A second look at the performance of neural networks for keystroke dynamics using a publicly available dataset

Yasin Uzun*, Kemal Bicakci

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)


Keystroke Dynamics, which is a biometric characteristic that depends on typing style of users, could be a viable alternative or a complementary technique for user authentication if tolerable error rates are achieved. Most of the earlier studies on Keystroke Dynamics were conducted with irreproducible evaluation conditions therefore comparing their experimental results are difficult, if not impossible. One of the few exceptions is the work done by Killourhy and Maxion, which made a dataset publicly available, developed a repeatable evaluation procedure and evaluated the performance of different methods using the same methodology. In their study, the error rate of neural networks was found to be one of the worst-performing. In this study, we have a second look at the performance of neural networks using the evaluation procedure and dataset same as in Killourhy and Maxion's work. We find that performance of artificial neural networks can outperform all other methods by using negative examples. We conduct comparative tests of different algorithms for training neural networks and achieve an equal error rate of 7.73% with Levenberg-Marquardt backpropagation network, which is better than equal error rate of the best-performing method in Killourhy and Maxion's work.

Original languageEnglish
Pages (from-to)717-726
Number of pages10
JournalComputers and Security
Issue number5
Publication statusPublished - Jul 2012
Externally publishedYes


We would like to thank TUBITAK (The Scientific and Technological Research Council of Turkey) for providing financial support to Yasin Uzun during his PhD study. We thank Musa Ataş, Fatih Kaya and anonymous reviewers for their valuable comments on the manuscript. Dr. Kemal Bıçakcı is currently an associate professor at Computer Engineering Department, TOBB (The Union of Chambers and Commodity Exchanges of Turkey) University of Economics and Technology. He has obtained his PhD degree from Middle East Technical University, Ankara, Turkey in 2003. Between 2004 and 2006, he was a postdoc researcher in Vrije Universiteit Amsterdam working with Prof. Tanenbaum in EU FP6 project named SecurE-Justice. His previous research experience includes several NSF funded security projects in which he participated as a research assistant during his MS studies in University of Southern California, Los Angeles, USA. Dr. Bıçakcı has directed a research project on usable security funded by Turkish Scientific and Technological Research Institute (TUBITAK).

FundersFunder number
Turkish Scientific and Technological Research Institute
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu


    • Backpropagation
    • Biometrics
    • Keystroke dynamics
    • Neural networks
    • Verification


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