Method and device for determining data anomaly

Patent metadata

Field	Value
Publication number	US-10917424-B2
Application number	US-202016810961-A
Country	US
Kind code	B2
Filing date	Mar 6, 2020
Priority date	Dec 29, 2017
Publication date	Feb 9, 2021
Grant date	Feb 9, 2021

Abstract

Computer-implemented methods, non-transitory, computer-readable media, and computer-implemented systems for determination of anomalous data are provided. In a computer-implemented method, a plurality of data packets is received within a predetermined time period, the plurality of data packets comprising a data structure. A historical distribution of historical data including the data structure as the data packets is determined. The plurality of data packets is compared to the historical distribution to generate a comparison result. If it is determined that data anomaly exists in the plurality of data packets according to the comparison result, an alert indicating the data anomaly is generated.

First claim

What is claimed is:

1. A computer-implemented method for determination of anomalous data, the computer-implemented method comprising:
retrieving, by one or more processors, a plurality of data packets within a predetermined time period, the plurality of data packets comprising a data structure;
determining, by the one or more processors, a historical distribution, wherein the historical distribution is of historical data comprising the data structure as the data packets;
determining, by the one or more processors, a data distribution state of the plurality of data packets as a current distribution;
determining, by the one or more processors, a first distribution state parameter of a randomly selected data packet in the current distribution;
determining, by the one or more processors, a second distribution state parameter of the randomly selected data packet in the historical distribution;
comparing, by the one or more processors, the plurality of data packets with the historical distribution by determining a difference value between the first distribution state parameter and the second distribution state parameter to generate a comparison result;
determining, by the one or more processors, that the difference value exceeds a predetermined difference threshold;
in response to determining that the difference value exceeds the predetermined difference threshold, determining, by the one or more processors, that a data anomaly exists in the plurality of data packets; and
in response to determining that the data anomaly exists in the plurality of data packets, generating, by the one or more processors, an alert indicating the data anomaly.

2. The computer-implemented method of claim 1 , wherein comparing the plurality of data packets with the historical distribution comprises:
generating a plurality of distribution state parameters by substituting the plurality of data packets into the historical distribution; and
comparing the plurality of distribution state parameters with a predetermined threshold related to a distribution state to determine a number of data packets having a distribution state parameter exceeding the predetermined threshold.

3. The computer-implemented method of claim 1 , wherein comparing the plurality of data packets with the historical distribution comprises:
comparing the current distribution with the historical distribution.

4. The computer-implemented method of claim 3 , wherein comparing the current distribution with the historical distribution comprises:
determining a distribution center of the current distribution;
determining a distribution center of the historical distribution;
determining an offset between the distribution center of the current distribution and the distribution center of the historical distribution;
determining that the offset between the distribution center of the current distribution and the distribution center of the historical distribution exceeds a predetermined offset threshold; and
in response to determining that the offset exceeds the predetermined offset threshold, determining that the data anomaly exists in the plurality of data packets.

5. The computer-implemented method of claim 1 , wherein the historical distribution comprises a historical probability distribution determined by processing the historical data with a hybrid Gaussian model.

6. The computer-implemented method of claim 1 , wherein the historical distribution comprises a historical clustering distribution determined by processing the historical data with a clustering algorithm.

7. A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform one or more operations for determination of anomalous data, comprising:
retrieving a plurality of data packets within a predetermined time period, the plurality of data packets comprising a data structure;
determining a historical distribution, wherein the historical distribution is of historical data comprising the data structure as the data packets;
determining a data distribution state of the plurality of data packets as a current distribution;
determining a first distribution state parameter of a randomly selected data packet in the current distribution;
determining a second distribution state parameter of the randomly selected data packet in the historical distribution;
comparing the plurality of data packets with the historical distribution by determining a difference value between the first distribution state parameter and the second distribution state parameter to generate a comparison result;
determining that the difference value exceeds a predetermined difference threshold;
in response to determining that the difference value exceeds the predetermined difference threshold, determining that a data anomaly exists in the plurality of data packets; and
in response to determining that the data anomaly exists in the plurality of data packets, generating an alert indicating the data anomaly.

8. The non-transitory, computer-readable medium of claim 7 , wherein comparing the plurality of data packets with the historical distribution comprises:
generating a plurality of distribution state parameters by substituting the plurality of data packets into the historical distribution; and
comparing the plurality of distribution state parameters with a predetermined threshold related to a distribution state to determine a number of data packets having a distribution state parameter exceeding the predetermined threshold.

9. The non-transitory, computer-readable medium of claim 7 , wherein comparing the plurality of data packets with the historical distribution comprises:
comparing the current distribution with the historical distribution.

10. The non-transitory, computer-readable medium of claim 9 , wherein comparing the current distribution with the historical distribution comprises:
determining a distribution center of the current distribution;
determining a distribution center of the historical distribution;
determining an offset between the distribution center of the current distribution and the distribution center of the historical distribution;
determining that the offset between the distribution center of the current distribution and the distribution center of the historical distribution exceeds a predetermined offset threshold; and
in response to determining that the offset exceeds the predetermined offset threshold, determining that the data anomaly exists in the plurality of data packets.

11. The non-transitory, computer-readable medium of claim 7 , wherein the historical distribution comprises a historical probability distribution determined by processing the historical data with a hybrid Gaussian model.

12. The non-transitory, computer-readable medium of claim 7 , wherein the historical distribution comprises a historical clustering distribution determined by processing the historical data with a clustering algorithm.

13. A computer-implemented system for determination of anomalous data, comprising:
one or more computers; and
one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform one or more operations, comprising:
retrieving a plurality of data packets within a predetermined time period, the plurality of data packets comprising a data structure;
determining a historical distribution, wherein the historical distribution is of historical data comprising the data structure as the data packets;
determining a data dis

Assignees

• Advanced New Technologies Co Ltd

Inventors

• Wu Yuehao
• Xie Ling
• Xu Xin

Classifications

• G06F18/23

  Clustering techniques · CPC title

• G06N7/01

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

• G06F16/90335

  Query processing · CPC title

• H04L63/1425 Primary

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

• G06F21/64

  Protecting data integrity, e.g. using checksums, certificates or signatures · CPC title