Abstract: The present disclosure relates to a method of anomaly detection using a trained artificial neural network (502) configured to implement at least an auto-associative function for replicating an input data sample at one or more outputs (A), the method comprising: a) injecting an input data sample into the trained artificial neural network (502) in order to generate a first replicated sample at the one or more outputs (A); b) performing at least one reinjection operation; c) computing a first parameter based on a distance between a value of an nth replicated sample present at the one or more outputs and a value of one of the previously injected or reinjected values; and d) comparing the first parameter with a first threshold (?), and processing the input data sample as an anomalous data sample if the first threshold is exceeded.