CN110189167A - A kind of moving advertising fraud detection method based on the insertion of isomery figure - Google Patents
A kind of moving advertising fraud detection method based on the insertion of isomery figure Download PDFInfo
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Abstract
The invention discloses a kind of moving advertising fraud detection methods based on the insertion of isomery figure, comprising steps of 1) obtaining moving advertising daily record data and pre-processing to data；2) user, application and advertisement three's incidence relation data are extracted, isomery figure of having the right is constructed；3) first path is defined, the migration number and longest step-length of each node are set, traverses isomery node of graph of having the right, constructs node member path random walk sequence；4) language model is used, the dense vector of lower dimensional space for constructing isomery figure interior joint of having the right indicates；5) label is defined, subject data are constituted；6) moving advertising fraud detection model is constructed；7) the mobile application subject data of training part are input to moving advertising fraud detection model training, obtain moving advertising fraud detection model；8) fraud detection is carried out to mobile application using moving advertising fraud detection model.The present invention effectively detects the mobile application of fraud using the entity associated relationship in moving advertising system.
Description
Technical Field
The invention relates to the technical field of mobile application advertisement fraud, in particular to a mobile advertisement fraud detection method based on heterogeneous graph embedding.
Background
As a novel marketing mode depending on an intelligent terminal, the mobile advertisement has the characteristics of accuracy, interactivity, flexibility, individuation and the like compared with the traditional media. However, the continuous growth of advertisement fraud poses a serious threat to the mobile advertisement market, it is very difficult to identify the fraud of mobile applications, and advertisement fraud detection has become a hot problem to be solved urgently in the mobile internet advertisement ecosystem. Graph analysis methods based on graph structure data are applied to anomaly and fraud detection due to good representation capability and robustness of the structured data.
The traditional analysis method based on the graph structure has low efficiency in large-scale graphs, the existing effective schemes such as deep learning are difficult to be directly applied to analysis of graph structure data, and the graph embedding method learns effective vector representation in a low-dimensional space for nodes in the graphs, so that subsequent graph data analysis is better supported. Aiming at a complicated and variable mobile advertisement fraud means, how to utilize a graph embedding-based method to carry out efficient detection on fraud mobile application is a problem to be solved urgently.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a mobile advertisement fraud detection method based on heterogeneous graph embedding, which can improve the accuracy of mobile application advertisement fraud detection.
In order to achieve the purpose, the technical scheme provided by the invention is as follows: a mobile advertisement fraud detection method based on heterogeneous graph embedding comprises the following steps:
1) acquiring mobile advertisement log data, and preprocessing the data;
2) extracting incidence relation data of users, applications and advertisements in the mobile advertisement ecosystem, and constructing an authorized heterogeneous graph and a meta template corresponding to the authorized heterogeneous graph Andrespectively represent a category set and a relation category set of edges and satisfy
3) Defining meta-pathsSetting the number of wandering times n and the longest step length l of each node, traversing the nodes in the weighted heterogeneous graph G, and constructing n weighted random wandering paths S of the nodes vv＝{Sv1,Sv2,...,SvnFourthly, finally obtaining an element path random walk sequence S with the right different composition G;
4) constructing a language model, and learning d-dimensional space dense vector representation X belonging to R in P mobile application nodes in the weighted abnormal graph GP×dForming an input feature vector;
5) manually marking the mobile application of the training part, and setting a label value of each mobile application according to the information of whether the mobile application is a fraud application; the tag of the fraudulent application is set to 1 and the tag of the non-fraudulent application is set to 0, resulting in PtrainIndividual label dataPtrain＜P，PtrainApplying a total number P η of floating point numbers smaller than 1 and larger than 0 to the training part, and combining the floating point numbers with the corresponding input feature vectors in the step 4) to form tested data;
6) constructing a fraud detection model for detecting fraudulent mobile applications;
7) inputting the tested data into a fraud detection model, and acquiring parameters of the fraud detection model to obtain a mobile advertisement fraud detection model;
8) inputting the input characteristics of the mobile application which is not marked into the mobile advertisement fraud detection model to detect the fraud application.
In the step 1), the data preprocessing comprises data cleaning and missing value filling; the mobile ad log data contains four attributes: a. unique identification attribute: unique identifiers of users, applications, advertisements; b. the time attribute is as follows: the user uses the application to operate the specific time of the advertisement, and the time is accurate to the second; c. position attribute: identifying a geographic location at which the user is located; d. the device attribute is as follows: the model of the device used by the user, the size of the display screen, the operating system.
In step 2), the authorized heterogeneous graph G comprises three types of nodes which are user nodes respectivelyApplication nodeAnd advertising node
The authorized different composition G comprises three node relations which are respectively used by usersUser operated advertisementApplication display advertisementIts corresponding meta templateIndicating that there is a mapping function for any node V ∈ VAnd there is a mapping function for any connected edge
The weight of the edge between two adjacent nodes in the weighted abnormal graph G is determined by the corresponding operation information.
In step 3), a random walk sequence S of each node is constructedti1, 2., n, the sampling mode of the node is divided into two stages: the initial stage and the subsequent stage represent respectively the walk sequence StiLength of 0 to half element pathIn the migration stage and length betweenTo the migration phase between the longest migration step l.
Further, the walk probability of the initial stage of constructing the random walk sequence of each node is:
wherein,and vi+1∈Vt+1Respectively a current node and a next node,belongs to V for the current nodet+1A set of neighbor nodes of a type that,for meta-paths, φ () is a node type mapping function.
Further, the walk probability of the subsequent stage of the random walk sequence of each node is:
wherein,and vi+1∈Vt+1Respectively a current node and a next node,is the type of relationship with the current node and the next node, wiIs a relationship ofThe weight of the last edge of (c), β is the offset,for a set of neighbor nodes that are eligible,in order to be a meta-path,the function is mapped for edge type.
In step 4)In the method, the constructed language model is a Skip-gram model and is accelerated by using a negative sampling mode, and the number of negative samples of the negative sampling is fn；
The optimization function in the constructed Skip-gram model is as follows:
wherein v istFor given node v heterogeneous contextIn (3), theta is a parameter of the model, Xv,For nodes v and vtCorresponding low-dimensional node vector representation, XaIs a low-dimensional vector representation of any node in the graph.
Obtaining a dense vector representation in a low-dimensional space of nodes in the graph asWhere d is the vector dimension.
In step 6), the constructed fraud detection model is a classifier model, including a traditional machine learning model and a deep learning model.
Compared with the prior art, the invention has the following advantages and beneficial effects:
the invention captures richer structure and semantic relation by constructing a plurality of different composition graphs representing entity incidence relation in the mobile advertisement system; meanwhile, aiming at random walk of the meta-paths of a plurality of different composition graphs, the relationship between nodes with similar behaviors is tighter by adding weight constraint in the node propagation probability, so that the behavior information of the nodes can be better reflected by the vector obtained by embedding the graph, and the fraudulent mobile application can be effectively detected.
Drawings
FIG. 1 is a detailed flow chart of the method of the present invention.
FIG. 2 is a diagram of an all-rights heterogeneous graph.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited thereto.
As shown in fig. 1, the method for detecting fraud in mobile advertisement based on heterogeneous graph embedding provided in this embodiment includes the specific steps of:
1) and acquiring mobile advertisement log data and preprocessing the data.
In this embodiment, the data preprocessing includes data cleaning and missing value filling; the mobile ad log data contains four attributes: a. unique identification attribute: a unique identifier of a user, application, advertisement, etc.; b. the time attribute is as follows: the user uses the application to operate the specific time of the advertisement, and the time is accurate to the second; c. position attribute: identifying the geographical location of the user, such as the country and city of the user, the IP address used by the user, and the like; d. the device attribute is as follows: the model of the device used by the user, the size of the display screen, the operating system, etc. For example, user a clicked on advertisement D on mobile application C using device B at a certain point in time.
2) Extracting the incidence relation data of users, applications and advertisements in the mobile advertisement ecosystem, and constructing a meta template corresponding to a weighted heterogeneous graph G ═ V, E, M-The heterogeneous graph of all rights is shown in figure 2.
In this embodimentThe weighted heterogeneous graph G comprises three types of nodes which are user nodes respectivelyApplication nodeAnd advertising node
Further, the weighted heterogeneous graph G includes three node relationships, which are respectively used by the user to applyUser operated advertisementApplication display advertisementIts corresponding meta templateIndicating that there is a mapping function for any node V ∈ VAnd there is a mapping function for any connected edge
Further, in the present invention,andrespectively represent a category set and a relation category set of edges and satisfy
Furthermore, the weight of the edge between two neighboring nodes in the weighted differential graph is determined by the corresponding operation information, i.e. the weight of the edge between two neighboring nodes is determined by the ratio of the number of operation actions to the total number of operation actions.
3) The meta path is defined as follows:
setting the number of wandering times of each node as 30 and the longest step length as 40, traversing the nodes in the authorized heterogeneous composition, constructing 30 authorized random wandering paths of each node, and finally obtaining a meta-path random wandering sequence of the authorized heterogeneous composition;
in the present embodiment, a random walk sequence S of each node is constructedvi1, 2., the sampling mode of the node in 30 is divided into two stages: an initial stage and a subsequent stage representing a walk stage in which the length of the walk sequence is between 0 and length 2 of a half-element path and a walk stage in which the length is between 2 and the longest walk step 40, respectively;
further, the walk probability of the initial walk stage of the random walk sequence for each node is constructed as follows:
wherein,and vi+1∈Vt+1Respectively a current node and a next node,belongs to V for the current nodet+1A set of neighbor nodes of a type that,for meta-paths, φ () is a node type mapping function.
Further, the walk probability of the subsequent stage of the random walk sequence of each node is:
wherein,and vi+1∈Vt+1Respectively a current node and a next node,is the type of relationship with the current node and the next node, wiIs a relationship ofThe weight of the last edge of (c), β is the offset,for a set of neighbor nodes that are eligible,in order to be a meta-path,the function is mapped for edge type.
4) And (4) constructing a language model, learning the low-dimensional dense vector representation of each mobile application node in the weighted abnormal graph, and constructing an input feature vector.
Furthermore, the constructed language model is a Skip-gram model and is accelerated by using a negative sampling mode, and the number of negative samples of the negative sampling is fnIn the present embodiment, negativeThe number of the sampled negative samples is 5;
the optimization function in the Skip-gram model is as follows:
wherein v istFor given node v heterogeneous contextIn (3), theta is a parameter of the model, Xv,For nodes v and vtCorresponding low-dimensional node vector representation, XaIs a low-dimensional vector representation of any node in the graph.
Finally, the dense vector of the nodes in the graph in the low-dimensional space is expressed asWhere d is the vector dimension.
5) Manually marking the mobile application of the training part, and setting a label value of each mobile application according to the information of whether the mobile application is a fraud application; the tag of the fraudulent application is set to 1 and the tag of the non-fraudulent application is set to 0, resulting in PtrainIndividual label dataPtrain＜P，PtrainApplying a total number P η to the training part, and combining the total number P η with the corresponding input feature vector in the step (4) to form tested data;
in this example, η is 0.8.
6) A fraud detection model is constructed for detecting fraudulent mobile applications.
In this embodiment, the constructed fraud detection model is a random forest classifier model, and the main parameters of the model are as follows: the number of weak learners is 150, the maximum depth of each tree is 5, the minimum sample number of non-leaf node partition samples, namely leaf nodes, is 5, the out-of-bag score is used, the random state is set to be 10, the number of features is selected to be the square root of the number of original features, and the others are model default values.
7) And inputting the tested data into the fraud detection model, and acquiring parameters of the fraud detection model to obtain the mobile advertisement fraud detection model.
8) Inputting the input characteristics of the mobile application which is not marked into the mobile advertisement fraud detection model to detect the fraud application.
In the embodiment, the input characteristics of the target mobile application are input into the random forest model to obtain a real number py of 0-1, which represents the probability that the target mobile application is a fraud application. And setting the threshold value tau to be 0.5, if py is larger than tau, the target mobile application is a fraud application, and otherwise, the target mobile application is a normal application.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (8)
1. A mobile advertisement fraud detection method based on heterogeneous graph embedding is characterized by comprising the following steps:
1) acquiring mobile advertisement log data, and preprocessing the data;
2) extracting incidence relation data of users, applications and advertisements in the mobile advertisement ecosystem, and constructing an authorized heterogeneous graph and a meta template corresponding to the authorized heterogeneous graph Andrespectively represent a category set and a relation category set of edges and satisfy
3) Defining meta-pathsSetting the number of wandering times n and the longest step length l of each node, traversing the nodes in the weighted heterogeneous graph G, and constructing n weighted random wandering paths S of the nodes vv＝{Sv1,Sv2,...,SvnFourthly, finally obtaining an element path random walk sequence S with the right different composition G;
4) constructing a language model, and learning d-dimensional space dense vector representation X belonging to R in P mobile application nodes in the weighted abnormal graph GP ×dForming an input feature vector;
5) manually marking the mobile application of the training part, and setting a label value of each mobile application according to the information of whether the mobile application is a fraud application; the tag of the fraudulent application is set to 1 and the tag of the non-fraudulent application is set to 0, resulting in PtrainIndividual label dataPtrain＜P，PtrainApplying a total number P η of floating point numbers smaller than 1 and larger than 0 to the training part, and combining the floating point numbers with the corresponding input feature vectors in the step 4) to form tested data;
6) constructing a fraud detection model for detecting fraudulent mobile applications;
7) inputting the tested data into a fraud detection model, and acquiring parameters of the fraud detection model to obtain a mobile advertisement fraud detection model;
8) inputting the input characteristics of the mobile application which is not marked into the mobile advertisement fraud detection model to detect the fraud application.
2. The mobile advertising fraud detection method based on heterogeneous graph embedding of claim 1, wherein in step 1), the data preprocessing includes data cleaning and missing value filling; the mobile ad log data contains four attributes: a. unique identification attribute: unique identifiers of users, applications, advertisements; b. the time attribute is as follows: the user uses the application to operate the specific time of the advertisement, and the time is accurate to the second; c. position attribute: identifying a geographic location at which the user is located; d. the device attribute is as follows: the model of the device used by the user, the size of the display screen, the operating system.
3. The method of claim 1, wherein the mobile advertising fraud detection method based on the heterogeneous graph embedding is characterized in that: in step 2), the authorized heterogeneous graph G comprises three types of nodes which are user nodes respectivelyApplication nodeAnd advertising node
The authorized different composition G comprises three node relations which are respectively used by usersUser operated advertisementApplication display advertisementIts corresponding meta templateIndicating that there is a mapping function for any node V ∈ VAnd there is a mapping function for any connected edge
The weight of the edge between two adjacent nodes in the weighted abnormal graph G is determined by the corresponding operation information.
4. The method of claim 1, wherein the mobile advertising fraud detection method based on the heterogeneous graph embedding is characterized in that: in step 3), a random walk sequence S of each node is constructedti1, 2., n, the sampling mode of the node is divided into two stages: the initial stage and the subsequent stage represent respectively the walk sequence StiLength of 0 to half element pathIn the migration stage and length betweenTo the migration phase between the longest migration step l.
5. The method of claim 4, wherein the mobile advertising fraud detection method based on the heterogeneous graph embedding is characterized in that: the wandering probability of the initial stage of constructing the random wandering sequence of each node is as follows:
wherein,and vi+1∈Vt+1Respectively a current node and a next node,belongs to V for the current nodet+1A set of neighbor nodes of a type that,for meta-paths, φ () is a node type mapping function.
6. The method of claim 4, wherein the mobile advertising fraud detection method based on the heterogeneous graph embedding is characterized in that: the walk probability of the subsequent stage of the random walk sequence of each node is:
wherein,and vi+1∈Vt+1Respectively a current node and a next node,is the type of relationship with the current node and the next node, wiIs a relationship ofThe weight of the last edge of (c), β is the offset,for a set of neighbor nodes that are eligible,in order to be a meta-path,the function is mapped for edge type.
7. The method for detecting fraud in mobile advertisement based on embedding of heterogeneous graph according to claim 1, characterized in that: in the step 4), the constructed language model is a Skip-gram model and is accelerated by using a negative sampling mode, and the number of negative samples of negative sampling is fn；
The optimization function of the constructed Skip-gram model is as follows:
wherein v istFor a given node v a heterogeneous context Nt(v),A node in (1); theta is a parameter of the model, Xv,For nodes v and vtA corresponding low-dimensional node vector representation; xaA vector representation of a low dimension of any node in the graph;
finally, the dense vector of the nodes in the graph in the low-dimensional space is expressed asWhere d is the vector dimension.
8. The method of claim 1, wherein the mobile advertising fraud detection method based on the heterogeneous graph embedding is characterized in that: in step 6), the constructed fraud detection model is a classifier model, including a traditional machine learning model and a deep learning model.
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| CN113706279B (en) * | 2021-06-02 | 2024-04-05 | 同盾科技有限公司 | Fraud analysis method, fraud analysis device, electronic equipment and storage medium |
| CN113553446A (en) * | 2021-07-28 | 2021-10-26 | 厦门国际银行股份有限公司 | Financial anti-fraud method and device based on heteromorphic graph deconstruction |
| CN113553446B (en) * | 2021-07-28 | 2022-05-24 | 厦门国际银行股份有限公司 | Financial anti-fraud method and device based on heterograph deconstruction |
| CN113656797A (en) * | 2021-10-19 | 2021-11-16 | 航天宏康智能科技(北京)有限公司 | Behavior feature extraction method and behavior feature extraction device |
| CN113656797B (en) * | 2021-10-19 | 2021-12-21 | 航天宏康智能科技(北京)有限公司 | Behavior feature extraction method and behavior feature extraction device |
| CN114528479A (en) * | 2022-01-20 | 2022-05-24 | 华南理工大学 | Event detection method based on multi-scale different composition embedding algorithm |
| CN114528479B (en) * | 2022-01-20 | 2023-03-21 | 华南理工大学 | Event detection method based on multi-scale heteromorphic image embedding algorithm |
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