An anomaly detection method and apparatus based on kernel density estimation

A technique of kernel density estimation and density estimation, which is applied in computing, digital data information retrieval, special data processing applications, etc., can solve problems such as not having wide adaptability, and achieve the effect of wide adaptability

Inactive Publication Date: 2019-02-19
JINAN INSPUR HIGH TECH TECH DEV CO LTD
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Problems solved by technology

[0004] However, these algorithms are suitable for specific data set scenarios to find

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  • An anomaly detection method and apparatus based on kernel density estimation
  • An anomaly detection method and apparatus based on kernel density estimation
  • An anomaly detection method and apparatus based on kernel density estimation

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[0080] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work belong to the protection of the present invention. scope.

[0081] Such as figure 1 As shown, the embodiment of the present invention provides an anomaly detection method based on kernel density estimation, including:

[0082] Step 101: Acquire in advance at least three eigenvectors that have undergone data processing;

[0083] Step 102: Determine the density estimate corresponding to each of the feature vecto...

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Abstract

The invention provides an anomaly detection method and device based on kernel density estimation. The method comprises the following steps: acquiring at least three feature vectors subjected to data processing in advance; determining a density estimate corresponding to each of the feature vectors; determining a probability density function of the at least three eigenvectors based on each of the density estimates; obtaining a probability of occurrence of each of the feature vectors according to the probability density function; determining an offset corresponding to each of the probabilities; normalizing each of the offset quantities to obtain a corresponding standard value; determining whether each of the feature vectors is abnormal according to each of the standard values and a preset threshold value. The scheme has wide adaptability.

Description

technical field [0001] The invention relates to the technical field of data detection, in particular to an abnormality detection method and device based on kernel density estimation. Background technique [0002] With the development of information technology, the era of big data has come. In the fields of finance, network security, and the Internet, by learning a large amount of historical data, anomaly detection algorithms can distinguish normal data from abnormal data, so as to provide early warning for abnormal problems. [0003] Currently, commonly used density-based anomaly detection algorithms include the Local Outlier Factor (LOF) algorithm and its variants, such as the simplified-LOF algorithm, the LDF algorithm, and the LOOP algorithm. [0004] However, these algorithms are suitable for specific data set scenarios to find sparsely distributed points, that is, outliers, so they do not have wide adaptability. Contents of the invention [0005] Embodiments of the ...

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Application Information

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IPC IPC(8): G06F16/2458
Inventor 段强李锐于治楼
Owner JINAN INSPUR HIGH TECH TECH DEV CO LTD
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