Abstract In this paper, a new light weight highly secured c iphering engine creation methodology we called On D emand Ciphering Engine (ODCE) was suggested. The main fea ture of this method is that both, the ciphering engine and the secret key, are kept secrets and created by the user or th e system administrator when initiating his transmis sion, then the involved sides exchange these secrets. The design methodolog y and structure of this system was described and prototyped using Artificial Neural Network (ANN) as the main buildin g block in the system. Software and hardware implem entations of the suggested system were performed to prove its practi cability. Also, different experimental tests were achieved to determine the impact of the suggested method on both network dela y and system performance.  

References

[1] Alallayah K., Amin M., Abd W. and Alhamami A., Applying Neural Networks for Simplified Data Encryption Standard (SDES) Cipher System Cryptanalysis, the International Arab Journal of Information Technology , vol. 9, no. 2, pp. 1630 169, 2012.

[2] Al0Omari K. and Sumari P., Spiking Neurons with ASNN Based Methods for the Neural Block Cipher, International Journal of Computer Science and Information Technology , vol. 2, no. 4, pp. 1380148, 2010.

[3] Anitha P., Raj M., and Sivan S., An Efficient Neural Network Based Algorithm for Detecting Content in Corporate Mails: A Web Based Steganalysis, International Journal of Computer Science Issues , vol. 9, no. 3, pp. 5090513, 2012.

[4] Bartlett P., The Sample Complexity of Pattern Classification with Neural Networks: The Size of the Weights is More Important than the Size of the Network, IEEE Transactions on Information Theory , vol. 44, no. 2, pp. 5250536, 1998.

[5] Cole E., Krutz R., and Conley J., Network Security Bible , Wiley Publishing Inc., 2005.

[6] Cybenko G., Approximation by Superpositions of a Sigmoidal Function, Mathematics of Control, Signals and Systems , vol. 2, no. 4, pp. 3030314, 1989.

[7] Dalkiran I. and Danisman K., Artificial Neural Network Based Chaotic Generator for Cryptology, SOURCE Turkish Journal of Electrical Engineering and Computer Sciences , vol. 18, no. 2, pp. 225, 2010.

[8] Fukushima K., Neocognitron: A Hierarchical Neural Network Capable of Visual Pattern Recognition, Neural Networks , vol. 1, no. 2, pp. 1190130, 1988.

[9] Gomathi P. and Nasira G., A Survey on Biometrics Based Key Authentication Using Neural Network, Global Journal of Computer Science and Technology , vol. 11, no. 11, pp. 103, 2011.

[10] Gomi H. and Kawato M., Neural Network Control For A Closed0Loop System Using Feedback0Error0Learning, Neural Networks, vol. 6, no. 7, pp. 9330946,1993.

[11] Hetcht0Nielsen R., Theory of the Backpropagation Neural Networks, in proceeding of International Joint Conference on Neural Networks , Washington, pp . 5930605, 1989.

[12] Kanter I., Kinzel W., Kanter E., Secure Exchange of Information by Synchronization of Neural Networks, available at: http://arxiv.org/pdf/cond0mat/0202112.pdf, last visited 2002.

[13] Kinzel W. and Kanter I., Interacting Neural Networks and Cryptography, Advances in Solid State Physics 42 , pp. 383, 2002.

[14] Kulkami V., Shaheen S. and Apte S., Hash Function Implementation Using Artificial Neural Network, the International Journal on Soft Computing , vol. 1, no. 1, pp. 108, 2010.

[15] Narendra K. and Parthasarathy K., Identification and Control of Dynamical Systems Using Neural Networks, IEEE Transactions on Neural Networks , vol. 1, no. 1, pp. 4027, 1990.

[16] Long H., Stream Cipher Method Based on Neural Network, in Proceedings of National Conference on Information Technology and Computer Science Conference , pp. 104, 2012.

[17] Masci J., Meier U., CireRan D., and Schmidhuber J., Stacked Convolutional Auto0Encoders for Hierarchical Feature Extraction, available at: http://people.idsia.ch/~ciresan/data/icann2011.pd f, last visited 2011.

[18] Mohamed M., Multi0Service Cryptography Scheme for Secure Data Communication, International Journal of Computer Science and Network Security , vol. 11, no. 7, pp. 1480153, 2011.

[19] Pandey S. and Mishra M., Neural Cryptanalysis of Block Cipher, International Journal of Advanced Research in Computer Science and Software Engineering, vol. 2, no. 5, pp. 50052, 2012.

[20] Prabakaran N., Loganathan P., and Vivekanandan P., Neural Cryptography with Multiple Transfers Functions and Multiple 470 The International Arab Journal of Information Techn ology, Vol. 13, No. 4, July 2016 Learning Rule, International Journal of Soft Computing , vol. 3, no. 3, pp. 1770181, 2008.

[21] Schuessler O. and Loyola D., Parallel Training of Artificial Neural Networks Using Multithreaded and Multicore CPUs, in Proceedings of the 10 th International Conference, ICANNGA 2011 , Ljubljana, Slovenia, pp. 70079 2011.

[22] Shihab K., A Backpropagation Neural Network for Computer Network Security, Journal of Computer Science , vol. 2, no. 9, pp. 7010, 2006.

[23] Shukla N. and Tiwari A., An Empirical Investigation of Using ANN Based N0State Sequential Machine and Chaotic Neural Network in the Field of Cryptography, Global Journal of Computer Science and Technology , vol. 12, no. 10, pp. 1011, 2012.

[24] Sierra0Canto X., Madera0Ram rez F., and Uc0 Cetina V., Parallel Training of a Back0 Propagation Neural Network using CUDA, in Proceedings of the 9 th International Conference on Machine Learning and Applications , Washington, pp. 3070312 2010.

[25] Sumangala G., Kulkarni V., Sali S., and Apte S., Performance Analysis of SHA2 Algorithm with and without Using Artificial Neural Networks, World Journal of Science and Technology , vol. 1, no. 12, pp. 1012, 2011.

[26] Tanenbaum A., Computer Networks , Prentice0 Hall Publishing, 2006.

[27] Tonkes B., Blair A., Wiles J., A Paradox of Neural Encoders and Decoders or Why Don t We Talk Backwards?, in Proceedings of the 2 nd Asia7Pacific Conference on Simulated Evolution and Learning, SEAL 98 Canberra , Australia, pp. 3570364, 1999.

[28] Tsai H., Cheng J., and Yu P., Audio Watermarking Based on HAS and Neural Networks in DCT, available at: http://cse.hcmut.edu.vn/~minhnguyen/NET/Com puter%20Networks%200%20A%20Tanenbaum% 200%205th%20edition.pdf, last visited 2003.

[29] Waibel lexander., Hanazawa A., Hinton T., Geoffery E., Kiyohiro S., and Kevin L., Phoneme Recognition using Time0Delay Neural Networks, IEEE Transactions on Acoustics, Speech and Signal Processing , vol. 37, no. 3, pp. 3280339, 1989.

[30] Yu W. and Cao J., Cryptography based on Delayed Chaotic Neural Networks, Physical Letters Journal , vol. 356, no. 405, pp. 3330338 2006. Qutaiba Ali received BSC and MSC in Electrical Engineering in 1996 and 1999. He obtained PHD in computer Engineering 1n 2006. Since 2000, he joined Mosul University/Iraq as a faculty member and still there. His research interests include: Network simulation and modeling, real time and embedded systems. He published 4 international books and mor e than 56 papers (some of them are in ISI indexed journals) in his fields of interest. He participate d (as TPC) in more than 40 IEEE International conferences and joined the editorial board of more than 15 international journals (IEEE, IET and Elsevier Journals). Shefa Dawwd received the BSc degree in Electronic and Communication Engineering, the MSc and the PhD degree in Computer Engineering in 1991, 2000, and 2006, respectively. He is presently a faculty member (Associated Professor) in the computer engineering department/University of Mosul. His main research interests include image and signal processing and t heir hardware models, parallel computer architecture, hardware implementation and GPU based systems. He has authored more than 27 research papers. He has been an editorial member of several national and international journals.