The design and realization of a new high speed FPGA-based chaotic true random number generator

Koyuncu, İ̇smail; zcerit, Ahmet Turan

doi:10.1016/j.compeleceng.2016.07.005

Cited by 87 publications

(48 citation statements)

References 17 publications

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“…The approximate entropy algorithm is proposed from the angle of measuring the complexity of time series [36][37][38][39]. The main idea of the approximate entropy algorithm is using a non-negative value to quantify the complexity and irregularity of the time series, and the value increases with the increase of sequence complexity.…”

Section: The Analysis Of Complexitymentioning

confidence: 99%

A New Two-Dimensional Map with Hidden Attractors

Wang

Ding

2018

Entropy

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Abstract:The investigations of hidden attractors are mainly in continuous-time dynamic systems, and there are a few investigations of hidden attractors in discrete-time dynamic systems. The classical chaotic attractors of the Logistic map, Tent map, Henon map, Arnold's cat map, and other widely-known chaotic attractors are those excited from unstable fixed points. In this paper, the hidden dynamics of a new two-dimensional map inspired by Arnold's cat map is investigated, and the existence of fixed points and their stabilities are studied in detail.

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Section: The Analysis Of Complexitymentioning

confidence: 99%

A New Two-Dimensional Map with Hidden Attractors

Wang

Ding

2018

Entropy

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“…So, it's very critical to find the hardware usage of this work. İ. Koyuncu, and A.T. Özcerit, [12] presented the new high-speed FPGA based chaotic true RNG. In this paper, chaotic system was modeled numerically with the help of fourth order of the Runge-Kutta method.…”

Section: Related Workmentioning

confidence: 99%

Low Area FPGA Implementation of PRNG-LCC-CSLA Architecture Based on Chaotic Circuit

Thammannagowda¹,

Ramchandra²

2019

IJIES

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In the present days, internet communication is adapting data hiding and cryptography that often needs billions of pseudo random numbers. The Pseudo Random Number Generator (PRNG) is used to fulfill privacy and security needs in most of the applications. In this paper, the PRNG using Lorenz Chaotic Circuit with Carry Select Adder (PRNG-LCC-CSLA) method used to generate the pseudo random numbers. The LCC implemented by using Verilog to generate the pseudo random numbers, and randomness of the LCC results also checked in National Institute of Standards Technology (NIST) testing scheme. Encryption process is implemented by employing binary value of both text and image. Decryption process is implemented by employing same LCC binary value of encrypted data. At last, the Matlab is used to observe the text and image data. The Field Programmable Gate Array (FPGA) results showed how Look Up Table (LUT), slice, flip-flop and frequency improved and Application Specific Integrated Circuit (ASIC) results showed area, power, delay, Area Power Product (APP) and Area Delay Product (ADP) improvements in the PRNG-LCC-CSLA method compared to the existing methods.

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“…The numbers produced by TRNGs have gained a structure that are aperiodic and unpredictable. Hence, the randomness of the created numbers has reached high levels . However, the bit streams of TRNGs should pass the statistical randomness tests to meet the needs of the security of one‐time pad, key generation, and other cryptographic applications that necessitate high security level.…”

Section: Basic Conceptsmentioning

confidence: 99%

“…The RS_output signal has been obtained by utilizing the signals received from the output of DFF 3 and DFF 4 for EX‐OR process. Output_ready signal will be set “1” when the RS_output signal is produced …”

Section: The Design and Realization Of Ann‐based Trng On Fpgamentioning

confidence: 99%

“…Hence, the randomness of the created numbers has reached high levels. 51,54,57 However, the bit streams of TRNGs should pass the statistical randomness tests to meet the needs of the security of one-time pad, key generation, and other cryptographic applications that necessitate high security level. In addition, the upcoming random bit should be unpredictable.…”

Section: True Random Number Generatormentioning

confidence: 99%

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

Alçın

Koyuncu

Tuna

et al. 2018

Circuit Theory & Apps

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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.

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The design and realization of a new high speed FPGA-based chaotic true random number generator

Cited by 87 publications

References 17 publications

A New Two-Dimensional Map with Hidden Attractors

A New Two-Dimensional Map with Hidden Attractors

Low Area FPGA Implementation of PRNG-LCC-CSLA Architecture Based on Chaotic Circuit

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

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