scholarly journals Evaluation of Random Number Generator Functions Using Statistical Analysis

2019 ◽  
Vol 8 (2) ◽  
pp. 1-5
Author(s):  
Rajashree Chaurasia
Keyword(s):  
Programming Languages ◽  
Goodness Of Fit ◽  
Random Number ◽  
Statistical Tests ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Random Numbers ◽  
Number Generator ◽  
Random Number Generators ◽  
The Third

Most programming languages have in-built functions for the sole purpose of generating pseudo-random numbers. This manuscript is aimed at analyzing the appropriateness of some of these in-built functions for some basic goodness-of-fit statistical tests for random number generators. The document is divided into four sections. The first section gives a broad introduction about randomness and the methods of generation of pseudo-random numbers. Section two discusses the statistical tests that were employed for testing the built-in library functions for random number generation. This section is followed by an analysis of the data collected for the various statistics in the third section, and lastly, the fourth section presents the results of the data analysis.

scholarly journals Quantum Random Numbers Generated by a Cloud Superconducting Quantum Computer

2020 ◽  
pp. 17-37
Author(s):  
Kentaro Tamura ◽  
Yutaka Shikano
Keyword(s):  
Random Number ◽  
Quantum Computer ◽  
Statistical Tests ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Black Box ◽  
Random Numbers ◽  
Number Generator ◽  
Random Number Generators ◽  
Simple Indicator

Abstract A cloud quantum computer is similar to a random number generator in that its physical mechanism is inaccessible to its users. In this respect, a cloud quantum computer is a black box. In both devices, its users decide the device condition from the output. A framework to achieve this exists in the field of random number generation in the form of statistical tests for random number generators. In the present study, we generated random numbers on a 20-qubit cloud quantum computer and evaluated the condition and stability of its qubits using statistical tests for random number generators. As a result, we observed that some qubits were more biased than others. Statistical tests for random number generators may provide a simple indicator of qubit condition and stability, enabling users to decide for themselves which qubits inside a cloud quantum computer to use.

SRAM Based Random Number Generator for Non-Repeating Pattern Generation

2014 ◽  
Vol 573 ◽  
pp. 181-186 ◽  
Author(s):  
G.P. Ramesh ◽  
A. Rajan
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Pattern Generation ◽  
Storage Device ◽  
Number Generator ◽  
Random Number Generators ◽  
Field Programmable ◽  
The Look

—Field-programmable gate array (FPGA) optimized random number generators (RNGs) are more resource-efficient than software-optimized RNGs because they can take advantage of bitwise operations and FPGA-specific features. A random number generator (RNG) is a computational or physical device designed to generate a sequence of numbers or symbols that lack any pattern, i.e. appear random. The many applications of randomness have led to the development of several different methods for generating random data. Several computational methods for random number generation exist, but often fall short of the goal of true randomness though they may meet, with varying success, some of the statistical tests for randomness intended to measure how unpredictable their results are (that is, to what degree their patterns are discernible).LUT-SR Family of Uniform Random Number Generators are able to handle randomness only based on seeds that is loaded in the look up table. To make random generation efficient, we propose new approach based on SRAM storage device.Keywords: RNG, LFSR, SRAM

scholarly journals Novel Memristor Based True Random Number Generator

2020 ◽  
Author(s):  
Scott Stoller
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Random Number Generator ◽  
Circuit Board ◽  
Random Numbers ◽  
Number Generator ◽  
Random Number Generators ◽  
Coin Toss ◽  
True Random Number Generator ◽  
Digital World

Random numbers are an important, but often overlooked part of the modern computing environment. They are used everywhere around us for a variety of purposes, from simple decision making in video games such as a coin toss, to securing financial transactions and encrypting confidential communications. They are even useful for gambling and the lottery. Random numbers are generated in many ways. Pseudo random number generators (PRNGs) generate numbers based on a formula. True random number generators (TRNGs) capture entropy from the environment to generate randomness. As our society and our devices become more connected in the digital world, it is important to develop new ways to generate truly random numbers in order to secure communications and connected devices. In this work a novel memristor-based True Random Number Generator is designed and a physical implementation is fabricated and tested using a W-based self-directed channel (SDC) memristor. The circuit was initially designed and prototyped on a breadboard. A custom Printed Circuit Board (PCB) was fabricated for the final circuit design and testing of the novel memristor-based TRNG. The National Institute of Standards and Technology (NIST) Statistical Test Suite (STS) was used to check the output of the TRNG for randomness. The TRNG was demonstrated to pass 13 statistical tests out of the 15 in the STS.

scholarly journals Memristor based ring oscillators true random number generator with different window functions for applications in cryptography

2019 ◽  
Vol 14 (1) ◽  
pp. 201 ◽  
Author(s):  
Noor Alia Nor Hashim ◽  
Julius Teo Han Loong ◽  
Azrul Ghazali ◽  
Fazrena Azlee Hamid
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Random Numbers ◽  
Ring Oscillators ◽  
Number Generator ◽  
True Random Number Generator ◽  
Number Generation ◽  
Window Functions

<span>Cryptographic applications require numbers that are random and pseudorandom. Keys must be produced in a random manner in order to be used in common cryptosystems. Random or pseudorandom inputs at different terminals are also required in a lot of cryptographic protocols. For example, producing digital signatures using supporting quantities or in verification procedures that requires generating challenges. Random number generation is an important part of cryptography because there are flaws in random number generation that can be taken advantage by attackers that compromised encryption systems that are algorithmically secure. True random number generators (TRNGs) are the best in producing random numbers. This paper presents a True Random Number Generator that uses memristor based ring oscillators in the design. The designs are implemented in 0.18 µm complementary metal oxide semiconductor (CMOS) technology using LT SPICE IV. Different window functions for the memristor model was applied to the TRNG and compared. Statistical tests results of the output random numbers produced showed that the proposed TRNG design can produce random output regardless of the window function.</span>

scholarly journals Random Numbers Generated from Audio and Video Sources

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
I-Te Chen
Keyword(s):  
Chaos Theory ◽  
Random Number ◽  
Statistical Tests ◽  
Random Number Generator ◽  
Random Numbers ◽  
Pseudorandom Number Generator ◽  
Number Generator ◽  
Random Number Generators ◽  
Video File ◽  
Recognition Probability

Random numbers are very useful in simulation, chaos theory, game theory, information theory, pattern recognition, probability theory, quantum mechanics, statistics, and statistical mechanics. The random numbers are especially helpful in cryptography. In this work, the proposed random number generators come from white noise of audio and video (A/V) sources which are extracted from high-resolution IPCAM, WEBCAM, and MPEG-1 video files. The proposed generator applied on video sources from IPCAM and WEBCAM with microphone would be the true random number generator and the pseudorandom number generator when applied on video sources from MPEG-1 video file. In addition, when applying NIST SP 800-22 Rev.1a 15 statistics tests on the random numbers generated from the proposed generator, around 98% random numbers can pass 15 statistical tests. Furthermore, the audio and video sources can be found easily; hence, the proposed generator is a qualified, convenient, and efficient random number generator.

scholarly journals Self-clocking fast and variation tolerant true random number generator based on a stochastic mott memristor

2020 ◽  
Author(s):  
Gwangmin Kim ◽  
Jae Hyun In ◽  
Hakseung Rhee ◽  
Woojoon Park ◽  
Hanchan Song ◽  
...  
Keyword(s):  
Random Number ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Harsh Environments ◽  
Random Numbers ◽  
Number Generator ◽  
High Tolerance ◽  
Stochastic Oscillation ◽  
Advanced Method ◽  
Intrinsic Stochasticity

Abstract The intrinsic stochasticity of the memristor can be used to generate true random numbers, essential for non-decryptable hardware-based security devices. Here we propose a novel and advanced method to generate true random numbers utilizing the stochastic oscillation behavior of a NbOx mott memristor, exhibiting self-clocking, fast and variation tolerant characteristics. The random number generation rate of the device can be at least 40 kbs-1, which is the fastest record compared with previous volatile memristor-based TRNG devices. Also, its dimensionless operating principle provides high tolerance against both ambient temperature variation and device-to-device variation, enabling robust security hardware applicable in harsh environments.

scholarly journals Cryptographically Secure Pseudo-Random Number Generator IP-Core Based on SHA2 Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1869 ◽  
Author(s):  
Luca Baldanzi ◽  
Luca Crocetti ◽  
Francesco Falaschi ◽  
Matteo Bertolucci ◽  
Jacopo Belli ◽  
...  
Keyword(s):  
High Performance ◽  
Random Number ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Random Numbers ◽  
Generation Process ◽  
Number Generator ◽  
Ip Core ◽  
Pseudo Random Number ◽  
Pseudo Random Number Generator

In the context of growing the adoption of advanced sensors and systems for active vehicle safety and driver assistance, an increasingly important issue is the security of the information exchanged between the different sub-systems of the vehicle. Random number generation is crucial in modern encryption and security applications as it is a critical task from the point of view of the robustness of the security chain. Random numbers are in fact used to generate the encryption keys to be used for ciphers. Consequently, any weakness in the key generation process can potentially leak information that can be used to breach even the strongest cipher. This paper presents the architecture of a high performance Random Number Generator (RNG) IP-core, in particular a Cryptographically Secure Pseudo-Random Number Generator (CSPRNG) IP-core, a digital hardware accelerator for random numbers generation which can be employed for cryptographically secure applications. The specifications used to develop the proposed project were derived from dedicated literature and standards. Subsequently, specific architecture optimizations were studied to achieve better timing performance and very high throughput values. The IP-core has been validated thanks to the official NIST Statistical Test Suite, in order to evaluate the degree of randomness of the numbers generated in output. Finally the CSPRNG IP-core has been characterized on relevant Field Programmable Gate Array (FPGA) and ASIC standard-cell technologies.

scholarly journals A Random Number Generator Using Ring Oscillators and SHA-256 as Post-Processing

2015 ◽  
Vol 61 (2) ◽  
pp. 199-204 ◽  
Author(s):  
Szymon Łoza ◽  
Łukasz Matuszewski ◽  
Mieczysław Jessa
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Random Number Generator ◽  
Random Numbers ◽  
Ring Oscillators ◽  
Number Generator ◽  
Post Processing ◽  
Field Programmable ◽  
Cryptographic System ◽  
Many Sources

Abstract Today, cryptographic security depends primarily on having strong keys and keeping them secret. The keys should be produced by a reliable and robust to external manipulations generators of random numbers. To hamper different attacks, the generators should be implemented in the same chip as a cryptographic system using random numbers. It forces a designer to create a random number generator purely digitally. Unfortunately, the obtained sequences are biased and do not pass many statistical tests. Therefore an output of the random number generator has to be subjected to a transformation called post-processing. In this paper the hash function SHA-256 as post-processing of bits produced by a combined random bit generator using jitter observed in ring oscillators (ROs) is proposed. All components – the random number generator and the SHA-256, are implemented in a single Field Programmable Gate Array (FPGA). We expect that the proposed solution, implemented in the same FPGA together with a cryptographic system, is more attack-resistant owing to many sources of randomness with significantly different nominal frequencies.

A CHAOS-BASED RANDOM NUMBER GENERATOR FOR EIGHT-BIT MICRO-CONTROLLER SYSTEM

2008 ◽  
Vol 18 (03) ◽  
pp. 851-867 ◽  
Author(s):  
K. W. TANG ◽  
H. S. KWOK ◽  
WALLACE K. S. TANG ◽  
K. F. MAN
Keyword(s):  
Chaotic Systems ◽  
Random Number ◽  
Statistical Tests ◽  
Chaotic Map ◽  
Random Number Generator ◽  
High Dimensional ◽  
Number Generator ◽  
Constrained System ◽  
Random Number Generators ◽  
Consumer Markets

Random number generators are widely used in different applications. However, it is difficult to obtain a good random number generator in low precision and resource constrained system, such as an eight-bit micro-controller system which is still commonly used in industrial and consumer markets. This paper provides a practical solution for this problem based on chaotic systems. By the use of a modified Chua's circuit, it is demonstrated that the sampled state, after post-processing by a high-dimensional chaotic map, can be used as a random source even in an eight-bit environment. The randomness of the generated sequence is testified and confirmed by different statistical tests and the up-to-date statistical suite.

scholarly journals Self-clocking fast and variation tolerant true random number generator based on a stochastic mott memristor

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Gwangmin Kim ◽  
Jae Hyun In ◽  
Young Seok Kim ◽  
Hakseung Rhee ◽  
Woojoon Park ◽  
...  
Keyword(s):  
Random Number ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Harsh Environments ◽  
Random Numbers ◽  
Number Generator ◽  
High Tolerance ◽  
Stochastic Oscillation ◽  
Advanced Method ◽  
Intrinsic Stochasticity

AbstractThe intrinsic stochasticity of the memristor can be used to generate true random numbers, essential for non-decryptable hardware-based security devices. Here, we propose a novel and advanced method to generate true random numbers utilizing the stochastic oscillation behavior of a NbOx mott memristor, exhibiting self-clocking, fast and variation tolerant characteristics. The random number generation rate of the device can be at least 40 kb s−1, which is the fastest record compared with previous volatile memristor-based TRNG devices. Also, its dimensionless operating principle provides high tolerance against both ambient temperature variation and device-to-device variation, enabling robust security hardware applicable in harsh environments.

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