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Random Number Generators--Pr
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“Book is in VERY GOOD, lightly used condition with no tears, creasing, or underlining. Pages are ”... Read moreAbout condition
Very Good
A book that has been read and does not look new, but is in excellent condition. No obvious damage to the book cover, with the dust jacket (if applicable) included for hard covers. No missing or damaged pages, no creases or tears, no underlining or highlighting of text, and no writing in the margins. Some identifying marks on the inside cover, but this is minimal. Very little wear and tear. See the seller’s listing for full details and description of any imperfections.
- Buy it nowRandom Number Generators--Principles And Practices by David Johnston 2018 PB/VG
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Located in: Owens Cross Roads, Alabama, United States
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eBay item number:226661064942
Item specifics
- Condition
- Very Good
- Seller notes
- ISBN
- 9781501515132
About this product
Product Identifiers
Publisher
DEG Press
ISBN-10
1501515136
ISBN-13
9781501515132
eBay Product ID (ePID)
6038736370
Product Key Features
Number of Pages
439 Pages
Language
English
Publication Name
Random Number Generators--Principles and Practices : a Guide for Engineers and Programmers
Publication Year
2018
Subject
Software Development & Engineering / Quality Assurance & Testing, Programming / Algorithms, Hardware / General, Security / Cryptography
Type
Textbook
Subject Area
Computers
Format
Trade Paperback
Dimensions
Item Weight
29 Oz
Item Length
9.4 in
Item Width
6.7 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2018-949266
Grade From
College Graduate Student
Illustrated
Yes
Grade To
College Graduate Student
Table Of Content
1 Introduction 1.1 Tools 1.2 Terminology 1.3 The Many Types of Random Numbers 1.3.1 Uniform Random Numbers 2 Random Number Generators 2.1 Classes of Random Number Generators 2.2 Names for RNGs 3 Making Random Numbers 3.1 A Quick Overview of the RNG Types 3.2 The Structure of Full RNG Implementations 3.3 Pool Extractor Structures 3.4 Multiple Input Extractors 4 Physically Uncloneable Functions 21 4.1 The other kind 'AS Static vs. Dynamic Random Number Generators . 5 Testing Random Numbers 5.1 Known Answer Tests 5.2 Distinguishing From Random 5.3 PRNG Test Suites 5.4 Entropy Measurements 5.5 Min Entropy Estimation 5.6 Model Equivalence Testing 5.7 Statistical Prerequisite Testing 5.8 The problem Distinguishing Entropy and Pseudo-randomness 5.9 PRNG Tests: DieHarder, NIST SP800-22,TestU01, China ICS 35.040 5.10 Entropy Measurements 5.11 Min Entropy Measurements 5.12 Modeling to Test a Source 5.13 Statistical Prerequisites 5.14 Testing for bias . 5.15 results that are 'AYtoo good'AZ (E.G. Chi-square == 0.5) 5.16 Distinguishing Correlation from Bias 5.17 Testing for Stationary properties 5.18 FFT analysis 5.19 Online Testing 5.20 Working From the Source RNG 5.21 Tools 5.22 Summary 6 Entropy Extraction or Distillation 6.1 A simple extractor, the XOR gate 6.2 A simple way of improving the distribution of random numbers that have known missing values using XOR 7 Quantifying Entropy 7.1 Rényi Entropy 7.2 Distance From Uniform Topics to put somewhere in the book- in existing chapters and new chapters 8.1 XOR as a 2 bit extractor 8.2 Properties of real random numbers 8.3 Binomial distributions 8.4 Normal distributions 8.4.1 Dice, more dice 8.4.2 Central limit theorem 8.5 Seeing patterns 8.6 Regression to the mean 8.7 Lack of correlation, bias, algorithmic connections, predictability 8.8 What's a True random number? 8.9 Random numbers in cryptography 8.10 Things they help with liveness, unpredictability, resistance to attacks 8.11 Examples of use 8.11.1 Salting Passwords . 8.11.2 802.11i exchange 8.11.3 PKMv2 exchange 8.11.4 Making Keys 8.12 Examples of RNG crypto failures 8.12.1 Sony PS3 attack 8.12.2 MiFare Classic 8.12.3 Online Poker 8.12.4 Debian OpenSSL Fiasco 8.12.5 Linux Boot Time Entropy 8.13 Humans and random numbers 8.14 Result of asking people for a random number 8.14.1 Normal People 8.14.2 Crypto People 8.15 Mental Random Number Tricks 8.15.1 How to think of a really random number 8.16 PRNGs 8.17 extractors 8.17.1 CBC MAC 8.17.2 BIW 8.17.3 Von Neumann 8.18 Extractor Theory 8.19 Random Number Standards 8.19.1 SP800-90A B C . 8.19.2 Ansi X9.82 8.20 PRNG Algorithms 8.20.1 SP800-90A CTR DRBG 8.20.2 SP800-90A SHA DRBG 8.20.3 XOR Construction 8.20.4 Oversampling Construction 8.21 Yarrow 8.22 Whirlpool 8.23 Linux Kernel random service 8.24 Appendices 8.25 Resources 8.25.1 SW Sources 8.25.2 Online random number sources 8.26 Example Algorithm Vectors 8.26.1 SP800-90A CTR DRBG 128 & 256 8.26.2 SP800-90A Hash DRBG SHA-1 & SHA 256 8.26.3 AES-CBC-MAC Conditioner 128 8.26.4 AES-CBC-MAC Conditioner 8.27 SP800-90 LZ Tests Issues
Synopsis
Random numbers come in many forms and distributions. Some represent measured data. Some are generated numbers intended to have a uniform distribution. Some are generated to have other distributions such as Gaussian (otherwise known as 'normal', binomial, logarithmic or one of many other distributions. They may be represented at decimal numbers or in another base such as hexadecimal which is common in computer science. They may be fractional or floating point. Often they are displayed graphically to show some property. In this book we take a look at some of the more common distributions and forms. Readers will learn: The different types of Random Number Generators The S/W tools used to generate random numbers Testing for randomness, Random Number Generators, Principles and Practices has been written for programmers, hardware engineers, and sophisticated hobbyists interested in understanding random numbers generators and gaining the tools necessary to work with random number generators with confidence and knowledge. Using an approach that employs clear diagrams and running code examples rather than excessive mathematics, random number related topics such as entropy estimation, entropy extraction, entropy sources, PRNGs, randomness testing, distribution generation, and many others are exposed and demystified. If you have ever Wondered how to test if data is really random Needed to measure the randomness of data in real time as it is generated Wondered how to get randomness into your programs Wondered whether or not a random number generator is trustworthy Wanted to be able to choose between random number generator solutions Needed to turn uniform random data into a different distribution Needed to ensure the random numbers from your computer will work for your cryptographic application Wanted to combine more than one random number generator to increase reliability or security Wanted to get random numbers in a floating point format Needed to verify that a random number generator meets the requirements of a published standard like SP800-90 or AIS 31 Needed to choose between an LCG, PCG or XorShift algorithm Then this might be the book for you.
LC Classification Number
(Q)
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- 0***d (320)- Feedback left by buyer.Past 6 monthsVerified purchaseItems arrived well packaged and shipped out same day as order. Items accurately described as depicted. Price was more than reasonable. Very pleased. Perfect transaction overall! Will shop again!
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