Pseudorandom Number Generators Based on Asynchronous Cellular Automata

2017 ◽  
pp. 66-108
Keyword(s):  
Cellular Automata ◽  
Cellular Automaton ◽  
Random Number ◽  
Random Number Generator ◽  
Active Cell ◽  
Time Step ◽  
Random Number Generators ◽  
Von Neumann ◽  
Asynchronous Cellular Automata ◽  
Pseudo Random Number

The fourth chapter deals with the use of asynchronous cellular automata for constructing high-quality pseudo-random number generators. A model of such a generator is proposed. Asynchronous cellular automata are constructed using the neighborhood of von Neumann and Moore. Each cell of such an asynchronous cellular state can be in two states (information and active states). There is only one active cell at each time step in an asynchronous cellular automaton. The cell performs local functions only when it is active. At each time step, the active cell transmits its active state to one of the neighborhood cells. An algorithm for the operation of a pseudo-random number generator based on an asynchronous cellular automaton is described, as well as an algorithm for working a cell. The hardware implementation of such a generator is proposed. Several variants of cell construction are considered.

Pseudorandom Number Generators Based on Asynchronous Cellular Automata and Cellular Automata With Inhomogeneous Cells

2017 ◽  
pp. 127-138
Keyword(s):  
Cellular Automata ◽  
Cellular Automaton ◽  
Local State ◽  
Active Cell ◽  
State Function ◽  
Local Function ◽  
Time Step ◽  
Random Number Generators ◽  
Asynchronous Cellular Automata ◽  
A Cell

The sixth chapter deals with the construction of pseudo-random number generators based on a combination of two cellular automata, which were considered in the previous chapters. The generator is constructed based on two cellular automata. The first cellular automaton controls the location of the active cell on the second cellular automaton, which realizes the local state function for each cell. The active cell on the second cellular automaton is the main cell and from its output bits of the bit sequence are formed at the output of the generator. As the first cellular automaton, an asynchronous cellular automaton is used in this chapter, and a synchronous cellular automaton is used as the second cellular automaton. In this case, the active cell of the second cellular automaton realizes another local function at each time step and is inhomogeneous. The algorithm for the work of a cell of a combined cellular automaton for implementing a generator and its hardware implementation are presented.

Pseudorandom Number Generators Based on Cellular Automata With the Hexagonal Coverage

2017 ◽  
pp. 139-156
Keyword(s):  
Cellular Automata ◽  
Cellular Automaton ◽  
Random Number ◽  
The State ◽  
Time Step ◽  
Random Number Generators ◽  
Asynchronous Cellular Automata ◽  
Local Functions ◽  
Initial Settings ◽  
The Right

The seventh chapter describes approaches to constructing pseudo-random number generators based on cellular automata with a hexagonal coating. Several variants of cellular automata with hexagonal coating are considered. Asynchronous cellular automata with hexagonal coating are used. To simulate such cellular automata with software, a hexagonal coating was formed using an orthogonal coating. At the same time, all odd lines shifted to the cell floor to the right or to the left. The neighborhood of each cell contains six neighboring cells that have one common side with one cell of neighborhood. The chapter considers the behavior of cellular automata for different sizes and different initial settings. The behavior of cellular automata with various local functions is described, as well as the behavior of the cellular automaton with an additional bit inverting the state of the cell in each time step of functioning.

The Pseudorandom Number Generators Based on Cellular Automata With Inhomogeneous Cells

2017 ◽  
pp. 109-126
Keyword(s):  
Cellular Automata ◽  
Random Number ◽  
Random Number Generator ◽  
Number Generator ◽  
Local Function ◽  
Random Number Generators ◽  
Hybrid Cellular Automata ◽  
Main Cell ◽  
Pseudo Random Number ◽  
Pseudo Random Number Generator

The fifth chapter deals with the use of hybrid cellular automata for constructing high-quality pseudo-random number generators. A hybrid cellular automaton consists of homogeneous cells and a small number of inhomogeneous cells. Inhomogeneous cells perform a local function that differs from local functions that homogeneous cells realize. The location of inhomogeneous cells and the main cell is chosen in advance. The output of the main cell is the output of a pseudo-random number generator. A hardware implementation of a pseudo-random number generator based on hybrid cellular automata is described. The local function that an inhomogeneous cell realizes is the majority function. The principles of constructing a pseudo-random number generator based on cellular automata with inhomogeneous neighborhoods are described. In such cellular automata, inhomogeneous cells have a neighborhood whose shape differs from that of neighborhoods of homogeneous cells.

scholarly journals Design of a Hybrid Programmable 2-D Cellular Automata Based Pseudo Random Number Generator

2020 ◽  
Vol 8 (6) ◽  
pp. 5741-5748
Keyword(s):  
Cellular Automata ◽  
Random Number ◽  
Hamming Distance ◽  
Random Number Generator ◽  
Number Generator ◽  
Two Dimensional ◽  
Pseudo Random Number ◽  
Rule Set ◽  
Initial Seed ◽  
Pseudo Random Number Generator

This paper proposes a hybrid programmable two-dimensional Cellular Automata (CA) based pseudo-random number generator which includes a newly designed rule set. The properties and evolution of one and two dimensional CA are revisited. The various metrics for evaluating CA as a Pseudo-Random Number Generator (PRNG) are discussed. It is proved that the randomness is high irrespective of the initial seed by applying this newly designed rule set. The PRNG is tested against a popular statistical test called Diehard test suite and the results show that the PRNG is highly random. The chaotic measures like entropy, hamming distance and cycle length have been measured

Pseudo-random number generation using a 3-state cellular automaton

2017 ◽  
Vol 28 (06) ◽  
pp. 1750078 ◽  
Author(s):  
Kamalika Bhattacharjee ◽  
Dipanjyoti Paul ◽  
Sukanta Das
Keyword(s):  
Cellular Automaton ◽  
Random Number ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Number Generator ◽  
Number Generation ◽  
Pseudo Random Number ◽  
Empirical Tests ◽  
Pseudo Random Number Generator

This paper investigates the potentiality of pseudo-random number generation of a 3-neighborhood 3-state cellular automaton (CA) under periodic boundary condition. Theoretical and empirical tests are performed on the numbers, generated by the CA, to observe the quality of it as pseudo-random number generator (PRNG). We analyze the strength and weakness of the proposed PRNG and conclude that the selected CA is a good random number generator.

scholarly journals Pseudo Random Number Generation through Reinforcement Learning and Recurrent Neural Networks

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 307
Author(s):  
Luca Pasqualini ◽  
Maurizio Parton
Keyword(s):  
Reinforcement Learning ◽  
Random Number ◽  
Short Term Memory ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Time Step ◽  
Software Applications ◽  
Pseudo Random Number ◽  
Markov Decision ◽  
Partially Observable

A Pseudo-Random Number Generator (PRNG) is any algorithm generating a sequence of numbers approximating properties of random numbers. These numbers are widely employed in mid-level cryptography and in software applications. Test suites are used to evaluate the quality of PRNGs by checking statistical properties of the generated sequences. These sequences are commonly represented bit by bit. This paper proposes a Reinforcement Learning (RL) approach to the task of generating PRNGs from scratch by learning a policy to solve a partially observable Markov Decision Process (MDP), where the full state is the period of the generated sequence, and the observation at each time-step is the last sequence of bits appended to such states. We use Long-Short Term Memory (LSTM) architecture to model the temporal relationship between observations at different time-steps by tasking the LSTM memory with the extraction of significant features of the hidden portion of the MDP’s states. We show that modeling a PRNG with a partially observable MDP and an LSTM architecture largely improves the results of the fully observable feedforward RL approach introduced in previous work.

A list of tri-state cellular automata which are potential pseudo-random number generators

2018 ◽  
Vol 29 (09) ◽  
pp. 1850088 ◽  
Author(s):  
Kamalika Bhattacharjee ◽  
Sukanta Das
Keyword(s):  
Cellular Automata ◽  
Random Number ◽  
Random Number Generators ◽  
Rule Space ◽  
Pseudo Random Number

This paper explores [Formula: see text]-neighborhood [Formula: see text]-state cellular automata (CAs) to find a list of potential pseudo-random number generators (PRNGs). As the rule-space is huge ([Formula: see text]), we have taken two greedy strategies to select the initial set of rules. These CAs are analyzed theoretically and empirically to find the CAs with consistently good randomness properties. Finally, we have listed [Formula: see text] good CAs which qualify as potential PRNGs.

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.

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