CN110874786A - False transaction group identification method, equipment and computer readable medium - Google Patents
False transaction group identification method, equipment and computer readable medium Download PDFInfo
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Abstract
The application provides a false transaction group identification scheme, which comprises the steps of firstly obtaining a transaction characteristic information item set corresponding to a buyer of a service provider in a preset time period, wherein the elements of the transaction characteristic information item set are transaction characteristic information of transactions completed by the corresponding buyer, and can reflect the characteristics of each buyer of the service provider in the transaction process, then obtaining a maximum frequent item set of the service provider according to the transaction characteristic information item set, calculating the similarity between the transaction characteristic information item set corresponding to the buyer and the maximum frequent item set of the service provider, and judging whether a false transaction behavior exists according to the similarity and a first threshold value, so that a false transaction group is identified.
Description
Technical Field
The present application relates to the field of information technology, and in particular, to a method, device, and computer readable medium for identifying a false transaction group.
Background
When business expansion is carried out, the enterprise hires a service provider to carry out user and merchant expansion. In this process, the facilitator develops new users and merchants in various ways and facilitates transactions between buyers (users) and sellers (merchants), thereby bringing benefits to the enterprise. For transactions facilitated by the facilitator, the enterprise may be rewarded with a monetary amount from the facilitator. Under the drive of benefits, part of service merchants form a group with sellers and buyers, and cheat the awards given by the enterprises through a false transaction mode, so that the losses are caused to the enterprises. There is currently no good way for a facilitator to identify this behavior.
Content of application
An object of the present application is to provide a false transaction group identification scheme, so as to solve the problem that it is currently impossible to effectively identify a service provider to partner a seller and a buyer to perform a false transaction.
The embodiment of the application provides a false transaction group identification method, which comprises the following steps:
acquiring a transaction characteristic information item set corresponding to a buyer of a service provider within a preset time period, wherein the elements of the transaction characteristic information item set are transaction characteristic information of a transaction completed by the corresponding buyer;
acquiring a maximum frequent item set under the service provider according to the transaction characteristic information item set;
calculating the similarity between the transaction characteristic information item set corresponding to the buyer and the maximum frequent item set under the service provider;
and if the buyers with the similarity exceeding the first threshold exist, determining the service provider as a member of the false transaction group.
The embodiment of the application also provides a false transaction group identification device, which comprises:
the information acquisition module is used for acquiring a transaction characteristic information item set corresponding to a buyer of a service provider within a preset time period, wherein the elements of the transaction characteristic information item set are transaction characteristic information of a transaction completed by the corresponding buyer;
the calculation processing module is used for acquiring the maximum frequent item set under the facilitator according to the transaction characteristic information item set and calculating the similarity between the transaction characteristic information item set corresponding to the buyer and the maximum frequent item set under the facilitator;
and the identification module is used for determining the service provider as a member of the false transaction group when judging that the buyers with the similarity exceeding the first threshold exist.
Further, some embodiments of the present application also provide a computing device comprising a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein the computer program instructions, when executed by the processor, trigger the device to perform the fake transaction group identification method.
Still other embodiments of the present application provide a computer readable medium having computer program instructions stored thereon that are executable by a processor to implement the false deal group identification method.
In the false transaction group identification scheme provided in the embodiment of the application, a transaction characteristic information item set corresponding to a buyer of a service provider within a preset time period can be obtained, an element of the transaction characteristic information item set is transaction characteristic information of a transaction completed by the corresponding buyer, and characteristics of each buyer of the service provider in the transaction process can be reflected, then according to the transaction characteristic information item set, a maximum frequent item set of the service provider is obtained, similarity between the transaction characteristic information item set corresponding to the buyer and the maximum frequent item set of the service provider is calculated, and if a buyer with the similarity exceeding a first threshold exists, the service provider is determined as a member of the false transaction group. The expression form of the false transaction group is usually that a batch of buyers and a batch of sellers expanded by the same facilitator conduct centralized transaction, the transaction usually shows some specific patterns, for example, sellers of the transaction are similar, transaction amounts are similar, the most frequent item set can represent the specific patterns, therefore, the higher the similarity is, the more the buyers under the facilitator are in the transaction accord with the specific patterns of the false transaction group, and whether the facilitator has the behavior of false transaction can be identified.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is a representation of a transaction for a fraudulent transaction group;
fig. 2 is a process flow diagram of a false transaction group identification method according to an embodiment of the present application;
FIG. 3 is a flow chart of a process for identifying a fake transaction group using the scheme provided by an embodiment of the present application;
fig. 4 is a schematic structural diagram of a false transaction group identification device according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a computing device for identifying a fraudulent transaction group according to an embodiment of the present application;
the same or similar reference numbers in the drawings identify the same or similar elements.
Detailed Description
The present application is described in further detail below with reference to the attached figures.
In a typical configuration of the present application, the terminal, the devices serving the network each include one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, which include both non-transitory and non-transitory, removable and non-removable media, may implement the information storage by any method or technology. The information may be computer readable instructions, data structures, program means, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
The embodiment of the application provides a false transaction group identification method, which identifies whether false transactions exist or not based on the similarity between a maximum frequent item set and a transaction characteristic information item set corresponding to a buyer user under a service provider, and as the expression form of the false transaction group is often the transaction of a group of buyers and a group of sellers extended by the same service provider, as shown in fig. 1, some specific patterns are often presented during the transaction, for example, sellers of the transaction are similar, transaction amounts are similar, and the maximum frequent item set can represent the specific patterns, the higher the similarity is, the more the transaction of the buyer under the service provider is consistent with the specific pattern of the false transaction group, so that whether false transaction exists or not of the service provider can be effectively identified.
In an actual scenario, the execution subject of the method may be a user equipment, a network device, or a device formed by integrating the user equipment and the network device through a network, and may also be a program running in the above device. The user equipment comprises but is not limited to various terminal equipment such as a computer, a mobile phone and a tablet computer; including but not limited to implementations such as a network host, a single network server, multiple sets of network servers, or a cloud-computing-based collection of computers. Here, the Cloud is made up of a large number of hosts or web servers based on Cloud Computing (Cloud Computing), which is a type of distributed Computing, one virtual computer consisting of a collection of loosely coupled computers.
Fig. 2 shows a processing flow of a false transaction group identification method provided by an embodiment of the present application, which includes at least the following processing steps:
step S201, a transaction characteristic information item set corresponding to the buyer of the service provider within a preset time period is obtained. The preset time period can be set according to the requirements of an actual scene, a shorter time period can be set when the transaction occurs more frequently and the data is denser, otherwise, a longer time period can be set, so that sufficient samples are guaranteed for subsequent calculation, and the preset time period can be set to be N hours, N days or N months and the like according to the requirements.
The elements of the transaction characteristic information item set are transaction characteristic information of a transaction completed by a corresponding buyer, and the transaction characteristic information may be information capable of reflecting characteristics of the transaction, such as a transaction amount of the buyer or a seller transacting with the buyer.
Taking the buyer 1 and the buyer 2 under the facilitator a as an example, if the transaction characteristic information is adopted as the seller transacting with the buyer, and the sellers transacting with the buyer 1 within the preset time period include the seller 11, the seller 23 and the seller 12, the transaction characteristic information item set corresponding to the buyer 1 under the facilitator a within the preset time period is { seller 11, seller 23 and seller 12}, and the sellers transacting with the buyer 2 within the preset time period include the seller 11, the seller 24, the seller 14, the seller 13 and the seller 12, so that the transaction characteristic information item set corresponding to the buyer 2 under the facilitator a within the preset time period is { seller 11, seller 24, the seller 14, the seller 13 and the seller 12 }.
If the adopted transaction characteristic information is the transaction amount of the buyer, and the transaction amount of the transaction completed by the buyer 1 in the preset time period includes 4,5 and 6, the transaction characteristic information item set corresponding to the buyer 1 under the facilitator a in the preset time period is {4,5,6}, and the transaction amount of the transaction completed by the buyer 2 in the preset time period includes 6,7,8,9 and 10, then the transaction characteristic information item set corresponding to the buyer 2 under the facilitator a in the preset time period is {6,7,8,9,10 }.
In an actual scenario, when data acquisition is performed, a transaction characteristic information item set corresponding to buyers under a plurality of service providers can be simultaneously acquired, and during actual use, grouping can be performed according to the service providers, for example, data under the service provider a is grouped into Group1, data under the service provider B is grouped into Group2, and after the grouping is completed by analogy, each Group can be independently processed to judge whether each service provider has a behavior of false transaction and belongs to a member of a false transaction Group.
And step S202, acquiring the maximum frequent item set of the service provider according to the transaction characteristic information item set. The frequent item set refers to a set which frequently appears in the data set, the maximum frequent item set is a frequent item set without a superset, and the support degree can be used as a basis for judging whether one item set is the frequent item set, that is, the item set with the support degree greater than the support degree threshold value can be judged as the frequent item set. The Support (Support) can be defined as the probability of the simultaneous occurrence of elements in the sample item set, taking X and Y as examples, and the Support is:
Support(X,Y)＝P(XY)＝num(XY)/num(AllSamples)
wherein, Support (X, Y) represents the Support of the item set { X, Y }, p (xy) represents the probability of X and Y appearing in the sample item set at the same time, num (xy) represents the number of X and Y appearing in the sample item set at the same time, and num (allsamples) represents the number of the sample item set. Taking the service provider C as an example, the transaction characteristic information corresponding to the following buyers 1-4 is shown in the following table 1:
| service provider ID | Buyer ID | Seller ID |
| C | Buyer 1 | Seller 11, seller 23, seller 12 |
| C | Buyer 2 | Seller 11, seller 24, seller 14, seller 13 |
| C | Buyer 3 | Seller 11, seller 25, seller 14, seller 13 |
| C | Buyer 4 | Seller 11, seller 13, seller 12 |
TABLE 1
Therefore, the transaction characteristic information item sets corresponding to the buyers 1-4 under the service provider C are respectively as follows: { seller 11, seller 23, seller 12}, { seller 11, seller 24, seller 14, seller 13}, { seller 11, seller 25, seller 14, seller 13}, { seller 11, seller 13, seller 12}, the largest frequent item set that can be obtained by the facilitator C by calculation is { seller 11, seller 13, seller 14 }.
In some embodiments of the present application, an Apriori algorithm may be employed in obtaining the most frequent item set. Taking the seller corresponding to the buyers 1-4 of the service provider C as an example, the processing flow of determining the maximum frequent item set by using Apriori algorithm is as follows:
the support threshold is set to 50%, and the support of the candidate frequent 1 item set C1 is calculated as follows in table 2:
| item set | Degree of support |
| Seller 11 | 100％ |
| The seller 12 | 50％ |
| Seller 13 | 75％ |
| The seller 14 | 50％ |
| Seller 23 | 25％ |
| The seller 24 | 25％ |
| Seller 25 | 25％ |
TABLE 2
After comparison with the support threshold, a set of frequent 1 items L1 can be obtained as table 3 below:
| item set | Degree of support |
| Seller 11 | 100％ |
| The seller 12 | 50％ |
| Seller 13 | 75％ |
| The seller 14 | 50％ |
TABLE 3
A set C2 of candidate frequent 2-item sets is generated by connecting the frequent 1-item set itself in the set L1, and its support is calculated as shown in Table 4 below:
TABLE 4
After comparison with the support threshold, a set of frequent 2-item sets L2 can be obtained as table 5 below:
| item set | Degree of support |
| Seller 11, seller 12 | 50％ |
| Seller 11, seller 13 | 75％ |
| Seller 11, seller 14 | 50％ |
| Seller 13, seller 14 | 50％ |
TABLE 5
A set C3 of candidate frequent 3-item sets is generated by connecting the frequent 2-item sets themselves in set L2, and its support is computed as follows in Table 6:
| item set | Degree of support |
| Seller(s)11, 12, 13 | 25％ |
| Seller 11, seller 12, seller 14 | 0％ |
| Seller 11, seller 13, seller 14 | 50％ |
| Seller 12, seller 13, seller 14 | 0％ |
TABLE 6
After comparison with the support threshold, a set of frequent 3-item sets L3 can be obtained as table 7 below:
| item set | Degree of support |
| Seller 11, seller 13, seller 14 | 50％ |
TABLE 7
Therefore, according to the transaction characteristic information item set, the frequent item sets contained in the set for acquiring the transaction characteristic information item set corresponding to the buyer of the facilitator are all the item sets in the sets L1, L2 and L3, wherein the maximum frequent item set is { seller 11, seller 13 and seller 14 }.
Similarly, if the element in the transaction characteristic information item set is the transaction amount of the buyer, an Apriori algorithm may be used to obtain the maximum frequent item set of the facilitator.
Step S203, calculating the similarity between the transaction characteristic information item set corresponding to the buyer and the maximum frequent item set under the service provider. The most frequent item set represents transaction characteristic information that frequently appears together, and may be, for example, a set of sellers who frequently transact with buyers by the same facilitator, or a transaction amount frequently used by buyers by the same facilitator. The information can reflect specific modes of transaction between buyers and sellers under the service provider, such as transactions between buyers concentrated in specific sellers, transactions between a large number of buyers at specific prices, etc., which may be common modes of transactions between fraudulent groups of buyers. Therefore, the higher the similarity between the transaction characteristic information item set corresponding to the buyer and the maximum frequent item set, the higher the possibility that the transaction performed by the buyer and the corresponding facilitator is a false transaction, and thus the higher the availability of the facilitator and the buyers and sellers under the facilitator to participate in the false transaction.
In some embodiments of the present application, when calculating that the transaction characteristic information item set corresponding to the buyer is similar to the maximum frequent item set of the facilitator, a ratio of the number of elements of the first set to the number of elements of the second set may be determined as a similarity between the transaction characteristic information item set corresponding to the buyer and the maximum frequent item set of the facilitator. The first set is an intersection of a transaction characteristic information item set corresponding to a buyer and a maximum frequent item set under the service provider, and the second set is a union of the transaction characteristic information item set corresponding to the buyer and the maximum frequent item set under the service provider. Therefore, the similarity calculation method can be expressed by the following formula:
S＝|A∩B|/|A∪B|
wherein, S is the similarity, A is the transaction characteristic information item set corresponding to the buyer under the service provider, and B is the maximum frequent item set under the service provider.
For example, for the sample data of the aforementioned facilitator C, the transaction characteristic information item set corresponding to the buyer 1 is { seller 11, seller 23, seller 12}, and the maximum frequent item set is { seller 11, seller 13, seller 14}, so that the first set is an intersection { seller 11} of { seller 11, seller 23, seller 12} and { seller 11, seller 13, seller 14}, and the second set is a union { seller 11, seller 23, seller 12} and { seller 11, seller 13, seller 14}, where the number of elements of the first set and the number of elements of the second set are respectively 1 and 5, so that the similarity S can be calculated to be 0.2. Similarly, the similarity between the transaction characteristic information item sets corresponding to the buyers 2, 3 and 4 and the maximum frequent item set under the service provider can be calculated as follows: 0.75, 0.75 and 0.5, as shown in table 8 below:
TABLE 8
And step S204, if the buyers with the similarity exceeding the first threshold exist, determining the service provider as a member of the false transaction group. The first threshold is a preset similarity threshold, and if the similarity between the transaction characteristic information item set of a certain buyer and the maximum frequent item set of a facilitator is higher than the first threshold, the buyer and the facilitator corresponding to the buyer can be considered to perform false transaction. If the similarity corresponding to the buyers under one service provider does not exceed the first threshold, the service provider is indicated to have no behavior of false transaction.
Taking the aforementioned service provider C as an example, if the first threshold is set to 0.6 in this embodiment, two buyers with similarity exceeding the first threshold, namely buyer 2 and buyer 3, exist, so that it is known that the service provider C has a behavior of false transaction, and can be determined as a member of a false transaction group. If only the service provider needs to be identified whether the false transaction is carried out, only the service provider corresponding to the buyer is determined as a member of the false transaction group.
In other embodiments of the present application, the buyer with the similarity exceeding the first threshold and the seller who has completed the transaction with the buyer may also be simultaneously determined as members of the fake transaction group, so as to obtain a complete fake transaction group. For example, the buyer 2 and the buyer 3 under the facilitator C are buyers having a similarity exceeding a first threshold, the seller 11, the seller 24, the seller 14, and the seller 13 are sellers who have completed a transaction with the buyer 2, and the seller 11, the seller 25, the seller 14, and the seller 13 are sellers who have completed a transaction with the buyer 3, so that the members of the false transaction group can be determined as:
the service provider: { C }
Buyer list: { buyer 2, buyer 3}
Seller listing: { seller 11, seller 13, seller 14, seller 24, seller 25}
In a similar manner, processing can be performed separately based on sample data under each facilitator to identify whether each facilitator is a member of a spurious transaction group, as well as a complete member list of spurious transaction groups including buyers, sellers.
In a practical scenario, the maximum number of frequent itemsets of the service provider may be one, or there may be more. When there are multiple most frequent itemsets, this indicates that there may be as many false trade groups under the facilitator. Therefore, when the similarity between the transaction characteristic information item set corresponding to the buyer and the maximum frequent item set under the service provider is calculated, candidate groups with the same number as the maximum frequent item set can be constructed, and each candidate group corresponds to one maximum frequent item set under the service provider.
For example, if the most frequent item sets under facilitator D are { seller 11, seller 13, seller 14} and { seller 11, seller 22, seller 23}, respectively, then two candidate parties corresponding to the two most frequent item sets may be constructed. When building a candidate partnership, a partnership ID may be created for the candidate partnership, respectively, to facilitate distinguishing multiple candidate partnership in subsequent processing. In this embodiment, the group ID may be automatically generated based on the facilitator and the most frequent item set, for example, for two candidate groups under the facilitator D, the group IDs "D + seller 11, seller 13, seller 14", "D + seller 11, seller 22, seller 23" may be created.
After the candidate gangs are constructed, the similarity between the transaction characteristic information item set corresponding to the buyer and the maximum frequent item set corresponding to each candidate gangs is calculated respectively. If the buyers under the facilitator D are buyers 1-6 respectively, the similarity of the candidate group "D + seller 11, seller 13, seller 14" is 0.2, 0.4, 0.7, 0.9, 0.5 and 0.8 respectively, and the similarity of the candidate group "D + seller 11, seller 22, seller 23" is 0.9, 0.7, 0.8, 0.3 and 0.2 respectively, as shown in the following table 9:
| service provider ID | Buyer ID | Similarity 1 | Similarity 2 |
| D | Buyer 1 | 0.2 | 0.9 |
| D | Buyer 2 | 0.4 | 0.7 |
| D | Buyer 3 | 0.7 | 0.8 |
| D | Buyer 4 | 0.9 | 0.3 |
| D | Buyer 5 | 0.5 | 0.3 |
| D | Buyer 6 | 0.8 | 0.2 |
TABLE 9
The similarity 1 is the similarity between the buyers 1 to 6 and the candidate partners "D + seller 11, seller 13 and seller 14", and the similarity 2 is the similarity between the buyers 1 to 6 and the candidate partners "D + seller 11, seller 22 and seller 23".
At this time, when determining whether the service provider is a member of the false transaction group, the buyer may be classified as a candidate group with the highest similarity, and for any candidate group, if there is a buyer with the similarity exceeding a first threshold in the candidate group, the candidate group is determined as the false transaction group, and the service provider is determined as a member of the false transaction group. For example, for buyer 1 with a similarity 1 of 0.2 and a similarity 2 of 0.9, the buyer 1 will be categorized into the candidate group "D + seller 11, seller 22, seller 23", and the buyer 2-6 can be categorized into the candidate group in the same manner.
Then, for each candidate group, whether the candidate group is a false transaction group is judged according to the buyers with the similarity exceeding the first threshold. If at least one buyer with the similarity exceeding the first threshold exists in the candidate group, the candidate group is the false transaction group, and the corresponding service provider is the member of the false transaction group. If no buyer with the similarity exceeding the first threshold exists in the candidate group, the candidate group is not a false transaction group. Here, it should be understood by those skilled in the art that the order described in the embodiment is not used to limit the processing logic in actual execution, for example, a strict time sequence relationship does not exist between grouping buyers into a candidate group with the highest similarity and performing judgment based on the first threshold, and in an actual scenario, buyers with a similarity exceeding the first threshold may be screened first, and then candidate groups may be allocated to the screened buyers. Present or future implementations based on similar principles, if applicable to the present application, are intended to be included within the scope of the present application and are incorporated herein by reference.
In other embodiments of the present application, for each false transaction group, the buyer whose similarity exceeds the first threshold and the seller who completed the transaction with the buyer can also be simultaneously determined as members of the false transaction group, so as to obtain a complete false transaction group. Therefore, when a plurality of candidate groups exist, for any candidate group, the judgment can be carried out based on a first threshold, if buyers with the similarity exceeding the first threshold exist in the candidate group, the candidate group is determined as a false transaction group, and the facilitator, the buyers with the similarity exceeding the first threshold under the candidate group and the sellers who have completed the transaction with the buyers in the false transaction group are determined as members of the false transaction group.
In a practical scenario, information of the number of sellers transacted within the preset time period of the buyer can be obtained, and a scheme of false transaction group identification is optimized according to the information. Therefore, when the transaction characteristic information item set corresponding to the buyer of the service provider within the preset time period is obtained, the screening can be performed once based on the number of the sellers transacted within the preset time period. When the facilitator organizes the false transaction group, the number of sellers performing false transactions organized by the facilitator is generally not very large, so a second threshold value can be set, the number of sellers performing transactions within the preset time period is compared with the second threshold value, and if the second threshold value is exceeded, the possibility of performing false transactions with buyers performing transactions with such many sellers is considered to be not high, so that the false transactions can be eliminated. That is, in some embodiments of the present application, when acquiring a set of transaction characteristic information items corresponding to buyers in a preset time period, a set of transaction characteristic information items corresponding to buyers meeting an identification requirement in the preset time period in the service provider may be acquired, where the buyers meeting the identification requirement are the buyers of which the number of sellers transacting in the preset time period is less than the second threshold.
Fig. 3 shows a processing flow when a false transaction group is identified by using the scheme provided by the embodiment of the application, and sample data required to be prepared in the processing process includes: the seller and seller trading sets of the facilitator in the preset time period, the transaction amount sets of the facilitator in the preset time period, and the seller and seller trading numbers of the facilitator in the preset time period are shown in the following table 10:
watch 10
When false transaction groups are identified, the method comprises the following processing steps:
step S301, grouping according to service providers, the grouping result is shown in table 11 below:
TABLE 11
Step S302, generate the most frequent item set for the seller. Taking group1 as an example, the Apriori algorithm is adopted, and the maximum frequent item set can be determined as { seller 11, seller 12, seller 13, seller 14} according to the set of sellers corresponding to each seller.
Step S303, constructing candidate group ID according to the service provider and the maximum frequent item set. Using Group1 as an example, a Group ID for Group1 may be generated based on { A } and { seller 11, seller 12, seller 13, seller 14 }.
In step S304, the similarity between the seller set corresponding to the buyer and the most frequent item set under the group is calculated by using the aforementioned formula S ═ a ∩ B |/| a ∪ B |. for group1, the following calculation results are obtained as shown in table 12 below:
TABLE 12
Step S305, group creation. If the first threshold is set to 0.6, the similarity of all buyers in the group1 is greater than the first threshold, and since there is only one candidate group, it can be determined that the candidate group is a false transaction group, and the facilitator a and the buyers and sellers of the transaction are all members of the false transaction group. Thus, a complete set of parties is generated as follows:
{ A } - -facilitator
{ seller 11, seller 12, seller 13, seller 14} - -set of top-most frequent items
{ buyer 1, buyer 10, buyer 11, buyer 12, buyer 13, buyer 14, buyer 2, buyer 3, buyer 4, buyer 5, buyer 6, buyer 7, buyer 8, buyer 9} - -buyer LIST
{ seller 11, seller 12, seller 13, seller 14, seller 21, seller 22, seller 23, seller 24} - - -seller LIST
Based on the same inventive concept, the embodiment of the application also provides a false transaction group identification device, the corresponding method of the device is the false transaction group identification method in the previous embodiment, and the problem solving principle is similar to the method.
The embodiment of the application provides false transaction group identification equipment, which identifies whether false transactions exist or not based on the similarity between a maximum frequent item set and a transaction characteristic information item set corresponding to a buyer user under a service provider when false transaction group identification is realized, and as the expression form of the false transaction group is often a batch of concentrated transactions of buyers and a batch of sellers extended by the same service provider, as shown in fig. 1, some specific modes are often presented during transactions, for example, sellers of the transactions are similar, transaction amounts are similar, and the maximum frequent item set can represent the specific modes, so that the higher the similarity is, the more the transactions of the buyers under the service provider are in accordance with the specific mode of the false transaction group, and whether false transaction behaviors exist or not of the service provider can be effectively identified.
In a practical scenario, the false transaction group identification device may be a user device, a network device, or a device formed by integrating a user device and a network device through a network, and may also be a program running in the device. The user equipment comprises but is not limited to various terminal equipment such as a computer, a mobile phone and a tablet computer; including but not limited to implementations such as a network host, a single network server, multiple sets of network servers, or a cloud-computing-based collection of computers. Here, the Cloud is made up of a large number of hosts or web servers based on Cloud Computing (Cloud Computing), which is a type of distributed Computing, one virtual computer consisting of a collection of loosely coupled computers.
Fig. 4 shows the structure of a false transaction group identification device provided by an embodiment of the present application, which includes an information acquisition module 410, a calculation processing module 420, and an identification module 430. The information obtaining module 410 is configured to obtain a transaction feature information item set corresponding to a buyer of a service provider within a preset time period. The calculation processing module 420 is configured to obtain the maximum frequent item set of the facilitator according to the transaction characteristic information item set, and calculate a similarity between the transaction characteristic information item set corresponding to the buyer and the maximum frequent item set of the facilitator. The identifying module 430 is configured to determine the facilitator as a member of a fake transaction group when determining that there is a buyer with the similarity exceeding a first threshold.
The preset time period can be set according to the requirements of an actual scene, a shorter time period can be set when the transaction occurs more frequently and the data is denser, otherwise, a longer time period can be set, so that sufficient samples are ensured to perform subsequent calculation, for example, the time period can be set to be N hours, N days or N months as required.
The elements of the transaction characteristic information item set are transaction characteristic information of a transaction completed by a corresponding buyer, and the transaction characteristic information may be information capable of reflecting characteristics of the transaction, such as a transaction amount of the buyer or a seller transacting with the buyer.
Taking the buyer 1 and the buyer 2 under the facilitator a as an example, if the transaction characteristic information is adopted as the seller transacting with the buyer, and the sellers transacting with the buyer 1 within the preset time period include the seller 11, the seller 23 and the seller 12, the transaction characteristic information item set corresponding to the buyer 1 under the facilitator a within the preset time period is { seller 11, seller 23 and seller 12}, and the sellers transacting with the buyer 2 within the preset time period include the seller 11, the seller 24, the seller 14, the seller 13 and the seller 12, so that the transaction characteristic information item set corresponding to the buyer 2 under the facilitator a within the preset time period is { seller 11, seller 24, the seller 14, the seller 13 and the seller 12 }.
If the adopted transaction characteristic information is the transaction amount of the buyer, and the transaction amount of the transaction completed by the buyer 1 in the preset time period includes 4,5 and 6, the transaction characteristic information item set corresponding to the buyer 1 under the facilitator a in the preset time period is {4,5,6}, and the transaction amount of the transaction completed by the buyer 2 in the preset time period includes 6,7,8,9 and 10, then the transaction characteristic information item set corresponding to the buyer 2 under the facilitator a in the preset time period is {6,7,8,9,10 }.
In an actual scenario, when data acquisition is performed, a transaction characteristic information item set corresponding to buyers under a plurality of service providers can be simultaneously acquired, and during actual use, grouping can be performed according to the service providers, for example, data under the service provider a is grouped into Group1, data under the service provider B is grouped into Group2, and after the grouping is completed by analogy, each Group can be independently processed to judge whether each service provider has a behavior of false transaction and belongs to a member of a false transaction Group.
The frequent item sets obtained by the calculation processing module are sets which frequently appear in the data set, the maximum frequent item set is a frequent item set without a superset, and the support degree can be used as a basis for judging whether one item set is a frequent item set, namely, the item set with the support degree greater than the support degree threshold value can be judged as the frequent item set. The Support (Support) can be defined as the probability of the simultaneous occurrence of elements in the sample item set, taking X and Y as examples, and the Support is:
Support(X,Y)＝P(XY)＝num(XY)/num(AllSamples)
wherein, Support (X, Y) represents the Support of the item set { X, Y }, p (xy) represents the probability of X and Y appearing in the sample item set at the same time, num (xy) represents the number of X and Y appearing in the sample item set at the same time, and num (allsamples) represents the number of the sample item set. Taking the service provider C as an example, the following table 1 shows the transaction characteristic information corresponding to the lower buyers 1-4.
Therefore, the transaction characteristic information item sets corresponding to the buyers 1-4 under the service provider C are respectively as follows: { seller 11, seller 23, seller 12}, { seller 11, seller 24, seller 14, seller 13}, { seller 11, seller 25, seller 14, seller 13}, { seller 11, seller 13, seller 12}, the largest frequent item set that can be obtained by the facilitator C by calculation is { seller 11, seller 13, seller 14 }.
In some embodiments of the present application, the computation processing module may employ Apriori algorithm when obtaining the most frequent item set.
The most frequent item set represents transaction characteristic information that frequently appears together, and may be, for example, a set of sellers who frequently transact with buyers by the same facilitator, or a transaction amount frequently used by buyers by the same facilitator. The information can reflect specific modes of transaction between buyers and sellers under the service provider, such as transactions between buyers concentrated in specific sellers, transactions between a large number of buyers at specific prices, etc., which may be common modes of transactions between fraudulent groups of buyers. Therefore, the higher the similarity between the transaction characteristic information item set corresponding to the buyer and the maximum frequent item set, the higher the possibility that the transaction performed by the buyer and the corresponding facilitator is a false transaction, and thus the higher the availability of the facilitator and the buyers and sellers under the facilitator to participate in the false transaction.
In some embodiments of the present application, when the calculation processing module calculates that the transaction characteristic information item set corresponding to the buyer is similar to the maximum frequent item set of the facilitator, the calculation processing module may determine a ratio of the number of elements of the first set to the number of elements of the second set as a similarity between the transaction characteristic information item set corresponding to the buyer and the maximum frequent item set of the facilitator. The first set is an intersection of a transaction characteristic information item set corresponding to a buyer and a maximum frequent item set under the service provider, and the second set is a union of the transaction characteristic information item set corresponding to the buyer and the maximum frequent item set under the service provider. Therefore, the similarity calculation method can be expressed by the following formula:
S＝|A∩B|/|A∪B|
wherein, S is the similarity, A is the transaction characteristic information item set corresponding to the buyer under the service provider, and B is the maximum frequent item set under the service provider.
For example, for the sample data of the aforementioned facilitator C, the transaction characteristic information item set corresponding to the buyer 1 is { seller 11, seller 23, seller 12}, and the maximum frequent item set is { seller 11, seller 13, seller 14}, so that the first set is an intersection { seller 11} of { seller 11, seller 23, seller 12} and { seller 11, seller 13, seller 14}, and the second set is a union { seller 11, seller 23, seller 12} and { seller 11, seller 13, seller 14}, where the number of elements of the first set and the number of elements of the second set are respectively 1 and 5, so that the similarity S can be calculated to be 0.2. Similarly, the similarity between the transaction characteristic information item sets corresponding to the buyers 2, 3 and 4 and the maximum frequent item set under the service provider can be calculated as follows: 0.75, 0.75 and 0.5, as shown in table 8.
The first threshold used by the identification module is a preset similarity threshold, and if the similarity between the transaction characteristic information item set of a certain buyer and the maximum frequent item set of a facilitator is higher than the first threshold, the buyer and the facilitator corresponding to the buyer can be considered to perform false transaction. If the similarity corresponding to the buyers under one service provider does not exceed the first threshold, the service provider is indicated to have no behavior of false transaction.
Taking the aforementioned service provider C as an example, if the first threshold is set to 0.6 in this embodiment, two buyers with similarity exceeding the first threshold, namely buyer 2 and buyer 3, exist, so that it can be known that the service provider C has a behavior of false transaction, and the identification module can determine that the service provider C is a member of a false transaction group. If only the service provider needs to be identified whether the false transaction is carried out, only the service provider corresponding to the buyer is determined as a member of the false transaction group.
In other embodiments of the present application, the identification module may further determine the buyer with the similarity exceeding the first threshold and the seller who completed the transaction with the buyer as members of the false transaction group at the same time, so as to obtain a complete false transaction group. For example, the buyer 2 and the buyer 3 under the facilitator C are buyers having a similarity exceeding a first threshold, the seller 11, the seller 24, the seller 14, and the seller 13 are sellers who have completed a transaction with the buyer 2, and the seller 11, the seller 25, the seller 14, and the seller 13 are sellers who have completed a transaction with the buyer 3, so that the members of the false transaction group can be determined as:
the service provider: { C }
Buyer list: { buyer 2, buyer 3}
Seller listing: { seller 11, seller 13, seller 14, seller 24, seller 25}
In a similar manner, processing can be performed separately based on sample data under each facilitator to identify whether each facilitator is a member of a spurious transaction group, as well as a complete member list of spurious transaction groups including buyers, sellers.
In a practical scenario, the maximum number of frequent itemsets of the service provider may be one, or there may be more. When there are multiple most frequent itemsets, this indicates that there may be as many false trade groups under the facilitator. Therefore, when the similarity between the transaction characteristic information item set corresponding to the buyer and the maximum frequent item set under the service provider is calculated, the calculation processing module can firstly construct candidate groups with the same number as the maximum frequent item set, and each candidate group respectively corresponds to one maximum frequent item set under the service provider.
For example, if the most frequent item sets under facilitator D are { seller 11, seller 13, seller 14} and { seller 11, seller 22, seller 23}, respectively, then two candidate parties corresponding to the two most frequent item sets may be constructed. When building a candidate partnership, a partnership ID may be created for the candidate partnership, respectively, to facilitate distinguishing multiple candidate partnership in subsequent processing. In this embodiment, the group ID may be automatically generated based on the facilitator and the most frequent item set, for example, for two candidate groups under the facilitator D, the group IDs "D + seller 11, seller 13, seller 14", "D + seller 11, seller 22, seller 23" may be created.
After the candidate gangs are constructed, the calculation processing module calculates the similarity between the transaction characteristic information item set corresponding to the buyer and the maximum frequent item set corresponding to each candidate gangs respectively. If the buyers under the facilitator D are buyers 1-6 respectively, the similarity of the candidate group "D + seller 11, seller 13, seller 14" is 0.2, 0.4, 0.7, 0.9, 0.5 and 0.8 respectively, and the similarity of the candidate group "D + seller 11, seller 22, seller 23" is 0.9, 0.7, 0.8, 0.3 and 0.2 respectively, which can be specifically shown in table 9.
The similarity 1 is the similarity between the buyers 1 to 6 and the candidate partners "D + seller 11, seller 13 and seller 14", and the similarity 2 is the similarity between the buyers 1 to 6 and the candidate partners "D + seller 11, seller 22 and seller 23".
At this time, when determining whether the service provider is a member of the false transaction group, the identification module may classify buyers into candidate groups with the highest similarity, and for any candidate group, if there is a buyer with the similarity exceeding a first threshold in the candidate groups, determine the candidate group as the false transaction group, and determine the service provider as a member of the false transaction group. For example, for buyer 1 with a similarity 1 of 0.2 and a similarity 2 of 0.9, the buyer 1 will be categorized into the candidate group "D + seller 11, seller 22, seller 23", and the buyer 2-6 can be categorized into the candidate group in the same manner.
Then, for each candidate group, whether the candidate group is a false transaction group is judged according to the buyers with the similarity exceeding the first threshold. If at least one buyer with the similarity exceeding the first threshold exists in the candidate group, the candidate group is the false transaction group, and the corresponding service provider is the member of the false transaction group. If no buyer with the similarity exceeding the first threshold exists in the candidate group, the candidate group is not a false transaction group. Here, it should be understood by those skilled in the art that the order described in the embodiment is not used to limit the processing logic in actual execution, for example, a strict time sequence relationship does not exist between grouping buyers into a candidate group with the highest similarity and performing judgment based on the first threshold, and in an actual scenario, buyers with a similarity exceeding the first threshold may be screened first, and then candidate groups may be allocated to the screened buyers. Present or future implementations based on similar principles, if applicable to the present application, are intended to be included within the scope of the present application and are incorporated herein by reference.
In other embodiments of the present application, for each false transaction group, the identification module may further determine, as members of the false transaction group, the buyer whose similarity exceeds the first threshold and the seller who completed the transaction with the buyer, so as to obtain a complete false transaction group. Therefore, when a plurality of candidate groups exist, for any candidate group, the judgment can be carried out based on a first threshold, if buyers with the similarity exceeding the first threshold exist in the candidate group, the candidate group is determined as a false transaction group, and the facilitator, the buyers with the similarity exceeding the first threshold under the candidate group and the sellers who have completed the transaction with the buyers in the false transaction group are determined as members of the false transaction group.
In a practical scenario, the information obtaining module may further obtain information of the number of sellers transacted by the buyer within the preset time period, and optimize a scheme of false transaction group identification according to the information. Therefore, when the transaction characteristic information item set corresponding to the buyer of the service provider within the preset time period is obtained, the screening can be performed once based on the number of the sellers transacted within the preset time period. When the facilitator organizes the false transaction group, the number of sellers performing false transactions organized by the facilitator is generally not very large, so a second threshold value can be set, the number of sellers performing transactions within the preset time period is compared with the second threshold value, and if the second threshold value is exceeded, the possibility of performing false transactions with buyers performing transactions with such many sellers is considered to be not high, so that the false transactions can be eliminated. That is, in some embodiments of the present application, when acquiring a set of transaction characteristic information items corresponding to buyers in a preset time period, a set of transaction characteristic information items corresponding to buyers meeting an identification requirement in the preset time period in the service provider may be acquired, where the buyers meeting the identification requirement are the buyers of which the number of sellers transacting in the preset time period is less than the second threshold.
In summary, in the false transaction group identification scheme provided in this embodiment of the present application, first, a transaction characteristic information item set corresponding to a buyer of a service provider within a preset time period may be obtained, where an element of the transaction characteristic information item set is transaction characteristic information of a transaction completed by the corresponding buyer, and characteristics of each buyer of the service provider during a transaction may be reflected, then, according to the transaction characteristic information item set, a maximum frequent item set of the service provider is obtained, a similarity between the transaction characteristic information item set corresponding to the buyer and the maximum frequent item set of the service provider is calculated, and if there is a buyer whose similarity exceeds a first threshold, the service provider is determined as a member of the false transaction group. The expression form of the false transaction group is usually that a batch of buyers and a batch of sellers expanded by the same facilitator conduct centralized transaction, the transaction usually shows some specific patterns, for example, sellers of the transaction are similar, transaction amounts are similar, the most frequent item set can represent the specific patterns, therefore, the higher the similarity is, the more the buyers under the facilitator are in the transaction accord with the specific patterns of the false transaction group, and whether the facilitator has the behavior of false transaction can be identified.
In addition, some of the present application may be implemented as a computer program product, such as computer program instructions, which when executed by a computer, may invoke or provide methods and/or techniques in accordance with the present application through the operation of the computer. Program instructions which invoke the methods of the present application may be stored on a fixed or removable recording medium and/or transmitted via a data stream on a broadcast or other signal-bearing medium and/or stored within a working memory of a computer device operating in accordance with the program instructions. Some embodiments according to the present application include a computing device as shown in fig. 5, which includes one or more memories 510 storing computer-readable instructions and a processor 520 for executing the computer-readable instructions, wherein when the computer-readable instructions are executed by the processor, the device is caused to perform the method and/or the technical solution according to the embodiments of the present application.
Furthermore, some embodiments of the present application also provide a computer readable medium, on which computer program instructions are stored, the computer readable instructions being executable by a processor to implement the methods and/or aspects of the foregoing embodiments of the present application.
It should be noted that the present application may be implemented in software and/or a combination of software and hardware, for example, implemented using Application Specific Integrated Circuits (ASICs), general purpose computers or any other similar hardware devices. In some embodiments, the software programs of the present application may be executed by a processor to implement the above steps or functions. Likewise, the software programs (including associated data structures) of the present application may be stored in a computer readable recording medium, such as RAM memory, magnetic or optical drive or diskette and the like. Additionally, some of the steps or functions of the present application may be implemented in hardware, for example, as circuitry that cooperates with the processor to perform various steps or functions.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the apparatus claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Claims (16)
1. A false transaction group identification method, wherein the method comprises:
acquiring a transaction characteristic information item set corresponding to a buyer of a service provider within a preset time period, wherein the elements of the transaction characteristic information item set are transaction characteristic information of a transaction completed by the corresponding buyer;
acquiring a maximum frequent item set under the service provider according to the transaction characteristic information item set;
calculating the similarity between the transaction characteristic information item set corresponding to the buyer and the maximum frequent item set under the service provider;
and if the buyers with the similarity exceeding the first threshold exist, determining the service provider as a member of the false transaction group.
2. The method of claim 1, wherein the method further comprises:
and determining the buyers with the similarity exceeding a first threshold value and the sellers who finish the transaction with the buyers as members of the fake transaction group.
3. The method of claim 1, wherein calculating a similarity of a set of transaction characteristic information items corresponding to a buyer to a most frequent set of items under the facilitator comprises:
determining the ratio of the element quantity of a first set to the element quantity of a second set as the similarity between a transaction characteristic information item set corresponding to a buyer and the maximum frequent item set under the service provider, wherein the first set is the intersection of the transaction characteristic information item set corresponding to the buyer and the maximum frequent item set under the service provider, and the second set is the union of the transaction characteristic information item set corresponding to the buyer and the maximum frequent item set under the service provider.
4. The method of claim 1, wherein the maximum number of frequent itemsets under the facilitator is at least one;
calculating the similarity between the transaction characteristic information item set corresponding to the buyer and the maximum frequent item set under the service provider, wherein the similarity comprises the following steps:
constructing candidate groups with the same number as the maximum frequent item set, wherein each candidate group respectively corresponds to one maximum frequent item set under the service provider;
respectively calculating the similarity between the transaction characteristic information item set corresponding to the buyer and the maximum frequent item set corresponding to each candidate group;
if the buyer with the similarity exceeding the first threshold exists, the service provider is determined as a member of the false transaction group, and the method comprises the following steps:
the buyers are classified into the candidate group with the highest similarity;
for any candidate group, if a buyer with the similarity exceeding a first threshold exists in the candidate group, determining the candidate group as a false transaction group, and determining the service provider as a member of the false transaction group.
5. The method of claim 4, wherein for any candidate group, if there is a buyer in the candidate group whose similarity exceeds a first threshold, determining the candidate group as a false transaction group and determining the facilitator as a member of the false transaction group comprises:
for any candidate group, if a buyer with the similarity exceeding a first threshold exists in the candidate group, the candidate group is determined as a false transaction group, and the facilitator, the buyer with the similarity exceeding the first threshold in the candidate group and the seller who completes the transaction with the buyer in the false transaction group are determined as members of the false transaction group.
6. The method of claim 1, wherein obtaining the set of transaction characteristic information items corresponding to the buyer of the service provider within the preset time period comprises:
and acquiring a transaction characteristic information item set corresponding to buyers meeting identification requirements under a service provider within a preset time period, wherein the buyers meeting the identification requirements are the buyers of which the number of sellers in the transaction within the preset time period is less than a second threshold value.
7. The method of claim 1, wherein the transaction characteristic information is a transaction amount of a buyer or a seller transacting with the buyer.
8. A false transaction group identification device, wherein the device comprises:
the information acquisition module is used for acquiring a transaction characteristic information item set corresponding to a buyer of a service provider within a preset time period, wherein the elements of the transaction characteristic information item set are transaction characteristic information of a transaction completed by the corresponding buyer;
the calculation processing module is used for acquiring the maximum frequent item set under the facilitator according to the transaction characteristic information item set and calculating the similarity between the transaction characteristic information item set corresponding to the buyer and the maximum frequent item set under the facilitator;
and the identification module is used for determining the service provider as a member of the false transaction group when judging that the buyers with the similarity exceeding the first threshold exist.
9. The apparatus of claim 8, wherein the identification module is further configured to determine buyers with the similarity exceeding a first threshold and sellers who completed transactions with the buyers as members of a fake transaction group.
10. The apparatus of claim 8, wherein the calculation processing module is configured to determine a ratio of a first set of element numbers to a second set of element numbers as a similarity between a transaction feature information item set corresponding to a buyer and a maximum frequent item set of the facilitator, wherein the first set is an intersection of the transaction feature information item set corresponding to the buyer and the maximum frequent item set of the facilitator, and the second set is a union of the transaction feature information item set corresponding to the buyer and the maximum frequent item set of the facilitator.
11. The apparatus of claim 8, wherein the maximum number of frequent itemsets under the facilitator is at least one;
the computing processing module is used for constructing candidate groups with the same number as the maximum frequent item set, and each candidate group corresponds to one maximum frequent item set under the service provider respectively; respectively calculating the similarity between the transaction characteristic information item set corresponding to the buyer and the maximum frequent item set corresponding to each candidate group;
the identification module is used for classifying buyers into candidate groups with highest similarity; for any candidate group, if a buyer with the similarity exceeding a first threshold exists in the candidate group, determining the candidate group as a false transaction group, and determining the service provider as a member of the false transaction group.
12. The apparatus of claim 11, wherein the identifying module is configured to determine, for any candidate group, if there is a buyer in the candidate group whose similarity exceeds a first threshold, the candidate group as a false transaction group, and determine the facilitator, the buyer in the candidate group whose similarity exceeds the first threshold, and a seller who has completed a transaction with the buyer in the false transaction group as members of the false transaction group.
13. The apparatus of claim 8, wherein the information obtaining module is configured to obtain a set of transaction characteristic information items corresponding to buyers meeting identification requirements under the service provider within a preset time period, where the buyers meeting identification requirements are the buyers with a number of sellers in the transaction within the preset time period smaller than a second threshold.
14. The apparatus of claim 8, wherein the transaction characteristic information is a transaction amount of a buyer or a seller transacting with the buyer.
15. A computing device, wherein the device comprises a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein the computer program instructions, when executed by the processor, trigger the device to perform the method of any of claims 1 to 7.
16. A computer readable medium having stored thereon computer program instructions executable by a processor to implement the method of any one of claims 1 to 7.
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