66. The system of claim 64 , wherein the plurality of n-grams in the training dataset also includes a second plurality of distinct training n-grams that are each a second size, and the input dataset also includes second input n-grams that are each the second size, and wherein the digital processing device also: computes a second plurality of appearance frequencies, wherein each of the first plurality of appearance frequencies corresponds to one of the second plurality of distinct training n-grams; identifies third matching n-grams by determining whether the second input n-grams in the first window correspond to one of the second plurality of distinct training n-grams; computes a third anomaly detection score for the input dataset using the third matching n-grams and the second plurality of appearance frequencies; and determines based upon the third anomaly detection score whether the input dataset contains an anomaly.
66. The system of claim 64 , wherein the plurality of n-grams in the training dataset also includes a second plurality of distinct training n-grams that are each a second size, and the input dataset also includes second input n-grams that are each the second size, and wherein the digital processing device also: computes a second plurality of appearance frequencies, wherein each of the first plurality of appearance frequencies corresponds to one of the second plurality of distinct training n-grams; identifies third matching n-grams by determining whether the second input n-grams in the first window correspond to one of the second plurality of distinct training n-grams; computes a third anomaly detection score for the input dataset using the third matching n-grams and the second plurality of appearance frequencies; and determines based upon the third anomaly detection score whether the input dataset contains an anomaly. 67. The system of claim 66 , wherein the first size and the second size are randomly or pseudo-randomly chosen.