An approach to classify keystroke patterns for remote user authentication

• Autorzy: Saha J. , Chaki R
• Języki publikacji: EN

Abstrakty

EN

The authentication of users is of utmost importance in remote applications such as healthcare, banking, stock markets, etc. Key stroke dynamics are popular biometrics tools used for this purpose. Continuous authentication requires free text analysis which has a number of challenges. This paper has proposed a solution to identify the existence of a unique pattern in each individual user’s keystroke dynamics. However, dense zone identification is important factor in forming the intelligent database of user profile for authentication. The authors have categorized basic key stroke features of digraph into 57 groups depending on distance traversed while moving from one key to another. The paper also includes graphical plots of the grouping of time vector which has unveiled some characteristics of overlapping typing style of users. The authors hope to extend this logic for identifying behavioral disorders in users.

Słowa kluczowe

EN

free text; keystroke dynamics; biometrics; digraph grouping; dense area identification

PL

tekst; dynamika pisania na klawiaturze; biometria; struktura dwuznakowa; digraf

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2014
• Tom: Vol. 23
• Strony: 141--148
• Opis fizyczny: Bibliogr. 10 poz., tab., wykr.

Twórcy

autor

Saha J.

• University of Calcutta, India

autor

Chaki R

• University of Calcutta, India

Bibliografia

• [1] AHMED A. A., TRAORE I., Biometric Recognition Based on Free-Text Keystroke Dynamics, IEEE TRANSACTIONS ON CYBERNETICS, 2014, pp. 458-472.
• [2] RYBNIK M., PANASIUK P., SAEED K., ROGOWSKI M., Advances in the Keystroke Dynamics: The Practical Impact of Database Quality, IFIP International Federation for Information Processing, 2012, pp. 203-214.
• [3] SINGH S., ARYA DR. K. V. , Key Classification: A New Approach in Free Text Keystroke Authentication System, Circuits, Communication & System(PACCS), 2001, pp. 1-5.
• [4] MONROSE F., RUBIN A. D. , Keystroke dynamics as a biometric for authentication, Future Generation Computer Systems, 2000, pp. 351-359.
• [5] RYBNIK M., TABEDZKI M., SAEED K. , Keystroke Dynamics Based System for User Identification, 7th computer Information System & Management Applications, 2008, pp. 225-230.
• [6] An augmented computer user login authentication using classifying regions of keystroke density neural network. Patent No: PST-15418/36 40410sh
• [7] RYBNIK M., PANASIUK P., SAEED K., User Authentication with Keystroke Dynamics using Fixed Text”, International Conference on Biometric and Kansei Engineering, 2009, pp. 70-75.
• [8] ROBINSON J. A., LIANG V. M., CHAMBERS J. A. M., MACKENZIE C. L., Computer User Verification Using Login String Keystroke Dynamics, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICSPART A: SYSTEMS AND HUMANS, 1998, Vol. 28, No. 2, pp. 236-241.
• [9] SIM T., JANAKIRAMAN R., Are Digraphs Good for Free-Text Keystroke Dynamics?, Computer Vision and Pattern Recognition CVPR ’07, 2007 , pp. 1-6.
• [10] MR N. PAVADAY, SOYJAUDAH DR. K. M. S., Investigating performance of Neural Networks in authentication using keystroke dynamics, AFRICON, 2007, pp. 1-8.