23. A method for outputting a dataset based upon anomaly detection, the method comprising: receiving a training dataset having a plurality of n-grams that includes a first plurality of distinct training n-grams, wherein each of the first plurality of distinct training n-grams is a first size; computing a first plurality of appearance frequencies, wherein each of the first plurality of appearance frequencies corresponds to one of the first plurality of distinct training n-grams; obtaining a first pseudo count associated with the first plurality of appearance frequencies; computing a first total count of the number of n-grams of the plurality of n-grams in the training dataset that are the first size; computing a first maximum possible count of distinct n-grams of the first size in the plurality of n-grams; computing a second total count of the first plurality of distinct training n-grams; computing a first smoothing factor; computing a first probability that the first plurality of distinct training n-grams are found in the training dataset using at least one of: the first plurality of appearance frequencies, the first pseudo count, the first total count, the second total count, and the first smoothing factor; computing a first consistency score of the plurality of n-grams in the training dataset that are the first size using the first maximum possible count and the first probability; receiving an input dataset including first input n-grams, wherein each of the first input n-grams is the first size; obtaining a second consistency score of the first input n-grams; classifying the input dataset using the first consistency score and the second consistency score; and outputting a dataset based upon the classifying of the input dataset.
23. A method for outputting a dataset based upon anomaly detection, the method comprising: receiving a training dataset having a plurality of n-grams that includes a first plurality of distinct training n-grams, wherein each of the first plurality of distinct training n-grams is a first size; computing a first plurality of appearance frequencies, wherein each of the first plurality of appearance frequencies corresponds to one of the first plurality of distinct training n-grams; obtaining a first pseudo count associated with the first plurality of appearance frequencies; computing a first total count of the number of n-grams of the plurality of n-grams in the training dataset that are the first size; computing a first maximum possible count of distinct n-grams of the first size in the plurality of n-grams; computing a second total count of the first plurality of distinct training n-grams; computing a first smoothing factor; computing a first probability that the first plurality of distinct training n-grams are found in the training dataset using at least one of: the first plurality of appearance frequencies, the first pseudo count, the first total count, the second total count, and the first smoothing factor; computing a first consistency score of the plurality of n-grams in the training dataset that are the first size using the first maximum possible count and the first probability; receiving an input dataset including first input n-grams, wherein each of the first input n-grams is the first size; obtaining a second consistency score of the first input n-grams; classifying the input dataset using the first consistency score and the second consistency score; and outputting a dataset based upon the classifying of the input dataset. 27. The method of claim 23 , wherein obtaining the second consistency score of the first input n-grams comprises: identifying a plurality of matching n-grams from the first input n-grams that correspond to the first plurality of distinct training n-grams; computing a second plurality of appearance frequencies, wherein each of the second plurality of appearance frequencies corresponds to one of the plurality of matching n-grams; obtaining a second pseudo count associated with the second plurality of appearance frequencies; computing a third total count of the first input n-grams; computing a third maximum possible count of distinct n-grams of the first input n-grams; computing a fourth total count of distinct n-grams of the first input n-grams; computing a second smoothing factor; computing a second probability that the distinct n-grams of the first input n-grams are found in the input dataset using at least one of: the second plurality of appearance frequencies, the second pseudo count, the third total count, the fourth total count, and the second smoothing factor; and computing a third consistency score of the first input n-grams using the third maximum possible count and the second probability.