Mobile advertisement fraud detection method based on heterogeneous graph embedding

A technology of mobile advertising and detection methods, which is applied in business, instrumentation, data processing applications, etc., can solve problems such as low efficiency and difficult graph structure data, and achieve a close relationship

Active Publication Date: 2019-08-30
SOUTH CHINA UNIV OF TECH
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  • Description
  • Claims
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Problems solved by technology

[0003] Traditional graph-structure-based analysis methods are inefficient in large-scale graphs, and existing effective solutions such as deep learning are difficult to directly apply to the ana

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  • Mobile advertisement fraud detection method based on heterogeneous graph embedding
  • Mobile advertisement fraud detection method based on heterogeneous graph embedding
  • Mobile advertisement fraud detection method based on heterogeneous graph embedding

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Embodiment Construction

[0035] The present invention will be further described in detail below in conjunction with the embodiments and the accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0036] like figure 1 As shown, the method for detecting mobile advertising fraud based on heterogeneous graph embedding provided in this embodiment, the specific steps include:

[0037] 1) Obtain mobile advertisement log data and preprocess the data.

[0038] In this embodiment, data preprocessing includes data cleaning and missing value filling; mobile advertisement log data contains four attributes: a, unique identification attributes: unique identifiers of users, applications, advertisements, etc.; b, time attributes: user usage The specific time when the application operation advertisement occurs, accurate to the second; c. Location attribute: identifies the geographical location of the user, such as the country and city where the user is located, and the IP address...

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Abstract

The invention discloses a mobile advertisement fraud detection method based on heterogeneous graph embedding. The method comprises the following steps of 1) acquiring the mobile advertisement log dataand preprocessing the data; 2) extracting the association relationship data of a user, an application and an advertisement, and constructing a weighted heterogeneous graph; 3) defining a meta-path, setting the walking frequency and the longest step length of each node, traversing the weighted heterogeneous graph nodes, and constructing a node meta-path random walking sequence; 4) constructing low-dimensional space dense vector representation of nodes in the weighted heterogeneous graph by using a language model; 5) defining a label to form tested data; 6) constructing a mobile advertisement fraud detection model; 7) inputting the mobile application tested data of the training part into a mobile advertisement fraud detection model for training to obtain a mobile advertisement fraud detection model, and 8) carrying out fraud detection on the mobile application by adopting the mobile advertisement fraud detection model, thereby effectively detecting the fraud mobile application by utilizing the entity association relationship in the mobile advertisement system.

Description

technical field [0001] The invention relates to the technical field of mobile application advertising fraud, in particular to a method for detecting mobile advertising fraud based on heterogeneous graph embedding. Background technique [0002] As a new marketing method based on smart terminals, mobile advertising has the characteristics of accuracy, interactivity, flexibility and personalization compared with traditional media. However, the ever-increasing advertising fraud poses a serious threat to the mobile advertising market, and it is very difficult to identify fraudulent behavior in mobile applications. Advertising fraud detection has become a hot issue in the mobile Internet advertising ecosystem. Due to its good representation ability and robustness to structured data, graph analytics methods based on graph-structured data are applied to anomaly and fraud detection. [0003] Traditional graph-structure-based analysis methods are inefficient in large-scale graphs, an...

Claims

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Application Information

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IPC IPC(8): G06Q30/02
CPCG06Q30/0242G06Q30/0248
Inventor 胡金龙庄懿陈浪黄旸珉黄松董守斌
Owner SOUTH CHINA UNIV OF TECH
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