1. A computer-implemented method of identifying anomalous entities in a dataset, comprising: using at least one hardware processor for executing a code for: selecting a subset stored in a hardware storage unit and comprising a plurality of training entities from a plurality of entities of at least one dataset; determining a plurality of dummy tuplets of entities in the subset by applying a permutation function on a plurality of real tuplets, wherein the real tuplets represent original and normal data of the at least one dataset, wherein the dummy tuplets represent anomalous data based on artificially created data not found in the original and normal at least one dataset, each one of the plurality of real tuplets and dummy tuplets comprises at least two of the plurality of training entities; analyzing the plurality of dummy tuplets and the plurality of real tuplets to identify at least one predefined characteristic relation that statistically differentiates between the real tuplets and the dummy tuplets according to a distinguishing requirement; and identifying, according to the identified at least one predefined characteristic relation, at least one of a normal entity and an anomalous entity in the at least one dataset or in a newly received dataset.