Method and device for determining data anomaly

Patent metadata

Field	Value
Publication number	US-2020329063-A1
Application number	US-202016911078-A
Country	US
Kind code	A1
Filing date	Jun 24, 2020
Priority date	Dec 29, 2017
Publication date	Oct 15, 2020
Grant date	—

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

1 - 20 . (canceled)

21 . A computer-implemented method for determination of anomalous data, the computer-implemented method comprising:
processing, by one or more processors, data packets comprising a data structure to determine a historical distribution of historical data based on the data structure and a current distribution corresponding to a data distribution state of the data packets; 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 current distribution of the 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 data packets; and in response to determining that the data anomaly exists in the data packets, generating, by the one or more processors, an alert indicating the data anomaly.

22 . The computer-implemented method of claim 21 , wherein comparing the data packets with the historical distribution comprises:
generating a plurality of distribution state parameters by substituting the 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.

23 . The computer-implemented method of claim 21 , wherein comparing the data packets with the historical distribution comprises:
determining a data distribution state of the data packets as a current distribution; and comparing the current distribution with the historical distribution.

24 . The computer-implemented method of claim 23 , 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 data packets.

25 . The computer-implemented method of claim 23 , wherein comparing the current distribution with the historical distribution comprises:
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; determining a difference value between the first distribution state parameter and the second distribution state parameter; determining that the difference value exceeds a predetermined difference threshold; and in response to determining that the difference value exceeds the predetermined difference threshold, determining that the data anomaly exists in the data packets.

26 . The computer-implemented method of claim 21 , wherein the historical distribution comprises a historical probability distribution determined by processing the historical data with a hybrid Gaussian model.

27 . The computer-implemented method of claim 21 , wherein the historical distribution comprises a historical clustering distribution determined by processing the historical data with a clustering algorithm.

28 . 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:
processing data packets comprising a data structure to determine a historical distribution of historical data based on the data structure and a current distribution corresponding to a data distribution state of the data packets; 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 current distribution of the 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 data packets; and in response to determining that the data anomaly exists in the data packets, generating an alert indicating the data anomaly.

29 . The non-transitory, computer-readable medium of claim 28 , wherein comparing the data packets with the historical distribution comprises:
generating a plurality of distribution state parameters by substituting the 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.

30 . The non-transitory, computer-readable medium of claim 28 , wherein comparing the data packets with the historical distribution comprises:
determining a data distribution state of the data packets as a current distribution; and comparing the current distribution with the historical distribution.

31 . The non-transitory, computer-readable medium of claim 30 , 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 data packets.

32 . The non-transitory, computer-readable medium of claim 30 , wherein comparing the current distribution with the historical distribution comprises:
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; determining a difference value between the first distribution state parameter and the second distribution state parameter; determining that the difference value exceeds a predetermined difference threshold; and in response to determining that the difference value exceeds th

Assignees

• Alibaba Group Holding 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

• H04L41/142 Primary

  using statistical or mathematical methods · CPC title

• H04L43/16

  Threshold monitoring · CPC title