Proposing Model for Recognizing User Position

In the progressive world of Internet, online banking or any other transaction is not secure because of methods such as brute force technique, dictionary attacks, and spoofing. It is because traditional methods of authentication require either password or pin which is not secure because it can be cracked easily using the methods mentioned above. Therefore, biometric authentication can be a viable option which can be used for identification of a genuine user. In this paper, we propose a model for identifying the user’s position that plays a vital role in identifying the genuinity of any user. This paper revolves around the effects on verification of genuine user if the data collected from user is done in both controlled and uncontrolled environment.

[1]  Gopal K. Gupta,et al.  Identity authentication based on keystroke latencies , 1990, Commun. ACM.

[2]  M. Akila,et al.  Biometric personal authentication using keystroke dynamics: A review , 2011, Appl. Soft Comput..

[3]  Ravi Das An Introduction to Biometrics , 2014 .

[4]  Khalid Saeed,et al.  A New Approach for Hand-Palm Recognition , 2005, Enhanced Methods in Computer Security, Biometric and Artificial Intelligence Systems.

[5]  Kenneth Revett A Bioinformatics Based Approach to Behavioural Biometrics , 2007, 2007 Frontiers in the Convergence of Bioscience and Information Technologies.

[6]  Klaas Apostol Brute-force Attack , 2012 .

[7]  Rama Chellappa,et al.  Discriminant analysis of principal components for face recognition , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[8]  Anil K. Jain,et al.  Handbook of Fingerprint Recognition , 2005, Springer Professional Computing.

[9]  Mohammad S. Obaidat,et al.  Verification of computer users using keystroke dynamics , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[10]  Fabian Monrose,et al.  Keystroke dynamics as a biometric for authentication , 2000, Future Gener. Comput. Syst..

[11]  Norman Shapiro,et al.  Authentication by Keystroke Timing: Some Preliminary Results , 1980 .

[12]  John Daugman,et al.  How iris recognition works , 2002, IEEE Transactions on Circuits and Systems for Video Technology.

[13]  Rama Chellappa,et al.  Discriminant analysis of principal components for face recognition , 1998 .