Systems, methods, and media for outputting a dataset based upon anomaly detection

A system for outputting a dataset based upon anomaly detection, the system comprising:

• a hardware processor that:
• receives an input dataset;
• determines an anomaly detection score that is indicative of the presence of anomalous n-grams in the input dataset by applying a content anomaly detection model to the received input data, wherein the content anomaly detection model is trained with data that includes training n-grams and wherein the anomaly detection score is based on a comparison of n-grams in the input dataset, which each represent a sequence of n bytes, with the training n-grams;
• and
• causes the input dataset to be outputted based on the anomaly detection score.

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 a dataset based upon anomaly detection, the method comprising:

• receiving an input dataset;
• determining an anomaly detection score that is indicative of the presence of anomalous n-grams in the input dataset by applying a content anomaly detection model to the received input data, wherein the content anomaly detection model is trained with data that includes training n-grams and wherein the anomaly detection score is based on a comparison of n-grams in the input dataset, which each represent a sequence of n bytes, with the training n-grams;
• and
• causing the input dataset to be outputted based on the anomaly detection score.