Evaluation of Randomness Test Results for Short Sequences

Sulak, Fatih; Doğanaksoy, Ali; Ege, Barış; Koçak, Onur

doi:10.1007/978-3-642-15874-2_27

Cited by 21 publications

(17 citation statements)

References 1 publication

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“…For short sequences, the NIST suggests that asymptotic distribution would be inappropriate and would need to be replaced by exact distributions that, according to them, would commonly be difficult to compute. Thus, [23] [7] [1] introduce new tests with their exact distribution, and [24] suggests a new method to take the decision of randomness for short sequences. Unfortunately, although these approaches can deal with short sequence, they require a significantly large set of such sequences.…”

Section: Statistical Tests For (Pseudo-) Random Generatorsmentioning

confidence: 99%

Memory Carving in Embedded Devices: Separate the Wheat from the Chaff

Gougeon

Barbier

Lacharme

et al. 2016

Applied Cryptography and Network Security

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Abstract. This paper investigates memory carving techniques for embedded devices. Given that cryptographic material in memory dumps makes carving techniques inefficient, we introduce a methodology to distinguish meaningful information from cryptographic material in smallsized memory dumps. The proposed methodology uses an adaptive boosting technique with statistical tests. Experimented on EMV cards, the methodology recognized 92% of meaningful information and 98% of cryptographic material.

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Section: Statistical Tests For (Pseudo-) Random Generatorsmentioning

confidence: 99%

Memory Carving in Embedded Devices: Separate the Wheat from the Chaff

Gougeon

Barbier

Lacharme

et al. 2016

Applied Cryptography and Network Security

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“…This method is used for testing the randomness of Advanced Encryption Standard candidates. Another method has been proposed by Sulak et al [16], in which distribution functions are used in NIST test suite, replaced by exact distribution and a similar method is used for producing the p values.…”

Section: Run Test Run Tests Depend Onmentioning

confidence: 99%

“…In this paper we use the method stated in [16]; thus we need the exact probabilities and exact distribution of tests statistics. Finding the number of sequences having a specified number of runs of length is a hard problem.…”

Section: Run Test Run Tests Depend Onmentioning

confidence: 99%

New Statistical Randomness Tests Based on Length of Runs

Doğanaksoy

Sulak

Uğuz

et al. 2015

Mathematical Problems in Engineering

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Random sequences and random numbers constitute a necessary part of cryptography. Many cryptographic protocols depend on random values. Randomness is measured by statistical tests and hence security evaluation of a cryptographic algorithm deeply depends on statistical randomness tests. In this work we focus on statistical distributions of runs of lengths one, two, and three. Using these distributions we state three new statistical randomness tests. New tests useχ2distribution and, therefore, exact values of probabilities are needed. Probabilities associated runs of lengths one, two, and three are stated. Corresponding probabilities are divided into five subintervals of equal probabilities. Accordingly, three new statistical tests are defined and pseudocodes for these new statistical tests are given. New statistical tests are designed to detect the deviations in the number of runs of various lengths from a random sequence. Together with some other statistical tests, we analyse our tests’ results on outputs of well-known encryption algorithms and on binary expansions ofe,π, and2. Experimental results show the performance and sensitivity of our tests.

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“…Soto et al concatenated the outputs of the algorithms to obtain long sequences in order to apply all the tests. Recently, Sulak et al proposed an alternative method where they computed and used the exact distributions instead of approximations or asymptotic distributions [24]. Having these exact probabilities, the necessity of long sequences was reduced, and they applied the randomness tests directly to the outputs of the algorithms instead of concatenating them.…”

Section: Introductionmentioning

confidence: 99%

New statistical randomness tests: 4-bit template matching tests

Sulak¹

2017

Turk J Math

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For cryptographic algorithms, secret keys should be generated randomly as the security of the system depends on the key and therefore generation of random sequences is vital. Randomness testing is done by means of statistical randomness tests. In this work, we show that the probabilities for the overlapping template matching test in the NIST test suite are only valid for a specific template and need to be recalculated for the other templates. We calculate the exact distribution for all 4-bit templates and propose new randomness tests, namely template matching tests. The new tests can be applied to any sequence of minimum length 5504 whereas the overlapping template matching test in the NIST test suite can only be applied to sequences of minimum length 10 6 . Moreover, we apply the proposed tests to biased nonrandom data and observe that the new tests detect the nonrandom behavior of the generator even for a bias of 0.001 , whereas the template matching tests in NIST cannot detect that bias.

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Evaluation of Randomness Test Results for Short Sequences

Cited by 21 publications

References 1 publication

Memory Carving in Embedded Devices: Separate the Wheat from the Chaff

Memory Carving in Embedded Devices: Separate the Wheat from the Chaff

New Statistical Randomness Tests Based on Length of Runs

New statistical randomness tests: 4-bit template matching tests

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