Wireless Web security using a neural network-based cipher

Web Services Security and E-Business

Volumn , Issue , 2006, Pages 32-56

  Woungang, Isaac ^a   Sadeghian, Alireza ^a   Wu, Shuwei ^a,c   Misra, Sudip ^b   Arvandi, Maryam ^a  

^a RYERSON UNIVERSITY   (Canada)

^b Department of Obstetrics Gynecology   (United States)

^c Ministry of Community and Social Services   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 77956597680     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-59904-168-1.ch002     Document Type: Chapter

References (27)

1. st EEE International Conference on Neural Networks (Vol. 2, pp. 105-110).
2. Arvandi, M. (2005). Analysis of neural network based ciphers. MASc thesis. Ryerson University, Department of Electrical and Computer Engineering, Toronto, Canada.
3. Benitez J., Castro J. L., & Requena, I. (1997). Are artificial neural networks black boxes? IEEE Transactions on Neural Networks, 8(5), 1156-1164.
4. Cantú-Paz, E. (2003). Pruning neural networks with distribution estimation algorithms. In E. Cantú Paz et al. (Eds.), Proceedings of Genetic and Evolutionary Computation Conference, GECCO-2003(pp. 790-800). July 7-16, Chicago, IL.
5. Coyle, F. P. (2001). Wireless Web: A manager's guide. NJ: Addison-Wesley.
6. st ed.). Boston: Addison-Wesley.
7. nd ed.). India: Prentice Hall of India.
8. th Annual Joint Conference of the IEEE Computer and Communications Societies(Vol. 3, pp. 1546-1555).
9. IPSec. (2004). The IPSec architecture. Retrieved August 18, 2006, from http://www.securitydocs.com/library/2926
10. Karras, D. A., & Zorkadis, V. (2003). On neural network techniques in the secure management of communication systems through improving and quality assessing pseudorandom stream generators.Neural Networks, 16(5-6), 899-905.
11. Kent, S., & Atkinson, R. (1998). RFC 2401: Security architecture for the Internet Protocol. Obsoletes RFC1825. Status: PROPOSED STANDARD.
12. th International Conference on Neural Information Processing (ICONIP'02) (Vol. 3, pp. 1351-1354).
13. Klimov, A., Mityaguine, A., & Shamir, A. (2002). Analysis of neural cryptography. In Proceedings of the AsiaCrypt 2002 (pp. 288-298). Springer Verlag.
14. Meletiou, G. C., Tasoulis, D. K., & Vrahatis, M.N. (2002). A first study of the neural network approach in the RSA cryptosystem. In Proc. 7th IASTED International Conference Artificial Intelligence and Soft Computing, July 17-19, 2002, Banff, Canada.
15. Meletiou, G. C., Tasoulis, D. K., & Vrahatis, M. N. (2003). Cryptography through interpolation and computational intelligence methods. Bulletin of the Greek Mathematical Society, Athens, Greece.
16. Pointcheval, D. (1994). Neural networks and their cryptographic applications. Eurocode '94, Pascale Charpin Ed., INRIA.
17. Reed, R. (1993). Pruning algorithms: A survey. IEEE Trans on Neural Networks, 4(5), 740-747.
18. Rosen, M., Kanter, I., & Kinzel, W. (2002). Cryptography based on neural networks: Analytical results. cond-mat/0202350.
19. nd ed.). John Wiley & Sons.
20. Scott, S., Alvin, L., & Jui-Cheng, Y. (2000). Design and realization of a new chaotic neural encryption/decryption network. In Proceedings of the IEEE Asia-Pacific Conference on Circuits and Systems (pp. 335-338).
21. Sereno, M. I. & Sereno, M. E. (1991). Learning to see rotation and dilation with a Hebb rule. In R. P. Lippmann, J. Moody, & D. S. Touretzky (Eds.), Advances in Neutral Informal Processing Systems(Vol. I). San Mateo, CA: Morgan Kaufmann Publishers.
22. rd ed.). Prentice Hall.
23. Stubblefield, A., Ioannidis, J., & Rubin, A. D. (August, 2001). Using the Fluhrer, Mantin, and Shamir attack to break WEP, Technical Report TD04ZCPZZ. NJ: AT & T Labs.
24. Su, S., Lin, A., & Yen, J. (2000). Design and realization of a new chaotic neural encryption/decryption network. In IEEE Asia-Pacific Conference on Circuits and Systems (pp. 335-338).
25. Townley, S., Iichmann, A., Weib, M. G., Mcclements, W., Ruiz, A. C., Owens, D. H., & Pratzel-Wolters, D. (2000). Existence and learning of oscillations in recurrent neural networks. IEEE Trans on Neural Networks, 11(1), 205-214.
26. Williams, R. J., & Zipser, D. (1989). A learning algorithm for continually running fully recurrent neural networks.Neural Computation, 1, 270-280.
27. Yee, L., & De Silva, C. (2002). Application of multilayer perceptron networks in public key cryptography. Proceedings of the 2002 International Joint Conference on Neural Networks (Vol. 2, pp. 1439-1443).