Detecting Benford’s Law Effectiveness Threshold Differences According to Affecting Operation

Benford’s Law describes the effect of specific first significant digit probability distribution in natural datasets. In the case of non-natural or artificial intervention within such datasets, the first digit probability distribution tends to deviate from the theoretical distribution. Thus, Benford’s Law-based methods are useful in detecting unnatural changes in datasets indicating artificial manipulation of the original data. In our article, we first briefly describe the theory behind this law with an overview of Benford’s Law’s properties. We then focus on conformity tests for Benford’s Law as methods for data change detection compared with the original dataset. In our research, the datasets were collected from electricity consumption metering devices. We provide the results of conformity with Benford’s Law for affected datasets within a series of simulations with different affecting operations. We found a research gap when comparing the deviation from a theoretical first-digit probability distribution for different operations affecting the original dataset. We have made a series of simulations with different affecting operations and we tried to determine the effectiveness thresholds for each operation. As shown in the results section, different intervention operations manifest different specific thresholds of such deviations from Benford’s Law’s distribution.