Randomness Tests in Hostile Environments - 2018

PROJECT TITLE :

Randomness Tests in Hostile Environments - 2018

ABSTRACT:

An acceptable way to assess the standard of an RNG (PRNG) is to apply a customary battery of statistical randomness tests to a sampled output. Such tests compare some observed properties of the sample to properties of the same distribution, with the hope to detect deviations from the expected behavior. Think about a (P)RNG that outputs M-bit values which, because of a failure or an attack, are coerced to a subset of 0, 1M of only 2n parts, for a few n <; M. Such outputs are predictable with a chance of at least a pair of-n > two-M, however the standard randomness tests do not essentially detect this behavior. We show here deterministic M-bit sequences (M = 128) that belong to a subset of size 2n, however pass the DIEHARD Battery of Tests of Randomness [1] and the NIST Statistical Test Suite [2], even with a relatively small price of n = twenty nine. To address the difficulty, we have a tendency to propose a detection methodology that is feasible even for giant values of n (e.g., n = 64). As a sensible example, we have a tendency to apply our method to rule out the existence of the speculative stealthy hardware Trojan that's mentioned in [3].

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