A novel high speed Artificial Neural Network–based chaotic True Random Number Generator on Field Programmable Gate Array. (8th November 2018)

• Record Type: Journal Article
• Authors: Alcin, Murat; Koyuncu, Ismail; Tuna, Murat; Varan, Metin; Pehlivan, Ihsan
• Is Part Of: International journal of circuit theory and applications. Volume 47:Number 3(2019)
• Page Start: 365
• Page End: 378
• Publication Date: 2018-11-08
• Subjects: artificial neural networks -- chaotic systems -- field‐programmable gate arrays -- true random number generators -- VHDL; Electric circuit analysis -- Periodicals; 621.319205
• DOI: 10.1002/cta.2581
• Languages: English
• ISSNs: 0098-9886

Abstract

Summary: 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. Abstract : The innovation of this paper is that it is the first time in literature for any artificial neural network (ANN)–modeled chaotic system to be used in true random number generator (TRNG) implementation on a field‐programmable gate array (FPGA). The proposed TRNG technique is superior to most of the other approaches of our knowledge because of its higher throughput and the success of passing the classical test packages. The implemented TRNG has successfully achieved a maximum throughput of 115.794 Mbps, which is the highest data rate to date.