A novel pseudo-random number generator for IoT based on a coupled map lattice system using the generalised symmetric map

Pseudo-random number generators (PRNGs) are one of the building blocks of cryptographic methods and therefore, new and improved PRNGs are continuously developed. In this study, a novel method to generate pseudo-random sequences using coupled map lattices is presented. Chaotic maps only show their chaotic behaviour for a specified range of control parameters, what can restrict their application in cryptography. In this work, generalised symmetric maps with adaptive control parameter are presented. This novel idea allows the user to choose any symmetric chaotic map, while ensuring that the output is a stream of independent and random sequences. Furthermore, to increase the complexity of the generated sequences, a lattice-based structure where every local map is linked to its neighbouring node via coupling factor has been used. The dynamic behaviour and randomness of the proposed system has been studied using Kolmogorov–Sinai entropy, bifurcation diagrams and the NIST statistical suite for randomness. Experimental results show that the proposed PRNG provides a large key space, generates pseudo-random sequences and is computationally suitable for IoT devices.

Itamaracá: A Novel Simple Way to Generate Pseudo-random Numbers

In this paper was presented Itamaracá, a novel simple way to generate pseudo random numbers. In general vision we can say that Itamaracá tends to pass in some statistical tests like frequency, chi square, autocorrelation, run sequence and run test. As an effect to comparison also was taking into account the results of the function R and Between by Microsoft Excel and true random numbers by Random Org analyzed its distinctive characteristics as well as with the proposal model. In this sense, the goal of this study is contributing to growing the existing Pseudo Random Number Generators (PRNGs) portfolio.

Itamaracá: A Novel Simple Way to Generate Pseudo-random Numbers

In this paper was presented Itamaracá, a novel simple way to generate pseudo random numbers. In general vision we can say that Itamaracá tends to pass in some statistical tests like frequency, chi square, autocorrelation, run sequence and run test. As an effect to comparison also was taking into account the results of the function R and Between by Microsoft Excel and true random numbers by Random Org analyzed its distinctive characteristics as well as with the proposal model. In this sense, the goal of this study is contributing to growing the existing Pseudo Random Number Generators (PRNGs) portfolio.

Design of a Cryptographically Secure Pseudo Random Number Generator with Grammatical Evolution

This work investigates the potential of evolving an initial seed with Grammatical Evolution (GE), for the construction of cryptographically secure (CS) pseudo-random number generator (PRNG). We harness the flexibility of GE as an entropy source for returning initial seeds. The initial seeds returned by GE demonstrate an average entropy value of 7.920261600000001 which is extremely close to the ideal value of 8. The initial seed combined with our proposed approach, control_flow_incrementor, is used to construct both, GE-PRNG and GE-CSPRNG.The random numbers generated with CSPRNG meet the prescribed National Institute of Standards and Technology (NIST) SP800-22 requirements. Monte Carlo simulations established the efficacy of the PRNG. The experimental setup was designed to estimate the value for pi, in which 100,000,000 random numbers were generated by our system and which resulted in returning the value of pi to 3.146564000, with a precision up to six decimal digits. The random numbers by GE-PRNG were compared against those generated by Python’s rand() function for sampling. The sampling results, when measured for accuracy against twenty-nine real world regression datasets, showed that GE-PRNG had less error when compared to Python’s rand() against the ground truths in seventeen of those, while there was no discernible difference in the remaining twelve.

Evaluation of Pseudo-Random Number Generation on GPU Cards

Monte Carlo methods rely on sequences of random numbers to obtain solutions to many problems in science and engineering. In this work, we evaluate the performance of different pseudo-random number generators (PRNGs) of the Curand library on a number of modern Nvidia GPU cards. As a numerical test, we generate pseudo-random number (PRN) sequences and obtain non-uniform distributions using the acceptance-rejection method. We consider GPU, CPU, and hybrid CPU/GPU implementations. For the GPU, we additionally consider two different implementations using the host and device application programming interfaces (API). We study how the performance depends on implementation parameters, including the number of threads per block and the number of blocks per streaming multiprocessor. To achieve the fastest performance, one has to minimize the time consumed by PRNG seed setup and state update. The duration of seed setup time increases with the number of threads, while PRNG state update decreases. Hence, the fastest performance is achieved by the optimal balance of these opposing effects.

Pseudo-random number generator based on linear congruence and delayed Fibonacci method

Pseudo-random number generation techniques are an essential tool to correctly test machine learning processes. The methodologies are many, but also the possibilities to combine them in a new way are plenty. Thus, there is a chance to create mechanisms potentially useful in new and better generators. In this paper, we present a new pseudo-random number generator based on a hybrid of two existing generators - a linear congruential method and a delayed Fibonacci technique. We demonstrate the implementation of the generator by checking its correctness and properties using chi-square, Kolmogorov and TestU01.1.2.3 tests and we apply the Monte Carlo Cross Validation method in classification context to test the performance of the generator in practice.

Hybrid quantum random number generator for cryptographic algorithms

The subject matter of the article is pseudo-random number generators. Random numbers play the important role in cryptography. Using not secure pseudo-random number generators is a very common weakness. It is also a fundamental resource in science and engineering. There are algorithmically generated numbers that are similar to random distributions but are not random, called pseudo-random number generators. In many cases the tasks to be solved are based on the unpredictability of random numbers, which cannot be guaranteed in the case of pseudo-random number generators, true randomness is required. In such situations, we use real random number generators whose source of randomness is unpredictable random events. Quantum Random Number Generators (QRNGs) generate real random numbers based on the inherent randomness of quantum measurements. The goal is to develop a mathematical model of the generator, which generates fast random numbers at a lower cost. At the same time, a high level of randomness is essential. Through quantum mechanics, we can obtain true numbers using the unpredictable behavior of a photon, which is the basis of many modern cryptographic protocols. It is essential to trust cryptographic random number generators to generate only true random numbers. This is why certification methods are needed which will check both the operation of the device and the quality of the random bits generated. The goal of the research is also to develop the model of a hybrid semi self-testing certification method for quantum random number generators (QRNG). The tasks to be solved are to create the mathematical model of a random number generator, which generates the fast random numbers at a lower cost. To create the mathematical model of a hybrid semi self-testing certification method for quantum random number generators. To integrate a hybrid semi self-testing certification method to the hybrid random number generator. the methods used are mathematical optimization and simulation. The following results were obtained: we present the improved hybrid quantum random number generator, which is based on QRNG, which uses the time of arrival of photons. The model of a hybrid semi self-testing certification method for quantum random number generators (QRNG) is offered in the paper. This method combines different types of certification approaches and is rather secure and efficient. Finally, the hybrid certification method is integrated into the model of the new quantum random number generator. Conclusions. The scientific novelty of the results obtained is as follows: 1. The hybrid quantum random number generator is offered, which is based on QRNG, which uses the time of the arrival of photons. It uses the simple version of the detectors with few requirements. The hybrid QRNG produces more than one random bit per the detection of each photon. It is rather efficient and has a high level of randomness. 2. The hybrid semi self-testing certification method for quantum random number generators (QRNG) is offered. The Self-testing, as well as device-independent quantum random number generation methods, are analyzed. The advantages and disadvantages of both methods are identified. Based on the result the hybrid method is offered. 3. The hybrid semi self-testing certification method for quantum random number generators is integrated into the offered model of the quantum random number generator. The paper analyzes its security and efficiency. The paper offers to use the new random number generator in the crypto-schemes.