Randomness Tests in Hostile Environments - 2018


An acceptable means to assess the quality of an RNG (PRNG) is to use a normal battery of statistical randomness tests to a sampled output. Such tests compare some observed properties of the sample to properties of an identical distribution, with the hope to detect deviations from the expected behavior. Take into account a (P)RNG that outputs M-bit values that, due to a failure or an attack, are coerced to a subset of 0, 1M of only 2n elements, for some n <; M. Such outputs are predictable with a likelihood of at least a pair of-n > a pair of-M, but the standard randomness tests don't essentially detect this behavior. We tend to 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 [one] and also the NIST Statistical Check Suite [two], even with a comparatively small worth of n = 29. To address the issue, we propose a detection methodology that's possible even for massive values of n (e.g., n = 64). As a practical example, we tend to apply our method to rule out the existence of the speculative stealthy hardware Trojan that is discussed in [three].

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