In previous papers we have addressed the problem of testing Random Number Generators (RNGs) through statistical tests, with particular emphasis on the approach we called second-level testing. We have shown that this approach is capable of achieving much higher accuracy in exposing nonrandom generators, but may suffer from reliability issues due to approximations introduced in the test. Here we consider the NIST Frequency Test and present a mathematical expression of the error introduced by approximating the effective discrete distribution function with its continuous limit distribution. The matching against experimental data is almost perfect
Abstract: The problem of generating sequences of uniformly distributed pseudorandom numbers is consi...
In many applications, for example cryptography and Monte Carlo simulation, there is need for random ...
none3noAs faster Random Number Generators become available, the possibility to improve the accuracy ...
In previous papers we have addressed the problem of testing Random Number Generators (RNGs) through ...
In previous papers we have addressed the problem of testing Random Number Generators (RNGs) through ...
Testing Random Number Generators (RNGs) is as important as designing them. Here we consider the NIST...
Testing random number generators (RNGs) is as important as designing them. The paper considers the N...
In this paper we review some statistical tests included in the NIST SP 800-22 suite, which is a coll...
In this paper we review some statistical tests included in the NIST SP 800-22 suite, which is a coll...
So-called Random number generators on computers are deterministic functions producing a sequence of ...
Random number generators are required for the operation of cryptographic information protection syst...
The use of second-level testing to reduce Type II errors in RNG validation was suggested from the ve...
Monte Carlo computations are considered easy to parallelize. However, the results can be adversely a...
<p>This data set contains the result of applying the NIST Statistical Test Suite on accelerometer da...
Random number generators (RNGs) are commonly used in simulations. The overlapping serial test is an ...
Abstract: The problem of generating sequences of uniformly distributed pseudorandom numbers is consi...
In many applications, for example cryptography and Monte Carlo simulation, there is need for random ...
none3noAs faster Random Number Generators become available, the possibility to improve the accuracy ...
In previous papers we have addressed the problem of testing Random Number Generators (RNGs) through ...
In previous papers we have addressed the problem of testing Random Number Generators (RNGs) through ...
Testing Random Number Generators (RNGs) is as important as designing them. Here we consider the NIST...
Testing random number generators (RNGs) is as important as designing them. The paper considers the N...
In this paper we review some statistical tests included in the NIST SP 800-22 suite, which is a coll...
In this paper we review some statistical tests included in the NIST SP 800-22 suite, which is a coll...
So-called Random number generators on computers are deterministic functions producing a sequence of ...
Random number generators are required for the operation of cryptographic information protection syst...
The use of second-level testing to reduce Type II errors in RNG validation was suggested from the ve...
Monte Carlo computations are considered easy to parallelize. However, the results can be adversely a...
<p>This data set contains the result of applying the NIST Statistical Test Suite on accelerometer da...
Random number generators (RNGs) are commonly used in simulations. The overlapping serial test is an ...
Abstract: The problem of generating sequences of uniformly distributed pseudorandom numbers is consi...
In many applications, for example cryptography and Monte Carlo simulation, there is need for random ...
none3noAs faster Random Number Generators become available, the possibility to improve the accuracy ...