1. A method for outputting a dataset based upon anomaly detection, the method comprising: receiving a training dataset having a plurality of n-grams, which plurality includes a first plurality of distinct training n-grams each being a first size; computing a first plurality of appearance frequencies, each for a corresponding one of the first plurality of distinct training n-grams; receiving an input dataset including first input n-grams each being the first size; defining a first window in the input dataset; identifying as being first matching n-grams the first input n-grams in the first window that correspond to the first plurality of distinct training n-grams; computing a first anomaly detection score for the input dataset using the first matching n-grams and the first plurality of appearance frequencies; and outputting the input dataset based on the first anomaly detection score.