Abstract
Neural network cryptography is an interesting area of research in the field of computer science. This paper proposes a new model to encrypt/decrypt a secret code using Neural Networks unlike previous private key cryptography model that are based on theoretic number functions. In the first part of the paper, we propose our model and analyze the privacy and security of the model thereby explaining why an attacker with a similar neural network model is unlikely to pose a threat to the system. This proves that the model is pretty secure. In the second part of the paper, we experiment with the neural network model using two different ciphertexts of different length. Parameters of the network that are tested are different learning rates, optimizers and step values. The experimental results show how to enhance the accuracy of our model even further. Furthermore, our proposed model is more efficient and accurate compared to other models for encryption and decryption.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Arvandi, M., et al.: Symmetric cipher design using recurrent neural networks. In: International Joint Conference on IJCNN 2006. IEEE (2006)
Rivest, R.L., Shamir, A., Adleman, L.: A method for obtaining digital signatures and public-key cryptosystems. Commun. ACM 21(2), 120–126 (1978)
Monrose, F., et al.: Cryptographic key generation from voice. In: Proceedings 2001 IEEE Symposium on Security and Privacy. IEEE (2001)
Pointcheval, D.: Neural Networks and their Cryptographic Applications. Pascale Charpin Ed. India (1994)
Guo, D., Cheng, L.-M., Cheng, L.L.: A new symmetric probabilistic encryption scheme based on chaotic attractors of neural networks. Appl. Intell. 10(1), 71–84 (1999)
Su, S., Lin, A., Yen, J.-C.: Design and realization of a new chaotic neural encryption/decryption network. In: The 2000 IEEE Asia-Pacific Conference on Circuits and Systems, IEEE APCCAS 2000. IEEE (2000)
Kinzel, W., Kanter, I.: Neural cryptography. In: Proceedings of the 9th International Conference on Neural Information Processing, ICONIP 2002, vol. 3. IEEE (2002)
Rosen-Zvi, M., et al.: Mutual learning in a tree parity machine and its application to cryptography. Phys. Rev. E 66(6) (2002). 066135
Klimov, A., Mityagin, A., Shamir, A.: Analysis of neural cryptography. In: International Conference on the Theory and Application of Cryptology and Information Security. Springer, Berlin, Heidelberg (2002)
Ruttor, A., et al.: Neural cryptography with feedback. Phys. Rev. E 69(4) (2004). 046110
Ruttor, A., et al.: Genetic attack on neural cryptography. Phys. Rev. E 73(3) (2006). 036121
Sarasamma, S.T., Zhu, Q.A., Huff, J.: Hierarchical Kohonenen net for anomaly detection in network security. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 35(2), 302–312 (2005)
Prabakaran, N., Loganathan, P., Vivekanandan, P.: Neural cryptography with multiple transfers functions and multiple learning rule. Int. J. Soft Comput. 3(3), 177–181 (2008)
Volna, E., et al.: Cryptography based on neural network. In: ECMS (2012)
Hecht-Nielsen, R.: Theory of the backpropagation neural network. Neural Netw. 1(Supplement–1), 445–448 (1988)
Stallings, W., Tahiliani, M.P.: Cryptography and network security: principles and practice, vol. 6. Pearson, London (2014)
Micali, S., Reyzin, L.: Physically observable cryptography. In: Theory of Cryptography Conference. Springer, Berlin, Heidelberg (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Pattanayak, S., Ludwig, S.A. (2018). Encryption Based on Neural Cryptography. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Hybrid Intelligent Systems. HIS 2017. Advances in Intelligent Systems and Computing, vol 734. Springer, Cham. https://doi.org/10.1007/978-3-319-76351-4_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-76351-4_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-76350-7
Online ISBN: 978-3-319-76351-4
eBook Packages: EngineeringEngineering (R0)