Typing Pattern Recognition Using Keystroke Dynamics
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Biometric authentication is individual characteristics that cannot be used by imposter to penetrate secure system. Keystroke dynamics based authentication verifies user from their typing pattern. To authenticate user based on their typing samples, it is required to find out he resemblance of a typing samples of user regardless of the text typed. Key event timing is extracted from key features Latency, Dwell time, Key interval, Up to up, Flight time and standard are measure in the form of FAR, FRR and ER. In this paper we introduces a k-nearest neighbor approach to classify users’ keystroke dynamics profiles. For authentication, an input will be checked against the profiles within the cluster which has significantly reduced the verification load.
KeywordsBiometric Keystroke dynamics Identification Verification
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- 1.Moskovitch, R., Feher, C., Messerman, A., Kirschnick, N., Mustafić, T., Camtepe, A., Lohlein, B., Heister, U., Moller, S., Rokach, L., Elovici, Y.: Identity Theft, Computers and Behavioral Biometrics. In: ISI 2009, Richardson, TX, USA, June 8-11 (2009)Google Scholar
- 2.Monrose, F., Rubin, A.D.: Keystroke Dynamics as a Biometric for Authentication. Courant Institute of Mathematical Science, New York (March 1999)Google Scholar
- 3.Revett, K.: A Bioinformatics Based Approach to Behavioral Biometrics. University of Westminster, Harrow School of Computer Science London, England HAI 3Tp (2007)Google Scholar
- 4.Shimshon, T., Moskovitch, R., Rokach, L., Elovici, Y.: Clustering Di-Graphs for Continuously Verify Users according to their Typing Patterns. Department of Information Systems Engineering, Ben Gurion University, Beer Sheva, Israel. 2 Deutsche Telekom Laboratories at Ben, Gurion University, IEEE (2010)Google Scholar
- 5.Rybnik, M., Tabedzki, M., Saeed, K.: A Keystroke Dynamics Based System for User Identification. University of Finance and Management Ciepla 40, 15-472, Bialystok, Poland, IEEE (2008)Google Scholar
- 6.Hocquet, S., Ramel, J.-Y., Cardot, H.: Fusion of Methods for Keystroke Dynamic Authentication. Universite Francois-Rabelais de Tours Laboratire d’ Informatique (EA, 64 Avenue Jean Portalis 37200 TOURS, France (2010))Google Scholar
- 7.Karnan, M., Akila, M.: Identity Authentication Based on Keystroke Dynamics Using Genetic Algorith and Particle Swarm Optimization. ,Dept. of Computer Science and Engineering, Tamilnadu College of Engineering, Coimbatore, India, Research Scholar, Dept. of Computer Science and Engineering, Anna University Coimbatore, India, IEEE (2009)Google Scholar
- 8.Karnan, M.: Personal Authentication Based on Keystroke Dynamics using Soft Computing Techniques. Dept. of Computer Science and Engineering, Tamilnadu College of Engineering, Coimbatore, India, IEEE (2010)Google Scholar
- 9.Crawford, H.: Keystroke Dynamics: Characteristics and Opportunities. Department of computer Science Sir Alwyn Wiliams Building, University of Glasgow, United Kingdom, IEEE (2010)Google Scholar
- 10.Pedernera, G.Z., Sznur, S., Ovando, G.S., García, S., Meschino, G.: School of Engineering FASTA University, Mar del Plata, Argentina: Revisiting clustering methods to their application on keystroke dynamics for intruder classification, IEEE (2010)Google Scholar