Abnormal data detection method, device, storage medium and program product

A technology of abnormal data detection and normal data, applied in the field of deep learning, can solve the problems of low accuracy and difficult to achieve abnormal detection, and achieve the effect of solving difficult implementation, removing data distribution, and improving accuracy

CN107784322AActive Publication Date: 2018-03-09NEUSOFT CORP
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2018-03-09

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Abstract

The invention provides an abnormal data detection method, an abnormal data detection device, a storage medium and a program product. The method comprises the steps of conducting at least two structural treatments on to-be-detected target data so as to obtain at least two structured data; carrying out feature extraction on each structured data, and acquiring the feature data of each structured data; performing feature fusion on each feature data so as to obtain target feature data; carrying out machine learning on the target feature data, and obtaining the recognition probability of the targetdata, wherein the recognition probability represents the probability that the target data is recognized as normal data. By means of the method, rich feature data can be extracted when no or few abnormal data are available. The accuracy of data anomaly detection is improved. The dependence on data distribution, variable independence or data volume balance is relieved. The technical problems that inthe prior art, abnormal detection is difficult to achieve and the accuracy is low are solved.
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Description

technical field

[0001] The present invention relates to the technical field of deep learning, in particular to an abnormal data detection method, device, storage medium and program product. Background technique

[0002] Anomaly detection aims to detect data that does not meet expectations, and has a wide range of applications in fault detection, fraud detection, intrusion detection, and other fields, such as vehicle fault detection.

[0003] Existing anomaly detection methods can be divided into supervised learning methods and unsupervised learning methods. However, when using unsupervised learning methods for anomaly detection, it is necessary to establish a hypothetical model of normal data, such as assuming that the variables are independent of each other, and the unsupervised learning method extracts fewer data features, which cannot fully represent the data pattern, resulting in abnormal detection. The accuracy is low; the supervised learning method requires a balanced...

Examples

Embodiment Construction

[0078] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0079] The abnormal data detection method, device, storage medium, and program product in the embodiments of the present invention are described below with reference to the accompanying drawings.

[0080] In the real environment, there are generally only data patterns that meet expectations. Due to the high sampling cost of abnormal data patterns or the difficulty of sampling, the public knows little about abnormal behaviors, or even knows nothing about abnormal behaviors. Abnormal behaviors often contain significant, harmful or ...