scholarly journals Challenges in Certifying Small-Scale (IoT) Hardware Random Number Generators

2021 ◽  
pp. 165-181
Author(s):  
Darren Hurley-Smith ◽  
Julio Hernandez-Castro
Keyword(s):  
Computer Security ◽  
Random Number ◽  
Statistical Tests ◽  
Statistical Testing ◽  
Small Scale ◽  
Security Systems ◽  
Random Number Generators ◽  
Iot Devices ◽  
Computational Resources ◽  
Hardware Random Number Generators

AbstractThis chapter focuses on the testing and certification of Random Number Generators (RNG). Statistical testing is required to identify whether sequences produced by RNG demonstrate non-random characteristics. These can include structures within their output, repetition of sequences, and any other form of predictability. Certification of computer security systems draws on such evaluations to determine whether a given RNG implementation contributes to a secure, robust security system. Recently, small-scale hardware RNGs have been targeted at IoT devices, especially those requiring security. This, however, introduces new technical challenges; low computational resources for post-processing and evaluation of on-board RNGs being just two examples. Can we rely on the current suite of statistical tests? What other challenges are encountered when evaluating RNG?

scholarly journals Enhancing the operational efficiency of quantum random number generators

2021 ◽  
Vol 13 (2) ◽  
pp. 10-18
Author(s):  
Botond L. Márton ◽  
Dóra Istenes ◽  
László Bacsárdi
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Practical Implementation ◽  
Random Numbers ◽  
Random Number Generators ◽  
Original Source ◽  
Negative Effect ◽  
Quantum Phenomena ◽  
The Right

Random numbers are of vital importance in today’s world and used for example in many cryptographical protocols to secure the communication over the internet. The generators producing these numbers are Pseudo Random Number Generators (PRNGs) or True Random Number Generators (TRNGs). A subclass of TRNGs are the Quantum based Random Number Generators (QRNGs) whose generation processes are based on quantum phenomena. However, the achievable quality of the numbers generated from a practical implementation can differ from the theoretically possible. To ease this negative effect post-processing can be used, which contains the use of extractors. They extract as much entropy as possible from the original source and produce a new output with better properties. The quality and the different properties of a given output can be measured with the help of statistical tests. In our work we examined the effect of different extractors on two QRNG outputs and found that witg the right extractor we can improve their quality.

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 Delay-Based True Random Number Generator in Sub-Nanomillimeter IoT Devices

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 817
Author(s):  
Maulana Randa ◽  
Mohammad Samie ◽  
Ian K. Jennions
Keyword(s):  
Random Number ◽  
Promising Result ◽  
Physical Phenomenon ◽  
Random Number Generator ◽  
Ring Oscillator ◽  
Random Number Generators ◽  
Propagation Delays ◽  
Field Programmable ◽  
Iot Devices ◽  
Non Gaussian

True Random Number Generators (TRNGs) use physical phenomenon as their source of randomness. In electronics, one of the most popular structures to build a TRNG is constructed based on the circuits that form propagation delays, such as a ring oscillator, shift register, and routing paths. This type of TRNG has been well-researched within the current technology of electronics. However, in the future, where electronics will use sub-nano millimeter (nm) technology, the components become smaller and work on near-threshold voltage (NTV). This condition has an effect on the timing-critical circuit, as the distribution of the process variation becomes non-gaussian. Therefore, there is an urge to assess the behavior of the current delay-based TRNG system in sub-nm technology. In this paper, a model of TRNG implementation in sub-nm technology was created through the use of a specific Look-Up Table (LUT) in the Field-Programmable Gate Array (FPGA), known as SRL16E. The characterization of the TRNG was presented and it shows a promising result, in that the delay-based TRNG will work properly, with some constraints in sub-nm technology.

scholarly journals Time-Adaptive Statistical Test for Random Number Generators

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 630 ◽  
Author(s):  
Boris Ryabko
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Asymptotic Properties ◽  
Test Sequence ◽  
P Value ◽  
Specific Test ◽  
Random Number Generators ◽  
P Values ◽  
Small Deviations ◽  
The One

The problem of constructing effective statistical tests for random number generators (RNG) is considered. Currently, there are hundreds of RNG statistical tests that are often combined into so-called batteries, each containing from a dozen to more than one hundred tests. When a battery test is used, it is applied to a sequence generated by the RNG, and the calculation time is determined by the length of the sequence and the number of tests. Generally speaking, the longer is the sequence, the smaller are the deviations from randomness that can be found by a specific test. Thus, when a battery is applied, on the one hand, the “better” are the tests in the battery, the more chances there are to reject a “bad” RNG. On the other hand, the larger is the battery, the less time it can spend on each test and, therefore, the shorter is the test sequence. In turn, this reduces the ability to find small deviations from randomness. To reduce this trade-off, we propose an adaptive way to use batteries (and other sets) of tests, which requires less time but, in a certain sense, preserves the power of the original battery. We call this method time-adaptive battery of tests. The suggested method is based on the theorem which describes asymptotic properties of the so-called p-values of tests. Namely, the theorem claims that, if the RNG can be modeled by a stationary ergodic source, the value − l o g π ( x 1 x 2 … x n ) / n goes to 1 − h when n grows, where x 1 x 2 … is the sequence, π ( ) is the p-value of the most powerful test, and h is the limit Shannon entropy of the source.

scholarly journals ANALYSIS OF RANDOM NUMBER GENERATORS USING MONTE CARLO SIMULATION

1994 ◽  
Vol 05 (03) ◽  
pp. 547-560 ◽  
Author(s):  
P.D. CODDINGTON
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Ising Model ◽  
High Precision ◽  
Random Number ◽  
Statistical Tests ◽  
Exact Results ◽  
Random Number Generators ◽  
Monte Carlo Algorithms

Monte Carlo simulation is one of the main applications involving the use of random number generators. It is also one of the best methods of testing the randomness properties of such generators, by comparing results of simulations using different generators with each other, or with analytic results. Here we compare the performance of some popular random number generators by high precision Monte Carlo simulation of the 2-d Ising model, for which exact results are known, using the Metropolis, Swendsen-Wang, and Wolff Monte Carlo algorithms. Many widely used generators that perform well in standard statistical tests are shown to fail these Monte Carlo tests.

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

2022 ◽  
Vol 4 (2) ◽  
Author(s):  
Unsub Zia ◽  
Mark McCartney ◽  
Bryan Scotney ◽  
Jorge Martinez ◽  
Ali Sajjad
Keyword(s):  
Random Number ◽  
Chaotic Map ◽  
Random Number Generator ◽  
Building Blocks ◽  
Coupling Factor ◽  
Coupled Map Lattices ◽  
Random Number Generators ◽  
Random Sequences ◽  
Pseudo Random Number ◽  
Iot Devices

AbstractPseudo-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.

scholarly journals Pseudo-Random Number Generator Based on Logistic Chaotic System

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 960 ◽  
Author(s):  
Luyao Wang ◽  
Hai Cheng
Keyword(s):  
Chaotic System ◽  
Random Number ◽  
Chaotic Behavior ◽  
Statistical Tests ◽  
Random Sequence ◽  
Random Number Generator ◽  
Statistical Characteristics ◽  
Random Number Generators ◽  
System Parameters ◽  
Pseudo Random Number

In recent years, a chaotic system is considered as an important pseudo-random source to pseudo-random number generators (PRNGs). This paper proposes a PRNG based on a modified logistic chaotic system. This chaotic system with fixed system parameters is convergent and its chaotic behavior is analyzed and proved. In order to improve the complexity and randomness of modified PRNGs, the chaotic system parameter denoted by floating point numbers generated by the chaotic system is confused and rearranged to increase its key space and reduce the possibility of an exhaustive attack. It is hard to speculate on the pseudo-random number by chaotic behavior because there is no statistical characteristics and infer the pseudo-random number generated by chaotic behavior. The system parameters of the next chaotic system are related to the chaotic values generated by the previous ones, which makes the PRNG generate enough results. By confusing and rearranging the output sequence, the system parameters of the previous time cannot be gotten from the next time which ensures the security. The analysis shows that the pseudo-random sequence generated by this method has perfect randomness, cryptographic properties and can pass the statistical tests.

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