85. A system for outputting a dataset based upon anomaly detection, the system comprising: a digital processing device that: receives 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; computes 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; obtains a first pseudo count associated with the first plurality of appearance frequencies; computes 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; computes a first maximum possible count of distinct n-grams of the first size in the plurality of n-grams; computes a second total count of the first plurality of distinct training n-grams; computes a first smoothing factor; computes 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; computes 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; receives an input dataset including first input n-grams, wherein each of the first input n-grams is the first size; obtains a second consistency score of the first input n-grams; classifies the input dataset using the first consistency score and the second consistency score; and outputs a dataset based upon the classifying of the input dataset.
85. A system for outputting a dataset based upon anomaly detection, the system comprising: a digital processing device that: receives 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; computes 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; obtains a first pseudo count associated with the first plurality of appearance frequencies; computes 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; computes a first maximum possible count of distinct n-grams of the first size in the plurality of n-grams; computes a second total count of the first plurality of distinct training n-grams; computes a first smoothing factor; computes 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; computes 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; receives an input dataset including first input n-grams, wherein each of the first input n-grams is the first size; obtains a second consistency score of the first input n-grams; classifies the input dataset using the first consistency score and the second consistency score; and outputs a dataset based upon the classifying of the input dataset. 91. The system of claim 85 , wherein the training dataset comprises a first count of the top-most-frequently occurring n-grams extracted from network data traffic.