Systems, methods, and media for outputting data based upon anomaly detection

A system for outputting data based on anomaly detection, comprising:

• a hardware processor configured to:
• receive an input dataset;
• identify commonly occurring grams in the input dataset that substantially include distinct byte values;
• create an input subset by removing the identified grams from the input dataset;
• determine whether the input dataset is likely to be anomalous based on the identified grams, and determines whether the input dataset is likely to be anomalous by applying the input subset to a binary anomaly detection model to check for an n-gram in the input subset, wherein the binary detection model is represented using a Bloom filter;
• and
• output the input dataset based on the likelihood that the input dataset is anomalous.

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;
• identifying commonly occurring grams in the input dataset that substantially include distinct byte values;
• creating an input subset by removing the identified grams from the input dataset;
• determining whether the input dataset is likely to be anomalous based on the identified grams, and determining whether the input dataset is likely to be anomalous by applying the input subset to a binary anomaly detection model to check for an n-gram in the input subset, wherein the binary detection model is represented using a Bloom filter;
• and
• outputting the input dataset based on the likelihood that the input dataset is anomalous.