Second-level NIST randomness tests for improving test reliability

Fabio Pareschi; Riccardo Rovatti; Gianluca Setti

doi:10.1109/iscas.2007.378572

Second-level NIST randomness tests for improving test reliability

Fabio Pareschi, Riccardo Rovatti, Gianluca Setti

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

37 Scopus citations

Abstract

Testing Random Number Generators (RNGs) is as important as designing them. Here we consider the NIST test suite SF 800-22 and we show that, as suggested by NIST itself, to reveal non-perfect generators a more in-depth analysis should be performed using the outcomes of the suite over many generated sequences. Testing these second-level statistics is not trivial and, relying on a proper model that takes into account the errors due to the approximations in the first level tests, we propose a tuning of the parameters in the simplest cases. The validity of our consideration is widely supported by experimental results on several RNG currently employed by major IT players, as well as a chaos-based RNG designed by authors. © 2007 IEEE.

Original language	English (US)
Title of host publication	Proceedings - IEEE International Symposium on Circuits and Systems
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1437-1440
Number of pages	4
DOIs	https://doi.org/10.1109/iscas.2007.378572
State	Published - Jan 1 2007
Externally published	Yes

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10.1109/iscas.2007.378572

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title = "Second-level NIST randomness tests for improving test reliability",

abstract = "Testing Random Number Generators (RNGs) is as important as designing them. Here we consider the NIST test suite SF 800-22 and we show that, as suggested by NIST itself, to reveal non-perfect generators a more in-depth analysis should be performed using the outcomes of the suite over many generated sequences. Testing these second-level statistics is not trivial and, relying on a proper model that takes into account the errors due to the approximations in the first level tests, we propose a tuning of the parameters in the simplest cases. The validity of our consideration is widely supported by experimental results on several RNG currently employed by major IT players, as well as a chaos-based RNG designed by authors. {\textcopyright} 2007 IEEE.",

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PY - 2007/1/1

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N2 - Testing Random Number Generators (RNGs) is as important as designing them. Here we consider the NIST test suite SF 800-22 and we show that, as suggested by NIST itself, to reveal non-perfect generators a more in-depth analysis should be performed using the outcomes of the suite over many generated sequences. Testing these second-level statistics is not trivial and, relying on a proper model that takes into account the errors due to the approximations in the first level tests, we propose a tuning of the parameters in the simplest cases. The validity of our consideration is widely supported by experimental results on several RNG currently employed by major IT players, as well as a chaos-based RNG designed by authors. © 2007 IEEE.

AB - Testing Random Number Generators (RNGs) is as important as designing them. Here we consider the NIST test suite SF 800-22 and we show that, as suggested by NIST itself, to reveal non-perfect generators a more in-depth analysis should be performed using the outcomes of the suite over many generated sequences. Testing these second-level statistics is not trivial and, relying on a proper model that takes into account the errors due to the approximations in the first level tests, we propose a tuning of the parameters in the simplest cases. The validity of our consideration is widely supported by experimental results on several RNG currently employed by major IT players, as well as a chaos-based RNG designed by authors. © 2007 IEEE.

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BT - Proceedings - IEEE International Symposium on Circuits and Systems

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Second-level NIST randomness tests for improving test reliability

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