34. A non-transitory computer-readable medium containing computer-executable instructions that, when executed by a processor, cause the processor to perform a method for outputting data based on anomaly detection, the method comprising: receiving an input dataset; generating n-grams of different sizes from the input dataset; counting the number of distinct n-grams in the n-grams of different sizes that are not present in a binary anomaly detection model; computing an anomaly score based upon the number of distinct n-grams and a total count of the n-grams in the input dataset; using the anomaly score to determine whether an input dataset contains an anomaly; and outputting the input dataset based on whether the input dataset contains an anomaly.
34. A non-transitory computer-readable medium containing computer-executable instructions that, when executed by a processor, cause the processor to perform a method for outputting data based on anomaly detection, the method comprising: receiving an input dataset; generating n-grams of different sizes from the input dataset; counting the number of distinct n-grams in the n-grams of different sizes that are not present in a binary anomaly detection model; computing an anomaly score based upon the number of distinct n-grams and a total count of the n-grams in the input dataset; using the anomaly score to determine whether an input dataset contains an anomaly; and outputting the input dataset based on whether the input dataset contains an anomaly. 35. The medium of claim 34 , wherein the binary anomaly detection model is represented using a Bloom filter.