Benford’s law in the Beale ciphers

Wase, Viktor

doi:10.1080/01611194.2020.1821409

Cited by 3 publications

(1 citation statement)

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“…Roukema, 2013) frauds. Other interesting applications are in image processing (Pérez-González et al, 2007), where Benford's law can be used to test whether or not the image has been compressed, in natural sciences, where the law has been shown to hold for geophysical observables such as the depths of earthquakes (Sambridge et al, 2010), and in cryptology, where it can be used to examine the truthfulness of undeciphered numerical codes (Wase, 2021, Campanelli, 2022b. 2 The aim of this paper is to assert if the data relative to the monkeypox daily counts in the United States of America (USA) comply or not with Benford's law.…”

Section: Introductionmentioning

confidence: 99%

Monkeypox obeys the (Benford) law: a dynamic analysis of daily case counts in the United States of America

Campanelli

2024

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We analyze, for the first time, the first-digit distribution of the monkeypox daily cases in the United States of America, from May 17 to September 21, 2022. The overall data follow Benford’s law, a conclusion substantiated by eight different statistical tests, including the “Euclidean distance test”, which has been designed to specifically check Benford’s distribution in data. This result aligns with those of other infectious diseases, such as COVID 19, whose Benfordness has already been confirmed in the literature. Daily counts of monkeypox cases, like any other disease evolve in time. For this reason, we analyzed the temporal deviation of monkeypox counts from Benford’s law to check for possible anomalies in the temporal series of cases. The dynamic analysis was performed by means of the Euclidean distance test. This is because, to our best knowledge, that is the only statistically valid, Benford-specific test whose underlying estimator has a cumulative distribution function with known analytical properties, and is applicable to small and large samples. This is the case in dynamic analyses, where the number of data points usually starts from small values and then increases in time. No anomalies were detected, which indicates that no (fraudulent) alterations or errors in data gathering took place.

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