About Communications       Author's Guide       Reviewers       Editorial Members       Archive
Volume 8
Volume 7
Volume 6
Volume 5
Volume 4
Volume 3
Volume 2
Volume 1
AASCIT Communications | Volume 1, Issue 2 | Jul. 18, 2014 online | Page:27-32
A Novel Approach to Synchronization Problem of Artificial Neural Network in Cryptography
It is becoming increasingly difficult to have data security nowadays. There have been used various cryptography methods in literature, but recent developments in computational area have heightened the need of new methods. In this study the feed-forward artificial neural network (FFNN) was used with a different perspective by using the structure of artificial neural network as a key as a solution of synchronization problem of FFNNs in cryptography. The proposed method was employed for text, audio and image data and the results were found acceptable. Also, the results of FFNN were assessed with the results of probabilistic, radial basis artificial neural networks and k nearest neighbor and wavelet transforms. The FFNN was faster than these methods and had 100% decryption accuracy.
Ömer Faruk Ertuğrul, Department of Electrical and Electronics Engineering, Batman University, 72060, Batman, Türkiye.
Cryptography, Synchronization, Artificial Neural Network
Agrawal, H., & Sharma, M. A Review of Text Encryption Techniques. Asian Journal Of Computer Science & Information Technology. 4(5): 47-54 (2014)
Kinzel, Wolfgang, and Ido Kanter. "Neural cryptography." arXiv preprint cond-mat/0208453 (2002).
R. Mislovaty, E. Klein, I. Kanter ve W. Kinzel, "Security of neural cryptography." sign (hi) 1 (2004): 1.
Yee, Liew Pol, and Liyanage C. De Silva. "Application of multilayer perceptron networks in public key cryptography." Neural Networks, 2002. IJCNN'02. Proceedings of the 2002 International Joint Conference on. Vol. 2. IEEE, 2002.
Sağıroğlu, Şeref, and Necla Özkaya. "Neural Solutions for Information Security." Gazi Üniversitesi Politeknik Dergisi 10.1 (2007).
Zhou Kaili, Kang Yaohong, Huang Yan ve Feng Erli. "Encrypting Algorithm Based on RBF Neural Network."Natural Computation, 2007. ICNC 2007. Third International Conference on. Vol. 1. IEEE, 2007.
Arvandi, M., S. Wu, and A. Sadeghian. "On the use of recurrent neural networks to design symmetric ciphers." Computational Intelligence Magazine, IEEE 3.2 (2008): 42-53.
Su, Scott, Alvin Lin, and Jui-Cheng Yen. "Design and realization of a new chaotic neural encryption/decryption network." Circuits and Systems, 2000. IEEE APCCAS 2000. The 2000 IEEE Asia-Pacific Conference on. IEEE, 2000.
Zhang, Yunpeng, et al. "The Improvement of Public Key Cryptography Based on Chaotic Neural Networks." 2008 Eighth International Conference on Intelligent Systems Design and Applications. Vol. 3. 2008.
Dalkiran, Ilker, and Kenan Danisman. "Artificial neural network based chaotic generator for cryptology." Turkish Journal of Electrical Engineering and Computer Sciences 18.2: 225-240, 2010.
Liu, Niansheng, and Donghui Guo. "Security analysis of public-key encryption scheme based on neural networks and its implementing." Computational Intelligence and Security. Springer Berlin Heidelberg, 2007. 443-450.
Liu, Niansheng, and Donghui Guo. "Security analysis of public-key encryption scheme based on neural networks and its implementing." Computational Intelligence and Security. Springer Berlin Heidelberg, 2007. 443-450.
Meghdad Ashtiyani, Soroor Behbahani, Saeed Asadi ve Parmida Moradi Birgani, “Transmitting Encrypted Data by Wavelet Transform and Neural Network, 2007 IEEE International Symposium on Signal Processing and Information Technology, IEEE, Pages:385-389, 2007.
Godhavari, T., N. R. Alamelu, and R. Soundararajan. "Cryptography using neural network." INDICON, 2005 Annual IEEE. IEEE, 2005.
Kanter, Ido, and Wolfgang Kinzel. "The Theory of Neural Networks and Cryptography." Quantum Computers and Computing 5.1 (2005): 130-140.
Volna, E., Kotyrba, M., Kocian, V., & Janosek, M. Cryptography Based On Neural Network. In Proceedings 26th European Conference on Modelling and Simulation. 386-391 (2012).
Rosen-Zvi, Michal, Ido Kanter, and Wolfgang Kinzel. "Cryptography based on neural networks—analytical results." Journal of Physics A: Mathematical and General 35.47 (2002): L707.
Kanter, Ido, Wolfgang Kinzel, and Eran Kanter. "Secure exchange of information by synchronization of neural networks." EPL (Europhysics Letters)57.1 (2002): 141.
Ruttor, Andreas, Wolfgang Kinzel, and Ido Kanter. "Neural cryptography with queries." Journal of Statistical Mechanics: Theory and Experiment 2005.01 (2005): P01009.
Klein, E., Mislovaty, R., Kanter, I., Ruttor, A., & Kinzel, W. "Synchronization of neural networks by mutual learning and its application to cryptography." Advances in Neural Information Processing Systems. 2004.
Adel A. El-Zoghabi, Amr H. Yassin, Hany H. Hussien. Survey Report on Cryptography Based on Neural Network. International Journal of Emerging Technology and Advanced Engineering, 3(12):456-462, 2013.
Bishop, Christopher M. "Neural networks for pattern recognition." (1995): 5.
Rosenblatt F., 1959. Principles of Neurodynamics. New York. Spartan Books:23-26
Minsky M., Papert S. Perceptrons: An introduction to Computational Geometry. The MIT Press: 1969, 13-33
Rumelhat DE., Hinon GE., Williams RJ. Learning representations by back-propagating errors. Nature, 1986, 323:533-536
Fausett, L, Fundamentals of Neural Networks, New York: Prentice Hall, 1994.
Ben Kröse, Patrick van der Smagt, “An introduction to Neural Networks”, The University of Amsterdam, Eighth edition, 1996
Zhan, J., Chang, L., & Matwin, S. Building k-nearest neighbor classifiers on vertically partitioned private data. IEEE International Conference on Granular Computing, 2: 708-711, 2005
Arcticle History
Submitted: Jul. 1, 2014
Accepted: Jul. 15, 2014
Published: Jul. 18, 2014
The American Association for Science and Technology (AASCIT) is a not-for-profit association
of scientists from all over the world dedicated to advancing the knowledge of science and technology and its related disciplines, fostering the interchange of ideas and information among investigators.
©Copyright 2013 -- 2019 American Association for Science and Technology. All Rights Reserved.