The CHNN nonlinear combination generator

Author

Chan, Chi-Kwong ; Cheng, L.M.

Author_Institution

Dept. of Electron. Eng., City Univ. of Hong Kong, Kowloon, Hong Kong

Volume

2

fYear

1998

fDate

1998

Firstpage

257

Abstract

We present a new construction of a combination generator based on a clipped version of Hopfield neural network and maximum-length linear feedback shift registers (LFSRs). The clipped Hopfield neural network (CHNN) acts as a nonlinear combining function on the outputs of the LFSRs, which destroys the linearity and algebraic structure of the LFSRs. The resulting sequences have long period, large linear complexity and key space. The construction is suitable for practical implementation of efficient stream cipher cryptosystems

Keywords

Hopfield neural nets; binary sequences; computational complexity; cryptography; feedback; nonlinear functions; random number generation; Boolean function; clipped Hopfield neural network; efficient stream cipher cryptosystems; key space; large linear complexity; long period sequences; maximum-length linear feedback shift registers; nonlinear combination generator; nonlinear combining function; synchronous dynamics; Cryptography; Electronic mail; Hopfield neural networks; Linear feedback shift registers; Linearity; Neural networks; Neurons; Nonlinear equations; Polynomials;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronics, Circuits and Systems, 1998 IEEE International Conference on

Conference_Location

Lisboa

Print_ISBN

0-7803-5008-1

Type

conf

DOI

10.1109/ICECS.1998.814875

Filename

814875