74. A system for outputting a dataset based upon anomaly detection, the system comprising: a digital processing, device that: receives a first 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; receives a second training dataset having a plurality of n-grams that includes a second plurality of distinct training n-grams, wherein each of the second plurality of distinct training n-grams is the 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; computes a first plurality of uniformities of distribution, wherein each of the first plurality of uniformities of distribution corresponds to one of the first plurality of distinct training n-grams; computes a second plurality of uniformities of distribution, wherein each of the second plurality of uniformities of distribution corresponds to one of the second plurality of distinct training n-grams; determines a first plurality of most-heavily weighted n-grams from the first plurality of distinct training n-grams using at least one of: the first-plurality of appearance frequencies; the first plurality of uniformities of distribution; and the second plurality of uniformities of distribution; selects a subset of the first plurality of most-heavily weighted n-grams, wherein the subset includes in n-grams and at least one of the n-grams in the subset is outside of the top m of the first plurality of most-heavily weighted n-grams; receives an input dataset including first input n-grams, wherein each of the plurality of first input n-grams is the first size; obtains a subset of a second plurality of most-heavily weighted n-grams from the first input n-grams that correspond to the subset of the first plurality of distinct training n-grams; classifies the input dataset as containing an anomaly using the subset of the first plurality of most-heavily weighted n-grams and the subset of the second plurality of most-heavily weighted n-grams; and outputs a dataset based upon the classifying of the input dataset.
75. The system of claim 74 , wherein the plurality of n-grams in the first training dataset also includes a third plurality of distinct training n-grams that are each a second size, the plurality of n-grams in the second training dataset also includes a fourth plurality of distinct training n-grams that are each the second size, and the input dataset also includes second input n-grams that are each the second size, wherein the digital processing device also: computes a second plurality of appearance frequencies, wherein each of the second plurality of appearance frequencies corresponds to one of the third plurality of distinct training n-grams; computes a third plurality of uniformities of distribution, wherein each of the third plurality of uniformities of distribution corresponds to one of the third plurality of distinct training n-grams; computes a fourth plurality of uniformities of distribution, wherein each of the fourth plurality of uniformities of distribution corresponds to one of the fourth plurality of distinct training n-grams; determines a third plurality of most-heavily weighted n-grams from the third plurality of distinct training n-grams using at least one of: the second plurality of appearance frequencies; the third plurality of uniformities of distribution; and the fourth plurality of uniformities of distribution; determines a fourth plurality of most-heavily weighted n-grams using the first plurality of most-heavily weighted n-grams and the third plurality of most-heavily weighted n-grams; and classifies the input dataset as containing an anomaly using the fourth plurality of most-heavily weighted n-grams.