12. A method for outputting a dataset based upon anomaly detection, the method comprising: receiving 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; receiving 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; 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; computing 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; computing 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; determining 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; selecting a subset of the first plurality of most-heavily weighted n-grams, wherein the subset includes m 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; receiving an input dataset including first input n-grams, wherein each of the plurality of first input n-grams is the first size; obtaining 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; classifying 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 outputting a dataset based upon the classifying of the input dataset.
12. A method for outputting a dataset based upon anomaly detection, the method comprising: receiving 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; receiving 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; 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; computing 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; computing 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; determining 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; selecting a subset of the first plurality of most-heavily weighted n-grams, wherein the subset includes m 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; receiving an input dataset including first input n-grams, wherein each of the plurality of first input n-grams is the first size; obtaining 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; classifying 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 outputting a dataset based upon the classifying of the input dataset. 14. The method of claim 12 , further comprising: receiving a third training dataset having a plurality of n-grams that includes a third plurality of distinct training n-grams, wherein each of the third plurality of distinct training n-grams is the first size and contains malicious code; computing 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; and determining a third plurality of most-heavily weighted n-grams from the third 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 third plurality of uniformities of distribution; and classifying the input dataset as containing an anomaly using a subset of the third plurality of most-heavily weighted n-grams and the subset of the second plurality of most-heavily weighted n-grams.