1. A method for outputting data based on anomaly detection, comprising: receiving, using a hardware processor, an input dataset; identifying commonly occurring grams in the input dataset that substantially include distinct byte values; creating an input subset by removing the identified commonly occurring grams from the input dataset; determining whether the input dataset is likely to be anomalous by applying the input subset to a binary anomaly detection model that was generated using training datasets of known data to check for an n-gram in the input subset; and outputting the input dataset based on the likelihood that the input dataset is anomalous.