45,726 research outputs found
D2.1 - Report on Selected TRNG and PUF Principles
This report represents the final version of Deliverable 2.1 of the HECTOR work package WP2. It is a result of discussions and work on Task 2.1 of all HECTOR partners involved in WP2. The aim of the Deliverable 2.1 is to select principles of random number generators (RNGs) and physical unclonable functions (PUFs) that fulfill strict technology, design and security criteria. For example, the selected RNGs must be suitable for implementation in logic devices according to the German AIS20/31 standard. Correspondingly, the selected PUFs must be suitable for applying similar security approach. A standard PUF evaluation approach does not exist, yet, but it should be proposed in the framework of the project. Selected RNGs and PUFs should be then thoroughly evaluated from the point of view of security and the most suitable principles should be implemented in logic devices, such as Field Programmable Logic Arrays (FPGAs) and Application Specific Integrated Circuits (ASICs) during the next phases of the project
Generating realistic scaled complex networks
Research on generative models is a central project in the emerging field of
network science, and it studies how statistical patterns found in real networks
could be generated by formal rules. Output from these generative models is then
the basis for designing and evaluating computational methods on networks, and
for verification and simulation studies. During the last two decades, a variety
of models has been proposed with an ultimate goal of achieving comprehensive
realism for the generated networks. In this study, we (a) introduce a new
generator, termed ReCoN; (b) explore how ReCoN and some existing models can be
fitted to an original network to produce a structurally similar replica, (c)
use ReCoN to produce networks much larger than the original exemplar, and
finally (d) discuss open problems and promising research directions. In a
comparative experimental study, we find that ReCoN is often superior to many
other state-of-the-art network generation methods. We argue that ReCoN is a
scalable and effective tool for modeling a given network while preserving
important properties at both micro- and macroscopic scales, and for scaling the
exemplar data by orders of magnitude in size.Comment: 26 pages, 13 figures, extended version, a preliminary version of the
paper was presented at the 5th International Workshop on Complex Networks and
their Application
Bayesian approach to Spatio-temporally Consistent Simulation of Daily Monsoon Rainfall over India
Simulation of rainfall over a region for long time-sequences can be very
useful for planning and policy-making, especially in India where the economy is
heavily reliant on monsoon rainfall. However, such simulations should be able
to preserve the known spatial and temporal characteristics of rainfall over
India. General Circulation Models (GCMs) are unable to do so, and various
rainfall generators designed by hydrologists using stochastic processes like
Gaussian Processes are also difficult to apply over the vast and highly diverse
landscape of India. In this paper, we explore a series of Bayesian models based
on conditional distributions of latent variables that describe weather
conditions at specific locations and over the whole country. During parameter
estimation from observed data, we use spatio-temporal smoothing using Markov
Random Field so that the parameters learnt are spatially and temporally
coherent. Also, we use a nonparametric spatial clustering based on Chinese
Restaurant Process to identify homogeneous regions, which are utilized by some
of the proposed models to improve spatial correlations of the simulated
rainfall. The models are able to simulate daily rainfall across India for
years, and can also utilize contextual information for conditional simulation.
We use two datasets of different spatial resolutions over India, and focus on
the period 2000-2015. We propose a large number of metrics to study the
spatio-temporal properties of the simulations by the models, and compare them
with the observed data to evaluate the strengths and weaknesses of the models
Pseudorandom sequence generation using binary cellular automata
Tezin basılısı İstanbul Şehir Üniversitesi Kütüphanesi'ndedir.Random numbers are an integral part of many applications from computer simulations,
gaming, security protocols to the practices of applied mathematics and physics. As
randomness plays more critical roles, cheap and fast generation methods are becoming a
point of interest for both scientific and technological use.
Cellular Automata (CA) is a class of functions which attracts attention mostly due to the
potential it holds in modeling complex phenomena in nature along with its discreteness
and simplicity. Several studies are available in the literature expressing its potentiality
for generating randomness and presenting its advantages over commonly used random
number generators.
Most of the researches in the CA field focus on one-dimensional 3-input CA rules. In
this study, we perform an exhaustive search over the set of 5-input CA to find out the
rules with high randomness quality. As the measure of quality, the outcomes of NIST
Statistical Test Suite are used.
Since the set of 5-input CA rules is very large (including more than 4.2 billions of rules),
they are eliminated by discarding poor-quality rules before testing.
In the literature, generally entropy is used as the elimination criterion, but we preferred
mutual information. The main motive behind that choice is to find out a metric for
elimination which is directly computed on the truth table of the CA rule instead of the
generated sequence. As the test results collected on 3- and 4-input CA indicate, all rules
with very good statistical performance have zero mutual information. By exploiting this
observation, we limit the set to be tested to the rules with zero mutual information. The
reasons and consequences of this choice are discussed.
In total, more than 248 millions of rules are tested. Among them, 120 rules show out-
standing performance with all attempted neighborhood schemes. Along with these tests,
one of them is subjected to a more detailed testing and test results are included.
Keywords: Cellular Automata, Pseudorandom Number Generators, Randomness TestsContents
Declaration of Authorship ii
Abstract iii
Öz iv
Acknowledgments v
List of Figures ix
List of Tables x
1 Introduction 1
2 Random Number Sequences 4
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2 Theoretical Approaches to Randomness . . . . . . . . . . . . . . . . . . . 5
2.2.1 Information Theory . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2.2 Complexity Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2.3 Computability Theory . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.3 Random Number Generator Classification . . . . . . . . . . . . . . . . . . 7
2.3.1 Physical TRNGs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3.2 Non-Physical TRNGs . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3.3 Pseudorandom Number Generators . . . . . . . . . . . . . . . . . . 10
2.3.3.1 Generic Design of Pseudorandom Number Generators . . 10
2.3.3.2 Cryptographically Secure Pseudorandom Number Gener- ators . . . . . . . . . . . . . .11
2.3.4 Hybrid Random Number Generators . . . . . . . . . . . . . . . . . 13
2.4 A Comparison between True and Pseudo RNGs . . . . . . . . . . . . . . . 14
2.5 General Requirements on Random Number Sequences . . . . . . . . . . . 14
2.6 Evaluation Criteria of PRNGs . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.7 Statistical Test Suites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.8 NIST Test Suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.8.1 Hypothetical Testing . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.8.2 Tests in NIST Test Suite . . . . . . . . . . . . . . . . . . . . . . . . 20
2.8.2.1 Frequency Test . . . . . . . . . . . . . . . . . . . . . . . . 20
2.8.2.2 Block Frequency Test . . . . . . . . . . . . . . . . . . . . 20
2.8.2.3 Runs Test . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.8.2.4 Longest Run of Ones in a Block . . . . . . . . . . . . . . 21
2.8.2.5 Binary Matrix Rank Test . . . . . . . . . . . . . . . . . . 21
2.8.2.6 Spectral Test . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.8.2.7 Non-overlapping Template Matching Test . . . . . . . . . 22
2.8.2.8 Overlapping Template Matching Test . . . . . . . . . . . 22
2.8.2.9 Universal Statistical Test . . . . . . . . . . . . . . . . . . 23
2.8.2.10 Linear Complexity Test . . . . . . . . . . . . . . . . . . . 23
2.8.2.11 Serial Test . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.8.2.12 Approximate Entropy Test . . . . . . . . . . . . . . . . . 24
2.8.2.13 Cumulative Sums Test . . . . . . . . . . . . . . . . . . . . 24
2.8.2.14 Random Excursions Test . . . . . . . . . . . . . . . . . . 24
2.8.2.15 Random Excursions Variant Test . . . . . . . . . . . . . . 25
3 Cellular Automata 26 3.1 History of Cellular Automata . . . . . . . . . . . . . . . . . . . . . . . .26
3.1.1 von Neumann’s Work . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.1.2 Conway’s Life . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.1.3 Wolfram’s Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.2 Cellular Automata and the Definitive Parameters . . . . . . . . . . . . . . 31
3.2.1 Lattice Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.2.2 Cell Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.2.3 Guiding Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.2.4 Neighborhood Scheme . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.3 A Formal Definition of Cellular Automata . . . . . . . . . . . . . . . . . . 37
3.4 Elementary Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.5 Rule Families . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.6 Producing Randomness via Cellular Automata . . . . . . . . . . . . . . . 42
3.6.1 CA-Based PRNGs . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.6.2 Balancedness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.6.3 Mutual Information . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.6.4 Entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4 Test Results 47 4.1 Output of a Statistical Test . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.2 Testing Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.3 Interpretation of the Test Results . . . . . . . . . . . . . . . . . . . . . . . 49
4.3.1 Rate of success over all trials . . . . . . . . . . . . . . . . . . . . . 49
4.3.2 Distribution of P-values . . . . . . . . . . . . . . . . . . . . . . . . 50
4.4 Testing over a big space of functions . . . . . . . . . . . . . . . . . . . . . 50
4.5 Our Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.6 Results and Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.6.1 Change in State Width . . . . . . . . . . . . . . . . . . . . . . . . 53
4.6.2 Change in Neighborhood Scheme . . . . . . . . . . . . . . . . . . . 53
4.6.3 Entropy vs. Statistical Quality . . . . . . . . . . . . . . . . . . . . 58
4.6.4 Mutual Information vs. Statistical Quality . . . . . . . . . . . . . . 60
4.6.5 Entropy vs. Mutual Information . . . . . . . . . . . . . . . . . . . 62
4.6.6 Overall Test Results of 4- and 5-input CA . . . . . . . . . . . . . . 6
4.7 The simplest rule: 1435932310 . . . . . . . . . . . . . . . . . . . . . . . . . 68
5 Conclusion 74
A Test Results for Rule 30 and Rule 45 77
B 120 Rules with their Shortest Boolean Formulae 80
Bibliograph
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