Systems, methods, and media for outputting a dataset based upon anomaly detection
Systems, methods, and media for outputting a dataset based upon anomaly detection are provided. In some embodiments, methods for outputting a dataset based upon anomaly detection: receive a training dataset having a plurality of n-grams, which plurality includes a first plurality of distinct training n-grams each being a first size; compute a first plurality of appearance frequencies, each for a corresponding one of the first plurality of distinct training n-grams; receive an input dataset including first input n-grams each being the first size; define a first window in the input dataset; identify as being first matching n-grams the first input n-grams in the first window that correspond to the first plurality of distinct training n-grams; compute a first anomaly detection score for the input dataset using the first matching n-grams and the first plurality of appearance frequencies; and output the input dataset based on the first anomaly detection score.
1. A method for outputting a dataset based upon anomaly detection, the method comprising:
receiving, using a hardware processor, an input dataset;
determining, using the hardware processor, an anomaly detection score by comparing n-grams that each represent a sequence of n bytes in the input dataset with training n-grams, wherein the anomaly detection score is indicative of the presence of anomalous n-grams in the input dataset; and
causing, using the hardware processor, the input dataset to be outputted based on the anomaly detection score.
2. The method of claim 1 , wherein the anomaly detection score is determined by applying a content anomaly detection model to the received input dataset.
3. The method of claim 2 , wherein the content anomaly detection model is a frequency distribution-based detection model that determines a plurality of appearance frequencies, and wherein each of the plurality of appearance frequencies corresponds to one of the training n-grams.
4. The method of claim 2 , wherein the content anomaly detection model is a binary-based detection model that determines a number of distinct n-grams in the input dataset and a total number of n-grams contained in the input dataset.
5. The method of claim 1 , wherein the training n-grams include a first plurality of distinct training n-grams and a second plurality of distinct training n-grams.
6. The method of claim 5 , further comprising:
determining a first plurality of appearance frequencies corresponding to the first plurality of distinct training n-grams; and
determining a second plurality of appearance frequencies corresponding to the second plurality of distinct training n-grams; and
determining a first anomaly detection score based on the first plurality of appearance frequencies and a second anomaly detection score based on the second plurality of appearance frequencies.
7. The method of claim 6 , further comprising:
determining which of the first anomaly detection score and the second anomaly detection score is higher; and
outputting the input dataset based on the anomaly detection score that is higher.
8. 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 by comparing n-grams that each represent a sequence of n bytes in the input dataset with training n-grams, wherein the anomaly detection score is indicative of the presence of anomalous n-grams in the input dataset; and
causes the input dataset to be outputted based on the anomaly detection score.
9. The system of claim 8 , wherein the anomaly detection score is determined by applying a content anomaly detection model to the received input dataset.
10. The system of claim 9 , wherein the content anomaly detection model is a frequency distribution-based detection model that determines a plurality of appearance frequencies, and wherein each of the plurality of appearance frequencies corresponds to one of the training n-grams.
11. The system of claim 9 , wherein the content anomaly detection model is a binary-based detection model that determines a number of distinct n-grams in the input dataset and a total number of n-grams contained in the input dataset.
12. The system of claim 8 , wherein the training n-grams include a first plurality of distinct training n-grams and a second plurality of distinct training n-grams.
13. The system of claim 12 , wherein the hardware processor is further configured to:
determine a first plurality of appearance frequencies corresponding to the first plurality of distinct training n-grams; and
determine a second plurality of appearance frequencies corresponding to the second plurality of distinct training n-grams; and
determining a first anomaly detection score based on the first plurality of appearance frequencies and a second anomaly detection score based on the second plurality of appearance frequencies.
14. The method of claim 13 , wherein the hardware processor is further configured to:
determine which of the first anomaly detection score and the second anomaly detection score is higher; and
output the input dataset based on the anomaly detection score that is higher.
15. 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, using a hardware processor, an input dataset;
determining, using the hardware processor, an anomaly detection score by comparing n-grams that each represent a sequence of n bytes in the input dataset with training n-grams, wherein the anomaly detection score is indicative of the presence of anomalous n-grams in the input dataset; and
causing, using the hardware processor, the input dataset to be outputted based on the anomaly detection score.
16. The non-transitory computer-readable medium of claim 15 , wherein the anomaly detection score is determined by applying a content anomaly detection model to the received input dataset.
17. The non-transitory computer-readable medium of claim 16 , wherein the content anomaly detection model is a frequency distribution-based detection model that determines a plurality of appearance frequencies, and wherein each of the plurality of appearance frequencies corresponds to one of the training n-grams.
18. The non-transitory computer-readable medium of claim 16 , wherein the content anomaly detection model is a binary-based detection model that determines a number of distinct n-grams in the input dataset and a total number of n-grams contained in the input dataset.
19. The non-transitory computer-readable medium of claim 15 , wherein the training n-grams include a first plurality of distinct training n-grams and a second plurality of distinct training n-grams.
20. The non-transitory computer-readable medium of claim 19 , wherein the method further comprises:
determining a first plurality of appearance frequencies corresponding to the first plurality of distinct training n-grams; and
determining a second plurality of appearance frequencies corresponding to the second plurality of distinct training n-grams; and
determining a first anomaly detection score based on the first plurality of appearance frequencies and a second anomaly detection score based on the second plurality of appearance frequencies.
21. The non-transitory computer-readable medium of claim 20 , wherein the method further comprises:
determining which of the first anomaly detection score and the second anomaly detection score is higher; and
outputting the input dataset based on the anomaly detection score that is higher.