A novel high speed Artificial Neural Network-based chaotic True Random Number Generator on Field Programmable Gate Array

Abstract

It is well observed that cryptographic applications have great challenges in guaranteeing high security as well as high throughput. Artificial neural network (ANN)-based chaotic true random number generator (TRNG) structure has not been unprecedented in current literature. This paper provides a novel type of high-speed TRNG based on chaos and ANN implemented in a Xilinx field-programmable gate array (FPGA) chip. The paper consists of two main parts. In the first part, chaos analyses of Pehlivan-Uyaroglu_2010 chaotic system (PUCS) have been accomplished to prove that PUCS operates in chaotic regime. So PUCS can be an efficient alternative to the entropy source for classical TRNGs. In the second part, the hardware design of the proposed TRNG has been created using VHDL in Xilinx platform. As a result, the implemented TRNG offers throughput up to 115.794 Mbps. Besides, the generated random numbers have been tested with the FIPS 140-1 and NIST 800.22 test suites. The high quality of generated true random numbers have been confirmed by passing all randomness tests. The results have shown that the proposed system can provide not only high throughput but also high quality random bit sequences for a wide variety of embedded cryptographic applications.