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TypingSuite: Integrated Software for Presenting Stimuli, and Collecting and Analyzing Typing Data

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Abstract

Research into typing patterns has broad applications in both psycholinguistics and biometrics (i.e., improving security of computer access via each user’s unique typing patterns). We present a new software package, TypingSuite, which can be used for presenting visual and auditory stimuli, collecting typing data, and summarizing and analyzing the data. TypingSuite is a Java-based software package that is platform-independent and open-source. To validate TypingSuite as a beneficial tool for researchers who are interested in keystroke dynamics, two studies were conducted. First, a behavioural experiment based on single word typing was conducted that replicated two well-known findings in typing research, namely the lexicality and frequency effects. The results confirmed that words are typed faster than pseudowords and that high frequency words are typed faster than low frequency words. Second, in regard to biometrics, it was also shown that typing data from the same user are more similar than data from different users. Because TypingSuite allows its users to easily implement an experiment and to collect and analyze data within a single software package, it holds promise for being a valuable educational and research tool in language-related sciences such as psycholinguistics and natural language processing.

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Notes

  1. The British online-security consultant NTA Monitor surveyed 500 computer users at Victoria Station, London over a week-long period in November 2002. According to the results of this study, the typical intensive Information Technology users have to use and remember 21 passwords for their accounts. Unfortunately, they either use common words as passwords or keep written records of them, neither of which is advisable from a security standpoint Hayday (2002).

  2. To verify that our numerical implementation of these two tests was sound, we confirmed that the results given by TypingSuite were identical to those obtained with IBM SPSS Statistics (IBM 2011).

References

  • Bergadano, F., Gunetti, D., & Picardi, C. (2002). User authentication through keystroke dynamics. ACM Transactions on Information System Security, 5, 367–397.

    Article  Google Scholar 

  • Bergadano, F., Gunetti, D., & Picardi, C. (2003). Identity verification through dynamic keystroke analysis. Intelligent Data Analysis, 7, 469–496.

    Google Scholar 

  • Bosman, E. (1993). Age-related differences in the motoric aspects of transcription typing skill. Psychology and Aging, 8(1), 87–102.

    Article  PubMed  Google Scholar 

  • Cho, S., Han, C., Han, D., & Kim, H. (2000). Web based keystroke dynamics identity verification using neural network. Journal of Organizational Computing and Electronic Commerce, 10(4), 295–307.

    Article  Google Scholar 

  • Crump, M., & Logan, G. (2010). Hierarchical control and skilled typing: Evidence for word-level control over the execution of individual keystrokes. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36, 1369–1380.

    PubMed  Google Scholar 

  • Delattre, M., Bonin, P., & Barry, C. (2006). Written spelling to dictation: Sound-to-spelling regularity affects both writing latencies and durations. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32(6), 1330–1340.

    PubMed  Google Scholar 

  • Filho, J., & Freire, E. (2006). On the equalization of keystroke timing histograms. Pattern Recognition Letters, 27, 1440–1446.

    Article  Google Scholar 

  • Gaines, R., Lisowski, W., Press, S., & Shapiro, N. (1980). Authentication by keystroke timing: Some preliminary results. Technical Report, Rand Corporation.

  • Giot, R., El-Abed M., & Rosenberger, C. (2009). GREYC keystroke: A benchmark for keystroke dynamics biometric system. IEEE international conference on biometric: Theory, applications and systems.

  • Hayday, G. (2002). Security nightmare: How do you maintain 21 different passwords? Silicon.com, 2002-12-11.

  • Hu, J., Gingrich, D., & Sentosa, A. (2008). A k-nearest neighbor approach for user authentication through biometric keystroke dynamics. In Proceedings of IEEE international conference on communications, pp. 1556–1560.

  • IBM (2011). IBM SPSS statistics for windows, version 20.0. Armonk, NY: IBM Corp.

  • Inhoff, A. (1991). Word frequency during copytyping. Journal of Experimental Psychology: Human Perception and Performance, 17, 478–487.

    PubMed  Google Scholar 

  • Inhoff, A., Briihl, D., Bohemier, G., & Wang, J. (1992). Eye-hand span and coding of text during copytyping. Journal of Experimental Psychology: Learning, Memory, and Cognition, 18(2), 298–306.

    PubMed  Google Scholar 

  • Jared, D. (2002). Spelling-sound consistency and regularity effects in word naming. Journal of Memory and Language, 46, 723–750.

    Article  Google Scholar 

  • Killourhy, K., & Maxion, R. (2009). Comparing anomaly detectors for keystroke dynamics. In Proceedings of the 39th annual international conference on dependable systems and networks, pp. 125–134.

  • Kreiner, D., Price, R., & Gross, A. (2008). Linguistics boundaries as predictors of the time between letters in oral and typed spellings. The Journal of General Psychology, 135(2), 117–130.

    Article  PubMed  Google Scholar 

  • Larochelle, S. (1984). Some aspects of movements in skilled typewriting. In H. Bouma & D. G. Bouwhis (Eds.), Attention and performance X: Control of language processes (pp. 43–54). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Leijten, M., & Van Waes, L. (2006). Inputlog: New perspectives on the logging of on-line writing processes in a windows environment. In G. Rijlaarsdam (Series Ed.) and K. P. H. Sullivan, & E. Lindgren (Eds.), Studies in writing. Computer keystroke logging: Methods and applications (Vol. 18, pp. 73–93). Oxford: Elsevier.

  • Li, Y., Zhang, B., Cao, Y., Zhao, S., Gao, Y., & Liu, J. (2011). Study on the BeiHang keystroke dynamics database. International joint conference biometrics.

  • Massaro, D. W., & Lucas, P. A. (1984). Typing letter strings varying in orthographic structure. Acta Psychologica, 57, 109–131.

    Article  PubMed  Google Scholar 

  • Monrose, F., & Rubin, A. (1980). Keystroke dynamics as a biometric for authentication. Future Generation Computer System, 351–359.

  • Obaidat, M., & Sadoun, B. (1997). Verification of computer users using keystroke dynamics. IEEE Transactions on Systems, Man, and Cybernetics, 27(2), 261–269.

    Article  PubMed  Google Scholar 

  • Pagano, R., (2001). Understanding statistics in the behavioral sciences, 6th ed. Wadsworth.

  • Peacock, A., Ke, X., & Wilkerson, M. (2004). Typing patterns: A key to user identification. IEEE Security and Privacy, 2, 40–47.

    Article  Google Scholar 

  • Rodrigues, R., Yared, G., do N. Costa, C., Yabu-Uti, J., Violaro, F., & Ling, L. (2005). Biometric access control through numerical keyboards based on keystroke dynamics. In Proceedings of the international conference on biometrics, pp. 640–646.

  • Salthouse, T. (1986). Perceptual, cognitive, and motoric aspects of transcription typing. Psycholigical Bulletin, 99, 303–319.

    Article  Google Scholar 

  • Sang, Y., Shen, H., & Fan, P. (2005). Novel impostors detection in keystroke dynamics by support vector machine. Lecture Notes in Computer Science, pp. 666–669.

  • Service, E., & Turpeinen, R. (2001). Working memory in spelling: Evidence from backward typing. Memory, 9(4), 395–421.

    Article  Google Scholar 

  • Shanmugapriya, D., & Padmavathi, G. (2009). A survey of biometric keystroke dynamics: Approaches, security and challenges. International Journal of Computer Science and Inform Security, 5, 115–119.

    Google Scholar 

  • Sridhar, M., Abraham, T., Rebello, J., D’souza, W., & D’Souza, A. (2013). Intrusion detection using keystroke dynamics. Lect Notes in Electr En, 150, 137–144.

    Google Scholar 

  • Viviani, P., & Laissard, G. (1996). Motor templates in typing. Journal of Experimental Psychology: Human Percpetion and Performance, 22(2), 417–445.

    Google Scholar 

  • Wengelin, A., Torrance, M., Holmqvist, K., Simpson, S., Galbraith, D., Johansson, V., et al. (2009). Combined eyetracking and keystroke-logging methods for studying cognitive processes in text production. Behavior Research Methods, 41, 337–351.

    Article  PubMed  Google Scholar 

  • Wu, C., & Liu, Y. (2008). Queuing network modeling of transcription typing. ACM Transactions on Computer-Human Interaction, 15(1)

  • Yu, E., & Cho, S. (2004). Keystroke dynamics identity verification: Its problems and practical solutions. Computers and Security, 23, 428–440.

    Article  Google Scholar 

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Acknowledgments

The authors would like to acknowledge Connie Jess, Vlado Keselj, John Christie, and Michael Lawrence for helpful discussions regarding this work as well as Celeste Lefebvre and Gregory Francis for their feedback on this paper. E.L.M. was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC), the Killam Trusts, Dalhousie University, and the National Research Council of Canada (NRC).

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Authors and Affiliations

  1. Department of Psychology and Neuroscience, Dalhousie University, Halifax, NS, Canada

    Erin L. Mazerolle & Yannick Marchand

  2. Department of Radiology, University of Calgary, Calgary, AB, Canada

    Erin L. Mazerolle

  3. Faculty of Computer Science, Dalhousie University, 6050 University Avenue, PO BOX 15000, Halifax, NS, B3H 4R2, Canada

    Yannick Marchand

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  1. Erin L. Mazerolle
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  2. Yannick Marchand
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Correspondence to Yannick Marchand.

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Mazerolle, E.L., Marchand, Y. TypingSuite: Integrated Software for Presenting Stimuli, and Collecting and Analyzing Typing Data. J Psycholinguist Res 44, 127–139 (2015). https://doi.org/10.1007/s10936-014-9283-9

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