Keystroke Authentication on Enhanced Needleman Alignment Algorithm

Seham Bamatraf, Mohamed Bamatraf, Osman Hegazy

Department of Computer Science, Faculty of Science, Hadhramout University, Yemen.
Department of Information System, Faculty of Information and Computers, Cairo University, Giza, Egypt.

DOI: 10.4236/iim.2014.64021                

Abstract

An important point for computer systems is the identification of users for authentication. One of these identification methods is keystroke dynamics. The keystroke dynamics is a biometric measurement in terms of keystroke press duration and keystroke latency. However, several problems are arisen like the similarity between users and identification accuracy. In this paper, we propose innovative model that can help to solve the problem of similar user by classifying user’s data based on a membership function. Next, we employ sequence alignment as a way of pattern discovery from the user’s typing behaviour. Experiments were conducted to evaluate accuracy of the proposed model. The results show high performance compared to standard classifiers in terms of accuracy and precision.

Keywords

Keystroke Dynamics, Authentication, Fuzzy Logic, Sequence Alignment, Classification

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Revett, K. (2007) A Bioinformatics Based Approach to Behavioural Biometrics. Proceedings of the 2007 Frontiers in the Convergence of Bioscience and Information Technologies, Jeju City, 11-13 October 2007, 665-670. http://dx.doi.org/10.1109/FBIT.2007.143
[2]	Joyce, R. and Gupta, G.K. (1990) Identity Authentication Based on Keystroke Latencies. Communications of the ACM, 33, 168-176. http://dx.doi.org/10.1145/75577.75582
[3]	Chaudhari, R.D., Pawar, A.A. and Deore, R.S. (2013) The Historical Development of Biometric Authentication Techniques: A Recent Overview. International Journal of Engineering Research & Technology (IJERT), 2, 3921-3928.
[4]	Nanavati, S., Thieme, M. and Nanavati, R. (2002) Biometrics: Identity Verification in a Networked World. John Wiley & Sons.
[5]	Voth, D. (2003) Face Recogniition Technology. IEEE Intelligent Systems, 3, 4-7.
[6]	Shu, W. and Zhang, D. (1998) Automated Personal Identification by Palmprint. Optical Engineering, 37, 2359-2362. http://dx.doi.org/10.1117/1.601756
[7]	Shaughnessy, D.O. (1986) Speaker Recognition. IEEE ASSP Magazine, 3, 4-17. http://dx.doi.org/10.1109/MASSP.1986.1165388
[8]	Tappert, C. (1984) Adaptive On-Line Handwriting Recognition. Proceedings of Seventh International Conference on Pattern Recognition, Montreal, 30 July-2 August 1984, 1004-1007.
[9]	Herbst, N.M. and Liu, C.N. (1977) Automatic Signature Verification Based on Accelerometry. IBM Journal of Research and Development, 21, 245-253. http://dx.doi.org/10.1147/rd.213.0245
[10]	Lin, D.-T. (1997) Computer-Access Authentication with Neural Network Based Keystroke Identity Verification. Proceedings of the International Conference on Neural Networks, Houston, 9-12 June 1997, 174-178.
[11]	Kumar, P. and Sahoo, G. (2013) Survey On Bioinformatics And Computational Biology. International Journal of Engineering Research & Technology (IJERT), 2, 108-114.
[12]	Dayhoff, M.O., Schwartz, R.M. and Orcutt, B.C. (1978) A Model of Evolutionary Change in Proteins. Atlas of Protein Sequence and Structure, 5, 345-351.
[13]	Henikoff, S. and Henikoff, J.G. (1992) Amino Acid Substitution Matrices from Protein Blocks. Proceeding of the National Academy of Sciences of the United States of America, 89, 10915-10919.
[14]	Needleman, S.B. and Wunsch, C.D. (1970) A General Method Applicable to the Search for Similarities in the Amino Acid Sequence of Two Proteins. Journal of Molecular Biology, 48, 443-453. http://dx.doi.org/10.1016/0022-2836(70)90057-4
[15]	Eger, S. (2013) Sequence Alignment with Arbitrary Steps and Further Generalizations, with Applications to Alignments in Linguistics. Information Sciences, 237, 287-304. http://dx.doi.org/10.1016/j.ins.2013.02.031
[16]	Wangsuk, K. and Anusas-amornkul, T. (2013) Trajectory Mining for Keystroke Dynamics Authentication. Procedia Computer Science, 24, 175-183. http://dx.doi.org/10.1016/j.procs.2013.10.041
[17]	Stefan, D., Shu, X. and Yao, D. (2012) Robustness of Keystroke-Dynamics Based Biometrics against Synthetic Forgeries. Computers & Security, 31, 109-121.
[18]	Alpar, O. (2014) Keystroke Recognition in User Authentication Using ANN Based RGB Histogram Technique. Engineering Applications of Artificial Intelligence, 32, 213-217.
[19]	Smith, T.F. and Waterman, M.S. (1981) Identification of Common Molecular Subsequences. Journal of Molecular Biology, 147, 195-197. http://dx.doi.org/10.1016/0022-2836(81)90087-5
[20]	Higgins, D.G. and Sharp, P.M. (1988) CLUSTAL: A Package for Performing Multiple Sequence Alignment on a Microcomputer. Gene, 73, 237-244. http://dx.doi.org/10.1016/0378-1119(88)90330-7
[21]	Dayhoff, M.O. and Foundation, N.B.R. (1979) Atlas of Protein Sequence and Structure. National Biomedical Research Foundation.
[22]	Karnan, M. and Krishnaraj, N. (2010) BioPassword—Keystroke Dynamic Approach to Secure Mobile Devices. IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Coimbatore, 28-29 December 2010, 1-4.
[23]	Gaines, R.S. (1980) Authentication by Keystroke Timing: Some Preliminary Results. Rand, Santa Monica.
[24]	Young, J.R. and Hammon, R.W. (1989) Method and Apparatus for Verifying an Individual’s Identity. United States Patent.
[25]	Hu, J., Gingrich, D. and Sentosa, A. (2008) A k-Nearest Neighbor Approach for User Authentication through Biometric Keystroke Dynamics. Proceedings of IEEE International Conference on Communications, Beijing, 19-23 May 2008, 1556-1560.
[26]	Rodrigues, R.N., Yared, G.F.G., do N. Costa, C.R., Yabu-Uti, J.B.T., Violaro, F. and Ling, L.L. (2006) Biometric Access Control through Numerical Keyboards Based on Keystroke Dynamics. Proceedings of the 2006 International Conference on Advances in Biometrics, Hong Kong, 5-7 January 2006, 640-646.
[27]	Bleha, S.A., Knopp, J. and Obaidat, M.S. (1992) Performance of the Perceptron Algorithm for the Classification of Computer Users. Presented at the Proceedings of the 1992 ACM/SIGAPP Symposium on Applied Computing: Technological Challenges of the 1990’s, Kansas City, 1992.
[28]	Bleha, S.A. and Obaidat, M.S. (1993) Computer Users Verification Using the Perceptron Algorithm. IEEE Transactions on Systems, Man, and Cybernetics, 23, 900-902. http://dx.doi.org/10.1109/21.256563
[29]	Bleha, S., Slivinsky, C. and Hussien, B. (1990) Computer-Access Security Systems Using Keystroke Dynamics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12, 1217-1222. http://dx.doi.org/10.1109/34.62613
[30]	Bleha, S.A. and Obaidat, M.S. (1991) Dimensionality Reduction and Feature Extraction Applications in Identifying Computer Users. IEEE Transactions on Systems, Man and Cybernetics, 21, 452-456. http://dx.doi.org/10.1109/21.87093
[31]	Obaidat, M.S. (1995) A Verification Methodology for Computer Systems Users. Proceedings of the 1995 ACM Symposium on Applied Computing, Nashville, 26-28 February 1995, 258-262. http://dx.doi.org/10.1145/315891.315976
[32]	Haider, S., Abbas, A. and Zaidi, A.K. (2000) A Multi-Technique Approach for User Identification through Keystroke Dynamics. IEEE International Conference on Systems, Man, and Cybernetics, 2, 1336-1341.
[33]	Gutiérrez, F.J., Lerma-Rascón, M.M., Salgado-Garza, L.R. and Cantú, F.J. (2002) Biometrics and Data Mining: Comparison of Data Mining-Based Keystroke Dynamics Methods for Identity Verification. Proceedings of the 2nd Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence, Yucatán, 22-26 April 2002, 460-469.
[34]	Krause, M. and Tipton, H.F. (2011) Handbook of Information Security Management. Vol. 5, CRC Press LLC, Boca Raton.
[35]	Eisner, R., Poulin, B., Szafron, D., Lu, P. and Greiner, R. (2005) Improving Protein Function Prediction Using the Hierarchical Structure of the Gene Ontology. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, San Diego, 14-15 November 2005, 1-10.
[36]	Lu, Z., Szafron, D., Greiner, R., Lu, P., Wishart, D.S., Poulin, B., Anvik, J., Macdonell, C. and Eisner, R. (2004) Predicting Subcellular Localization of Proteins Using Machine-Learned Classifiers. Bioinformatics, 20, 547-556. http://dx.doi.org/10.1093/bioinformatics/btg447
[37]	Killourhy, K. and Maxion, R. (2009) Keystroke Dynamics-Benchmark Data Set. www.cs.cmu.edu/~keystroke
[38]	Holmes, G., Donkin, A. and Witten, I.H. (1994) WEKA: A Machine Learning Workbench. Proceedings of the 1994 2nd Australian and New Zealand Conference on Intelligent Information Systems, Brisbane, 29 November-2 December 1994.