Title :
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
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;
Conference_Titel :
Electronics, Circuits and Systems, 1998 IEEE International Conference on
Conference_Location :
Lisboa
Print_ISBN :
0-7803-5008-1
DOI :
10.1109/ICECS.1998.814875
