Development of Committee Neural Network for Computer Access Security System

  • A. Sermet Anagun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3982)


A computer access security system, a reliable way of preventing unauthorized people for accessing, changing or deleting, and stealing the information, needed to be developed and implemented. In the present study, a neural network based system is proposed for computer access security for the issues of preventive security and detection of violation. Two types of data, time intervals between successive keystrokes during password entry through keyboard and voice patterns spoken via a microphone, are considered to deal with a situation of multiple users where each user has a certain password with different length. For each type of data, several multi-layered neural networks are designed and evaluated in terms of recognition accuracy. A committee neural network is formed consisting of six multi-layered neural networks. The committee decision was based on majority voting of the member networks. The committee neural network performance was better than the neural networks trained separately.


Recognition Accuracy Speaker Verification Multilayered Neural Network Authorized Person Security Code 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Anagun, A.S.: An Artificial Neural Network Approach for a Computer Access Security System Based on the Characteristics of the Users. Endüstri Mühendisliği 10, 3–11 (1999)Google Scholar
  2. 2.
    Hussein, B.R., McLaren, R., Bleha, S.A.: An Application of Fuzzy Algorithms in a Computer Access Security System. Pattern Recognition Letters 9, 39–43 (1989)CrossRefGoogle Scholar
  3. 3.
    Bleha, S.A., Slivinsky, C., Hussein, B.: Computer-Access Security Systems Using Keystroke Dynamics. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 1217–1222 (1990)CrossRefGoogle Scholar
  4. 4.
    Bleha, S.A., Obaidat, M.S.: Dimensionality Reduction and Feature Extraction Application In Identifying Computer Users. IEEE Transactions on Systems, Man, and Cybernetics 21, 452–456 (1991)CrossRefGoogle Scholar
  5. 5.
    Obaidat, M.S., Macchairolo, D.T., Bleha, S.A.: An Intelligent Neural Network System for Identifying Computer Users. In: Dagli, et al. (eds.) ASME Intelligent Engineering Systems through Artificial Neural Networks, vol. 1, pp. 953–959 (1991)Google Scholar
  6. 6.
    Bleha, S.A., Obaidat, M.S.: Computer Users Verification Using the Perceptron Algorithm. IEEE Transactions on Systems, Man, and Cybernetics 23, 900–902 (1993)CrossRefGoogle Scholar
  7. 7.
    Obaidat, M.S.,, Macchairolo, D.T.: An On-Line Neural Network System For Computer Access Security. IEEE Transactions on Industrial Electronics 40, 235–242 (1993)CrossRefGoogle Scholar
  8. 8.
    Anagun, A.S., Cin, I.: An Alternative Way for Computer Access Security: Password Entry Patterns. In: Proceedings of the 18th National Conference on Operations Research and Industrial Engineering, Istanbul, Turkey, pp. 17–20 (1996)Google Scholar
  9. 9.
    Anagun, A.S., Cin, I.: A Neural Network Based Computer Access Security System for Multiple Users. Computers and Industrial Engineering 35, 351–354 (1998)CrossRefGoogle Scholar
  10. 10.
    Anagun, A.S.: Designing a Neural Network Based Computer Access Security System: Keystroke Dynamics and/or Voice Patterns. International Journal of Smart Engineering Design 4, 125–132 (2002)CrossRefGoogle Scholar
  11. 11.
    Markel, J.D., Gray Jr., A.H.: Linear Prediction of Speech. Springer, New York (1982)Google Scholar
  12. 12.
    Deller Jr., J.R., Proakis, J.G., Hansen, J.H.L.: Discrete-Time Processing of Speech Signals. Macmillian Publishing Co., New York (1993)Google Scholar
  13. 13.
    Rabiner, L.R., Schafer, R.W.: Digital Processing of Speech Signals. Prentice-Hall, Englewood Cliffs (1978)Google Scholar
  14. 14.
    Burr, D.J.: Experiments on Neural Net Recognition of Spoken and Written Text. IEEE Transactions on Acoustics, Speech, and Signal Processing 36, 1162–1168 (1988)zbMATHCrossRefGoogle Scholar
  15. 15.
    Huang, W., Lippmann, R.: Comparisons between Neural Networks and Conventional Classifiers. In: Proceedings of the 1st International Conference on Neural Networks, pp. 485–494 (1987)Google Scholar
  16. 16.
    Pao, Y.H.: Adaptive Pattern Recognition and Neural Networks. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  17. 17.
    Freeman, J.A., Skapura, D.M.: Neural Networks: Algorithms, Applications, and Programming Techniques. Addison-Wesley Publishing Co., Reading (1991)zbMATHGoogle Scholar
  18. 18.
    Soucek, B.: Neural and Concurrent Real-Time Systems - The Sixth Generation. John Wiley-Sons, New York (1989)Google Scholar
  19. 19.
    Anagun, A.S.: A Multilayered Neural Network Based Computer Access Security System: Effects of Training Algorithms. Lecture Series on Computer and Computational Sciences 4B, 1604–1607 (2005)Google Scholar
  20. 20.
    Klimasauskas, C.C.: Applying Neural Networks, Part III: Training a Neural Network. PC AI, 20–24 (1991)Google Scholar
  21. 21.
    Reddy, N.P., Buch, O.A.: Speaker Verification Using Committee Neural Networks. Computer Methods and Programs in Biomedicine 72, 109–115 (2003)CrossRefGoogle Scholar
  22. 22.
    Jerebko, A.K., Malley, J.D., Franaszek, M., Summers, R.M.: Multiple Neural Network Classification Scheme for Detection of Colonic Polyps in CT Colonography Data Set. Academic Radiology 10, 254–260 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • A. Sermet Anagun
    • 1
  1. 1.Industrial Engineering DepartmentEskişehir Osmangazi UniversityBademlikTurkey

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