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Comparing Structure Learning Algorithms of Bayesian Network in Authentication via Short Free Text

by Charoon Chantan, Sukree Sinthupinyo, Tippakorn Rungkasiri
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 46 - Number 3
Year of Publication: 2012
Authors: Charoon Chantan, Sukree Sinthupinyo, Tippakorn Rungkasiri
10.5120/6888-9204

Charoon Chantan, Sukree Sinthupinyo, Tippakorn Rungkasiri . Comparing Structure Learning Algorithms of Bayesian Network in Authentication via Short Free Text. International Journal of Computer Applications. 46, 3 ( May 2012), 19-24. DOI=10.5120/6888-9204

@article{ 10.5120/6888-9204,
author = { Charoon Chantan, Sukree Sinthupinyo, Tippakorn Rungkasiri },
title = { Comparing Structure Learning Algorithms of Bayesian Network in Authentication via Short Free Text },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 46 },
number = { 3 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 19-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume46/number3/6888-9204/ },
doi = { 10.5120/6888-9204 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:38:47.608242+05:30
%A Charoon Chantan
%A Sukree Sinthupinyo
%A Tippakorn Rungkasiri
%T Comparing Structure Learning Algorithms of Bayesian Network in Authentication via Short Free Text
%J International Journal of Computer Applications
%@ 0975-8887
%V 46
%N 3
%P 19-24
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we empirically evaluate effectiveness of structure learning of Bayesian Network when applying such networks to the domain of Keystroke Dynamics authentication. We compare four structure learning methods of Bayesian Network Classifier – Genetic, TAN, K2, and Hill Climbing algorithms, on our authentication model, namely Classify User via Short-text and IP Model (CUSIM). The results show that Genetic algorithm was best suited to our model. The findings from the study also indicate that the Accuracy, FAR, and FRR rate of Genetic algorithm are better than other algorithms tested in this work. Moreover, we found that TAN algorithm gives better results in some scenario than other algorithms.

References
  1. M. Pearce, S. Zeadally, and R. Hunt, "Assessing and improving authentication confidence management", Information Management & Computer Security, Vol. 8, No. 2, 2010, pp. 124-139.
  2. R. S. Gaines, W. Lisowski, S. J. Press, and N. Shapiro, "Authentication by Keystroke Timing: some Preliminary Results", Technical Report R-2526-NSF: Rand Corporation, 1980. .
  3. D. Gunetti, and C. Picardi, "Keystroke analysis of free text", ACM Trans. Inf. Syst. Secure, 2005, Vol. 8, No. 3, pp. 312–347.
  4. W. Roadrungwasinkul, and S. Sinthupinyo, "A Combination of Statistical Features and Neural Networks to Classify Users Based on Free Text", IRAST International Congress on Computer Applications and Computational Science (CACS 2010). , 2010. Information Management & Computer Security, Vol. 8, No. 2, 2010, pp. 124-139.
  5. A. Aldridge, M. White, and K. Forcht, "Security considerations of doing business via the Internet: cautions to be considered", Internet Research: Electronic Networking Applications and Policy, Vol. 7, No. 1, 1997, pp. 9-15.
  6. M. C. Alexandra, L. O. Arlindo, and F. S. Marie, " Efficient learning of Bayesian network classifiers: An extension to the TAN classifier", Advances in Artificial Intelligence Lecture Note in Computer Science, Vol. 4830, 2007, pp. 16-25.
  7. C. Chantan, S. Sinthupinyo, and T. Rungkasiri, "Improving Accuracy of Authentication Process via Short Free Text using Bayesian Network", IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 2, No. 3,2012, pp. 10-16.
  8. F. Monrose, and A. Rubin, "Authentication via keystroke dynamics", Proceedings of the 4th ACM conference on Computer and communications security, 1997, pp. 48–56.
  9. J. Hu, D. Gingrich, and A. Sentosa, "A k-Nearest Neighbor Approach for User Authentication through Biometric Keystroke Dynamics", Communications 2008 ICC '08 IEEE International Conference, 2008, pp. 1556-1560.
  10. J. Park, B. Ahnl, and H. Cho, "Intrusion Detection Using a PCB and IP address", Communications, Computer and Signal Processing, PacRim 2007 IEEE Pacific Rim Conference, 2007, pp. 223-226.
  11. J. Winterbottom, and C. Bryce, "The Internet Location Service", Intelligence in Next Generation Networks (ICIN), 14th International Conference, 2010, pp. 1-7.
  12. D. Jaros, and R. Kuchta, "New Location Based Authentication Techniques in the Access Management", Wireless and Mobile Communications (ICWMC) 6th International Conference, 2010, pp. 426 – 430.
  13. I. Ben-Gal, "Bayesian Networks", In: F. , Ruggeri. , F. Faltin, & R. Kenett (Eds. ), "Encyclopedia of Statistics in Quality and Reliability", John Wiley & Sons,2007.
  14. J. Pearl, "Probabilistic Reasoning in Intelligent Systems", Morgan Kaufmann, San Francisco. 1988.
  15. A. Cufoglu, M. Lohi, and K. Madani, "A Comparative Study of Selected Classifiers with Classification Accuracy in User Profiling", Computer Science and Information Engineering 2009 WRI World Congress, 2009, Vol. 3, pp. 708 – 712.
  16. L. Huijuan, C. Kejie, and L. Bai, "Bayesian Network Based Behavior Prediction Model for Intelligent Location Based Services", Industrial Electronics and Applications ICIEA 2007, 2nd IEEE Conference, 2007, pp. 703 – 708.
  17. A. Cufoglu, M. Lohi, and K. Madani, "Classification Accuracy performance of Naïve Bayesian(NB) Bayesian Networks(BN) Lazy Learning of Bayesian Rules (LBR) and Instance-Based Learner (IB1) comparative study", Computer Engineering & Systems ICCES 2008, International Conference, 2008, pp. 210 – 215.
  18. M. Bartlett, I. Bate, and J. Cussens, "Learning Bayesian Networks for Improved Instruction Cache Analysis", Machine Learning and Applications (ICMLA) Ninth International Conference, 2010, pp. 417 – 423.
  19. G. F. Cooper, and E. Herskovits,(1992). "A Bayesian Method for the induction of probabilistic networks from data". Machine Learning, 9,1992, pp. 309-347.
  20. L. Boaz, and M. Roy, " Investigation of the K2 algorithm in learning Bayesian network classifiers", Applied Artificial Intelligence, Vol. 25, No. 1, 2011, pp. 74-96.
  21. C. O. Hong, "Improving classification in Bayesian networks using structural learning", World Academy of Science, Engineering and Technology,Vol. 75,2011, pp. 1407-1411.
  22. M. Stuart, H. Yulan, and L. Kecheng, "Choosing the best Bayesian classifier: An empirical study", IAENG International Journal of Computer Science, Vol. 36, No. 4, 2009, pp. 322-331.
  23. G. M. Michael, "The performance of Bayesian network classifiers constructed using differebt techniques", in working Notes of the 14th European Conference on Machine Learning (ECML), 2003, pp. 59-70.
  24. J. Holland, "Adaptation in Natural and Artificial Systems", University of Michigan Press. 1975.
  25. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, The WEKA Data Mining Software: An Update; SIGKDD Explorations, Volume 11, Issue 1,2009.
  26. Website Ranking", at: http://www. stats. in. th/?cmd=stats_global&list=y, (accessed December 2011).
Index Terms

Computer Science
Information Sciences

Keywords

Authentication Classification Short Free Text Keystroke Dynamics Bayesian Network Internet Security.

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