Hybrid pseudo-random number generator for cryptographic systems

Avaroğlu, Erdinç; Koyuncu, İ̇smail; Özer, Ahmet Bedri; Türk, Mustafa

doi:10.1007/s11071-015-2152-8

Cited by 50 publications

(21 citation statements)

References 14 publications

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“…After that, using Φ transition function, S n state is updated as S n +1 = Φ ( S n ). S 0 represents the first internal state and S 1 value corresponds to the seed value of S 0 state and the equation S 1 = Φ ( S 0 ) is generated [ 18 ]. In short, these generators need the starting parameters also known as seed.…”

Section: Random Number Generation Methodsmentioning

confidence: 99%

True Random Number Generation from Bioelectrical and Physical Signals

Tuncer

2018

Computational and Mathematical Methods in Medicine

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It is possible to generate personally identifiable random numbers to be used in some particular applications, such as authentication and key generation. This study presents the true random number generation from bioelectrical signals like EEG, EMG, and EOG and physical signals, such as blood volume pulse, GSR (Galvanic Skin Response), and respiration. The signals used in the random number generation were taken from BNCIHORIZON2020 databases. Random number generation was performed from fifteen different signals (four from EEG, EMG, and EOG and one from respiration, GSR, and blood volume pulse datasets). For this purpose, each signal was first normalized and then sampled. The sampling was achieved by using a nonperiodic and chaotic logistic map. Then, XOR postprocessing was applied to improve the statistical properties of the sampled numbers. NIST SP 800-22 was used to observe the statistical properties of the numbers obtained, the scale index was used to determine the degree of nonperiodicity, and the autocorrelation tests were used to monitor the 0-1 variation of numbers. The numbers produced from bioelectrical and physical signals were successful in all tests. As a result, it has been shown that it is possible to generate personally identifiable real random numbers from both bioelectrical and physical signals.

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Section: Random Number Generation Methodsmentioning

confidence: 99%

True Random Number Generation from Bioelectrical and Physical Signals

Tuncer

2018

Computational and Mathematical Methods in Medicine

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“…Random numbers from these sources are then used in conjunction with strong algorithms to provide the security [1]. These generators use different algorithms, especially cryptographic constructs, such as encryption or hashing algorithms, to guarantee security [8], [9]. Avaroğlu, et al proposes a hybrid structure using the Advanced Encryption Standard (AES) algorithm operating in conjunction with a chaotic additional input, and this work proves to be satisfactory for cryptographic applications [9].…”

Section: R4mentioning

confidence: 99%

“…These generators use different algorithms, especially cryptographic constructs, such as encryption or hashing algorithms, to guarantee security [8], [9]. Avaroğlu, et al proposes a hybrid structure using the Advanced Encryption Standard (AES) algorithm operating in conjunction with a chaotic additional input, and this work proves to be satisfactory for cryptographic applications [9]. Thamrin, et al uses an optical mechanism and linear feedback recording to generate hybrid random numbers, which satisfy the requirements of the appropriate statistical tests [10].…”

Section: R4mentioning

confidence: 99%

Secure and Efficient Hybrid Random Number Generator Based on Sponge Constructions for Cryptographic Applications

Yakut¹,

Tuncer²,

Özer³

2019

ELEKTRON ELEKTROTECH

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Random numbers constitute the most important part of many applications and have a vital importance in the security of these applications, especially in cryptography. Therefore, there is a need for secure random numbers to provide their security. This study is concerned with the development of a secure and efficient random number generator that is primarily intended for cryptographic applications. The generator consists of two subsystems. The first is algorithmic structure, Keccak, which is the latest standard for hash functions. The structure provides to generate secure random numbers. The second is additional input that generates with ring oscillators that are implemented on the FPGA. The additional inputs prevent reproduction and prediction of the subsequent random numbers. It is shown that the proposed generator is satisfies security requirements for cryptographic applications. In addition, NIST 800-22 test suite and autocorrelation test are used to demonstrate that generated random numbers have no statistical weaknesses and relationship among itself, respectively. Successful results from these tests show that generated numbers have no statistical weaknesses. Moreover, important advantage of the proposed generator is that it is more efficient than existing RNGs in the literature.

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“…In addition, the upcoming random bit should be unpredictable. [58][59][60][61] As TRNGs have been generated for cryptographic purpose, the resistance of TRNG against possible threats is too important. From this point of view, the IC application for TRNG has top-priority significance.…”

Section: True Random Number Generatormentioning

confidence: 99%

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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Hybrid pseudo-random number generator for cryptographic systems

Cited by 50 publications

References 14 publications

True Random Number Generation from Bioelectrical and Physical Signals

True Random Number Generation from Bioelectrical and Physical Signals

Secure and Efficient Hybrid Random Number Generator Based on Sponge Constructions for Cryptographic Applications

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

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