Random Number Generator Based on Hopfield Neural Network and SHA-2 (512)

With the rapid development of cryptography and network communication, random number is becoming more and more important in secure data communication. The nonlinearity of backward propagation neural network (BPNN) is used to improve the traditional random number generator (RNG). SHA-2 (512) hash function can ensure the unpredictability of the produced random numbers. So, a novel and secure RNG architecture is proposed in the presented paper, which is BPNN based on SHA-2 (512) hash function. The quality of random number generated by this proposed architecture can well satisfy the security of cryptographic system according to results of test suites standardized by the U.S. The proposed architecture can be used to improve performances such as power consumption, flexibility, cost and area in network security and security for cryptographic systems.