Advances in Keystroke Dynamics Techniques to Group Users Sessions

Yıl 2015, Cilt: 4 Sayı: 2, 26 - 38, 30.06.2015

Sebastian Sznur

Öz

Users identification by means of their keystroke pattern is an old and known technique. Several works had analysed and solved some of the most important issues in this area, but with the advances of technology, previous techniques have quickly become obsolete. Users as well as attackers spend a lot of time typing on their computers, locally and remotely. Their keystrokes leave a trace of patterns whose dynamism can be analysed and used to verify their identity. We propose to use unsupervised clustering algorithms to group user sessions together in order to correctly identify them. Furthermore, the verification of keystroke dynamics techniques has always been difficult because of the lack of a labeled free text dataset. To overcome this issue, we capture a large dataset of labeled keystrokes of more than two and a half million digraphs. Results show that users can be accurately grouped.

Anahtar Kelimeler

Security, Keystroke Dynamics, Biometrics

Kaynakça

• Obaidat M. S. and Sadoun B. Verification of computer users using keystroke dynamics. IEEE COMPUTER SOCIETY. 1997.
• Ahmed I and Traore A.A. Biometric recognition based on free-text Transactions 10.1109/TCYB.2013.2257745. Cybernetics, 2013. doi: on, 99,
• Warwick A. and Alsultan K. Keystroke dynamics authentication: A survey of free-text methods. IJCSI International Journal of Computer Science Issues, Vol. 10, Issue 4, No 1, 2013.
• Aráujo L. C. F., Lizárraga M. G. et al. Autentificación personal por dinámica de tecleo basada en lógica difusa. IEEE COMPUTER SOCIETY, 2005.
• Gunetti D. Picardi C. Bergadano, F. User authentication through keystroke dynamics. ACM Transactions on Information and System Security, 5(4):367–397, 2002. doi: 10.1145/581271.581272.
• Cheng-Huang J. and Shiuhpyng S. et al. Keystroke statistical learning model for web authentication. Proceedings of the 2nd ACM symposium on Information, computer and communications security. Singapore, ACM, 2007.
• Devijver P. A. and Kittler J. Pattern Recognition: A Statistical Approach. Prentice-Hall, Londres, 1982.
• Hart P. E., Stork D. G. and Duda R. O. Pattern classification. New York: Wiley, 2001.
• Picardi C. and Gunetti, D. Keystroke analysis of free text. ACM Transactions on Information and System Security, 8(3):312–347, 2005. doi: 10.1145/1085126.1085129.
• Shepherd S. J. Continuous authentication by analysis of keyboard typing characteristics. IEEE COMPUTER SOCIETY, 1995.
• Narendra K. S. and Parthsarathy K. Identification and control of dynamical system using neural networks. IEENN, 1(1):4–27, 1990.
• Prabhakar R. M., Christopher D. and Hinrich S. Introduction to Information Retrieval. Cambridge: Cambridge University Press. Cambridge Books Online, 2012.
• Paeckock A, Ke X. et al. Typing patterns: A key to user identification. IEEE COMPUTER SOCIETY, 2004.
• Mroczkowski P. Identity verification using keyboard statistics. Linkoping University, Electronic Press, 2004.
• Liang V., Chambers J., Mackenzie C. and Robinson, J. Computer user verification using login string keystroke dynamics. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 28(2):236– 241, 1998. doi: 10.1109/3468.661150.
• Kacholia V. and Pandit S. Biometric authentication using shashankpandit.com. (bioart). 2004.
• Mark S. Information Security: Principles and Practice, 2nd edition. Wiley, 2011.
• Yu Enzhe and Cho Sungzoon. Biometrics-based password identity verification: Some practical issues and solutions. 2003. Http://dmlab.snu.ac.kr.
• Cho Tai-Hoon. Pattern classification methods for keystroke analysis. SICE-ICASE International Joint Conference, 2006.
• Sorondo G., Garcıa S., Meschino G. J., Zamonsky Pedernera G. and Sznur S. Revisiting clustering methods to their application on keystroke dynamics for intruder classification. In IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications, volume 9, pages 36–40, 2010. Taranto, Italia.