of nine anomaly detectors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Roy, S., Roy, U., Sinha, D.D.: Free-text user authentication technique through keystroke dynamics. In: 2014 International Conference of High Performing Computer Application ICHPCA 2014 (2015)
CENELEC, alarm systemsβAccess control systems for use in security applicationsβpart 1. In: System Requirements, EN 50133-1 edition (1996)
Zulkarnain, S., et al.: Soft biometrics for keystroke dynamics: profiling individuals while typing passwords. Comput. Secur. (2014)
Epp, C., Lippold, M., Mandryk, R.L.: Identifying emotional states using keystroke dynamics. In: Proceedings of SIGCHI Conference Human Factors Computer System, pp. 715β724 (2011)
Roy, S., Roy, U., Sinha, D.D.: ACO-random forest approach to protect the kids from internet threats through keystroke. Int. J. Eng. Technol. 2β9 (2017) (Accepted)
Uzun, Y., Bicakci, K., Uzunay, Y.: Could We Distinguish Child Users from Adults Using Keystroke Dynamics? (2014)
Gaines, R.S., Lisowski, W., Press, S.J., Shapiro, N.: Authentication by Keystroke Timing: Some Preliminary Results, in Technical Report R-2526-NSF. Rand Corporation, May (1980)
Jain, A., Nandakumar, K., Lu, X., Park, U.: Integrating faces, fingerprints, and soft biometric traits for user recognition. Biometr. Authenticat. no. May, 259β269 (2004)
Frank, E., Hall, M.A., Witten, I.H.: The Weka Workbench Data Mining Practical Machine Learning Tools and Techniques, 4th ed. (1999)
Giot, R., Rosenberger, C.: A new soft biometric approach for keystroke dynamics based on gender recognition. Int. J. Inf. Technol. Manag. Spec. Issue Adv. Trends Biometric. 11(August), 1β16 (2012)
Idrus, S.Z.S., Cherrier, E., Rosenberger, C., Bours, P.: Soft biometrics for keystroke dynamics. In: 10th International Conference on Image Analysis and Recognition (ICIAR), pp. 11β18 (2013)
Killourhy, K.S., Maxion, R.A.: Comparing anomaly-detection algorithms for keystroke dynamics. In: Proceedings of the International Conference on Dependable Systems and Networks, pp. 125β134 (2009)
El-Abed, M., Dafer, M., El Khayat, R.: RHU keystroke: a mobile-based benchmark for keystroke dynamics systems. In: 2014 International Carnahan Conference Secured Technology, pp. 1β4 (2014)
Antal, M., Szabo, L.Z.: An evaluation of one-class and two-class classification algorithms for keystroke dynamics authentication on mobile devices. In: Proceedings of 2015 20th International Conference Control System Computer Science CSCS 2015, pp. 343β350 (2015)
Loy, C.C., Lim, C.P., Lai, W.K.: Pressure-based typing biometrics user authentication using the fuzzy ARTMAP neural network. In: International Conference on Neural Information Processing (ICONIP) (2005)
Roth, J., Liu, X., Ross, A., Metaxas, D.: Biometric authentication via keystroke sound. In: 2013 International Conference on Biometrics, pp. 1β8 (2013)
MontalvΓ£o, J., Freire, E.O., Bezerra, M.A., Garcia, R.: Contributions to empirical analysis of keystroke dynamics in passwords. Pattern Recogn. Lett. (2015)
Bello, L., Bertacchini, M.: Collection and publication of a fixed text keystroke dynamics dataset. In: CACIC 2010βXVI Argentino Ciencias LA Congress of Computer Science, pp. 822β831 (2010)
Idrus, S.Z.S., Cherrier, E., Rosenberger, C., Bours, P.: Soft biometrics database: a benchmark for keystroke dynamics biometric systems. In: 2013 International Conference of the Biometrics Special Interest Group (BIOSIG), 2013, no. September, pp. 1β8
Giot, R., El-Abed, M., Rosenberger, C.: Web-based benchmark for keystroke dynamics biometric systems: a statistical analysis. In: Intelligence of Information Hiding and Multimedia Signal Process, pp. 11β15 (2012)
Killourhy, K.S.: A scientific understanding of keystroke dynamics. Dr. Thesis, School of Computer Science, Computer Science Department Carnegie Mellon University Pittsburgh, PA 15213, no. January, pp. 1β198 (2012)
Idrus, S.Z.S., Cherrier, E., Rosenberger, C., Bours, P.: Soft biometrics database: a benchmark for keystroke dynamics biometric systems. In: 2013 International Conference of the Biometrics Special Interest Group (BIOSIG), September, pp. 1β8 (2013)
Jensen, R., Cornelis, C.: Fuzzy rough nearest neighbour classification and prediction. Theor. Comput. Sci. 412(42), 5871β5884 (2011)
Sarkar, M.: Fuzzy-rough nearest neighbor algorithms in classification. Fuzzy Sets Syst. 158(19), 2134β2152 (2007)
Hsu, C.-W., Chang, C.-C., Lin, C.-J.: A practical guide to support vector classification. BJU Int. 101(1), 1396β1400 (2008)
Alghamdi, S.J., Elrefaei, L.A.: Dynamic user verification using touch keystroke based on medians vector proximity. In: Proceedings of 7th International Conference Computer Intelligence Communication System Networks, CICSyN 2015, pp. 121β126 (2015)
Zulkarnain, S., et al.: Keystroke Dynamics Performance Enhancement With Soft Biometrics
Roy, S., Roy, U., Sinha, D.D.: Comparative study of various features-mining-based classifiers in different keystroke dynamics datasets. In: Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems, vol. 2, pp. 155β164 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
Β© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Roy, S., Roy, U., Sinha, D.D. (2019). Analysis of Typing Pattern in Identifying Soft Biometric Information and Its Impact in User Recognition. In: Chandra, P., Giri, D., Li, F., Kar, S., Jana, D. (eds) Information Technology and Applied Mathematics. Advances in Intelligent Systems and Computing, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-10-7590-2_5
Download citation
DOI: https://doi.org/10.1007/978-981-10-7590-2_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7589-6
Online ISBN: 978-981-10-7590-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)