Creating aggregate network flow time series in network anomaly detection systems

Patent metadata

Field	Value
Publication number	US-10356115-B2
Application number	US-201715475743-A
Country	US
Kind code	B2
Filing date	Mar 31, 2017
Priority date	Mar 31, 2017
Publication date	Jul 16, 2019
Grant date	Jul 16, 2019

Abstract

In an embodiment, a computer implemented method receives flow data for one or more flows that correspond to a device-circuit pair. The method calculates a time difference for each flow that corresponds to a device-circuit pair. Based on the calculated time differences and the received flow data, the method updates a probability distribution model associated with the device-circuit pair. Then, the method determines whether a time bucket is complete or open based on the updated probability distribution model.

First claim

What is claimed is:

1. A computer implemented method for processing network flow data over a time series associated with a device-circuit pair, comprising:
receiving, by a server, flow data for one or more flows that correspond to the device-circuit pair;
calculating, by the server, a time difference for each flow of the one or more flows that correspond to the device-circuit pair, wherein calculating the time difference for each flow is based on a start time and an end time of each flow at one of the device-circuit pair and a file stamp time of a network flow record indicating each flow received by the server;
based on the calculated time differences and the received flow data, updating, by the server, a probability distribution model associated with the device-circuit pair;
determining, by the server, whether a time bucket, of the time series, is complete or open based on the updated probability distribution model; and
detecting a network anomaly based on flow data corresponding to the time bucket, in response to determining that the time bucket is complete.

2. The method of claim 1 , further comprising:
in response to determining that the time bucket is complete, ignoring, by the server, additional flow data that corresponds to the time bucket; and
in response to determining that the time bucket is open, incorporating, by the server, additional flow data that corresponds to the time bucket.

3. The computer implemented method of claim 1 , wherein the probability distribution model comprises the received flow data that corresponds to the device-circuit pair and the time differences for the one or more flows that correspond to the device-circuit pair.

4. The computer implemented method of claim 3 , wherein the updating the probability distribution model comprises:
incorporating, by the server, the received flow data and the calculated time differences into the probability distribution model;
calculating, by the server, a mean value based on the time differences and the flow data included in the probability distribution model; and
calculating, by the server, a standard deviation value based on the time differences and the flow data included in the probability distribution model.

5. The computer implemented method of claim 4 , wherein the determining whether the time bucket is complete or open comprises:
calculating, by the server, a time delay value based on the standard deviation value; and
determining, by the server, whether the time bucket is complete or open based on the time delay value and the file stamp time.

6. The computer implemented method of claim 5 , wherein the calculating the time delay value comprises calculating the time delay value based on the standard deviation value and the mean value.

7. The computer implemented method of claim 5 , wherein the determining whether the time bucket is complete or open comprises:
creating an expiry time based on an end time of the time bucket and the calculated time delay value;
determining that the time bucket is complete if the file stamp time is beyond the created expiry time; and
determining that the time bucket is open if the file stamp time is not beyond the created expiry time.

8. A system for processing network flow data over a time series associated with a device-circuit pair, comprising:
a memory; and at least one processor coupled to the memory and configured to: receive flow data for one or more flows that correspond to the device-circuit pair;
calculate a time difference for each flow of the one or more flows that correspond to the device-circuit pair, wherein calculating the time difference for each flow is based on a start time and an end time of each flow at one of the device-circuit pair and a file stamp time of a network flow record indicating each flow received by the server;
based on the calculated time differences and the received flow data, update a probability distribution model associated with the device-circuit pair;
determine whether a time bucket, of the time series, is complete or open based on the updated probability distribution model; and
detect a network anomaly based on flow data corresponding to the time bucket, in response to determining that the time bucket is complete.

9. The system of claim 8 , wherein the at least one processor is further configured to:
in response to determining that the time bucket is complete, ignore additional flow data that corresponds to the time bucket; and
in response to determining that the time bucket is open, incorporate additional flow data that corresponds to the time bucket.

10. The system of claim 8 , wherein the probability distribution model comprises the received flow data that corresponds to the device-circuit pair and the time differences for the one or more flows that correspond to the device-circuit pair.

11. The system of claim 10 , wherein the at least one processor is further configured to update the probability distribution model by:
incorporating the received flow data and the calculated time differences into the probability distribution model;
calculating a mean value based on the time differences and the flow data included in the probability distribution model; and
calculating a standard deviation value based on the time differences and the flow data included in the probability distribution model.

12. The system of claim 11 , wherein the at least one processor is further configured to determine whether the time bucket is complete or open by:
calculating a time delay value based on the standard deviation value; and
determining whether the time bucket is complete or open based on the time delay value and the file stamp time.

13. The system of claim 12 , wherein the at least one processor is further configured to calculate the time delay value by calculating the time delay value based on the standard deviation value and the mean value.

14. The system of claim 12 , wherein the at least one processor is further configured to determine whether the time bucket is complete or open by:
creating an expiry time based on an end time of the time bucket and the calculated time delay value;
determining that the time bucket is complete if the file stamp time is beyond the created expiry time; and
determining that the time bucket is open if the file stamp time is not beyond the created expiry time.

15. A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one computing device, causes the at least one computing device to perform operations for processing network flow data over a time series associated with a device-circuit pair, comprising:
receiving flow data for one or more flows that correspond to the device-circuit pair;
calculating a time difference for each flow of the one or more flows that correspond to the device-circuit pair, wherein calculating the time difference for each flow is based on a start time and an end time of each flow at one of the device-circuit pair and a file stamp time of a network flow record indicating each flow received by the server;
based on the calculated time differences and the received flow data, updating a probability distribution model associated with the device-circuit pair;
determining whether a time bucket, of the time series, is complete or open based on the updated probability distribution model; and
detecting a network anomaly based on flow data corresponding to the time bucket, in response to determining that the time bucket is complete.

16. The non-transitory computer-readable medium of claim 15 , wherein the operations further comp

Assignees

• Level 3 Communications Llc

Inventors

• Yermakov Sergey

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• H04L63/1425 Primary

  Traffic logging, e.g. anomaly detection · CPC title

• H04L63/1458

  Denial of Service · CPC title

• H04L41/142

  using statistical or mathematical methods · CPC title

• H04L43/062

  related to network traffic · CPC title