350 rub
Journal Neurocomputers №6 for 2015 г.
Article in number:
Rise of firmness of cipher application of neural network block
Keywords: block encryption cipher strength symmetric encryption key neural network
Authors:
V.P. Dobritsa - Dr. Sc. (Phys.-Math), Professor, Chair of the Information Security and Communications Network, South-West state university (Kursk). E-mail: dobritsa@mail.ru A.Y. Zakharina - Master, Department of Protection of Information and Communications Systems, South-West State University (Kursk). E-mail: sasha_star@inbox.ru N.S. Ualiyev - Ph.D. (Phys.-Math), Associate Professor, Chair of Information Technology of Zhetysu State University named after Ilyas Zhansugurov (Zheltoksan, Kazakhstan). E-mail: n.ualiyev@gmail.com
Abstract:
In this paper the question raises durability of cipher by the change of the symmetric key by using the neural network block, in which the short code is generated. The private key of the open code is a sequence of 1 and 0 the length of the n . Private key length long, marked by m. This enables the use of 2n different private keys. The objective of this network is to prove the existence of a network to detect conditioning peaks n-dimensional Cuba has two classes, random distribution of the vertices of the cube for those classes. Thus justified the existence of this network with arbitrary selection 2n different cipher keys.
Pages: 14-17
References

 

  1. Fergjuson N., SHnajjer B. Prakticheskaja kriptografija: Per. s angl. M.: Izdatelskijj dom «Viljams». 2005. 424 s.
  2. Lavrinenko I. N., CHervjakov N. I., Evdokimov A. A., Golovko A. N. Programmiruemyjj blochnyjj shifr na osnove ispolzovanija nejjronnykh setejj // Nejjrokompjutery: razrabotka, primenenie. 2009. № 5. C. 72-80.
  3. Shiguo Lian. A block cipher based on chaotic neural networks // Neurocomputing. January 2009. V. 72. Is. 4-6, P. 296-1301.
  4. Volokitin S. S., Dobrica V. P. Blochnyjj shifr na osnove nejjronnojj seti // Nejjrokompjutery: razrabotka, primenenie. 2014. № 6. S.16 - 18.
  5. Dobrica V.P., Lipunov A.A. Nejjrosetevojj shifrator tekstov // Naukoemkie tekhnologii. 2012. T.13. № 9. S. 13-15.
  6. Dobrica V.P., Lipunov A.A. SHifrator na osnove nejjroseti // Izv. JUgo-Zapadnogo gosudarstvennogo universiteta. 2011. № 5 (38). CH. 1. S. 93-97.
  7. Dobrica V.P., Nurgabyl D. N., Ualiev N.S. Sushhestvovanie klassificirujushhejj nejjronnojj seti dlja proizvolnogo razbienija vershin n-mernogo kuba na dva mnozhestva // Nejjrokompjutery: razrabotka, primenenie. 2014. № 6. S. 12 - 15.
  8. Kolmogorov A.N. Predstavlenie nepreryvnykh funkcijj mnogikh peremennykh superpoziciejj funkcijj odnojj peremennojj i slozheniem // DAN SSSR. 1958. № 5. S. 953-956.
  9. Rojas Raul. Theorie der neuronalen Netze. Eine sistematische Einfuehrung. Berlin: Springer - Verlag. 1993.
  10. Hornik K., Stinchcombe M. and White H. Multilayer feed forward networks are universal approximations // Neural Networks. 1989. Universal approximations // Neural Networks. 1989. № 2.P. 359-366.