CN108510281B - Data detection method and device, electronic equipment and storage medium - Google Patents
Data detection method and device, electronic equipment and storage medium Download PDFInfo
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- CN108510281B CN108510281B CN201810276790.4A CN201810276790A CN108510281B CN 108510281 B CN108510281 B CN 108510281B CN 201810276790 A CN201810276790 A CN 201810276790A CN 108510281 B CN108510281 B CN 108510281B
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/38—Payment protocols; Details thereof
- G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
- G06Q20/401—Transaction verification
- G06Q20/4016—Transaction verification involving fraud or risk level assessment in transaction processing
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- G—PHYSICS
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/04—Payment circuits
- G06Q20/06—Private payment circuits, e.g. involving electronic currency used among participants of a common payment scheme
- G06Q20/065—Private payment circuits, e.g. involving electronic currency used among participants of a common payment scheme using e-cash
Abstract
The embodiment of the invention provides a data detection method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a target electronic currency transaction address to be transacted; obtaining a transaction record of a target electronic currency transaction address in a first preset time period as a first target transaction record; coding the first target transaction record by adopting a preset coding mode to obtain a target coding result; inputting a target coding result into a pre-constructed neural network model; the neural network model is used for: determining a fraud detection result of the electronic money transaction address corresponding to the target coding result; and obtaining a fraud detection result output by the neural network model as a fraud detection result of the target electronic currency transaction address. By applying the embodiment of the invention, whether the transaction to be carried out is a fraud transaction can be determined, thereby reducing the fraud transaction and protecting the asset security of the user.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a data detection method and apparatus, an electronic device, and a storage medium.
Background
As technology has developed, more and more users use electronic money to trade instead of conventional money. Among them, part of the electronic money is encrypted money based on the block chain technology, such as bitcoin and ethernet coin.
However, since the transaction in the blockchain technique is an anonymous transaction, when the user wants to perform an electronic money transaction, the user cannot know the identity information of the transaction partner. Thus making it difficult for the user to discern whether the transaction to be conducted is a fraudulent transaction, resulting in fraudulent transactions often occurring, causing the user's assets to be lost.
Disclosure of Invention
Embodiments of the present invention provide a data detection method, an apparatus, an electronic device, and a storage medium, so as to determine whether a transaction to be performed is a fraudulent transaction, thereby reducing fraudulent transactions and protecting asset security of a user. The specific technical scheme is as follows:
in a first aspect, an embodiment of the present invention provides a data detection method, where the method may include:
determining a target electronic currency transaction address to be transacted;
obtaining a transaction record of a target electronic currency transaction address in a first preset time period as a first target transaction record;
coding the first target transaction record by adopting a preset coding mode to obtain a target coding result;
inputting a target coding result into a pre-constructed neural network model; wherein the neural network model is to: determining a fraud detection result of the target electronic money transaction address corresponding to the target coding result;
and obtaining a fraud detection result output by the neural network model as a fraud detection result of the target electronic currency transaction address.
Optionally, before inputting the target encoding result into the pre-constructed neural network model, the method may further include:
constructing a neural network model;
accordingly, constructing the neural network model may include:
determining N preset electronic money transaction addresses, wherein the N preset electronic money transaction addresses comprise: an electronic money transaction address having fraudulent activity, and/or an electronic money transaction address not having fraudulent activity; wherein N is more than or equal to 2;
respectively obtaining the transaction records of each preset electronic money transaction address in a second preset time period as second target transaction records corresponding to each preset electronic money transaction address;
coding each second target transaction record by adopting a preset coding mode to obtain a coding result corresponding to each second target transaction record;
training by utilizing a preset neural network algorithm and N training samples to obtain a neural network model, wherein one training sample comprises: a second target transaction record corresponding to the encoded result and the fraud identification information; the fraud identification information is used to: and identifying the preset electronic currency transaction area corresponding to the second target transaction record as a fraudulent address or a non-fraudulent address.
Optionally, obtaining the transaction record of each preset electronic money transaction address in a second preset time period as a second target transaction record corresponding to each preset electronic money transaction address may include:
respectively obtaining a first extended transaction record of a preset electronic money transaction address with fraud and a transaction record within a second preset time period, and/or respectively obtaining a transaction record of the preset electronic money transaction address without fraud within the second preset time period as a second target transaction record corresponding to the corresponding preset electronic money transaction address; the first extended transaction record includes: receiving a transaction record of an electronic money transaction address for which an electronic money transaction address remittance is preset.
Optionally, the step of obtaining a transaction record of the target electronic money transaction address within a first preset time period as the first target transaction record may include:
obtaining a second extended transaction record of the target electronic currency transaction address and a transaction record in a first preset time period as a first target transaction record; the second extended transaction record includes: a transaction record is received for an electronic money transaction address remitted by a target electronic money transaction address.
Optionally, when the N preset electronic money transaction addresses include: and when the electronic money transaction address with the fraudulent conduct and the electronic money transaction address without the fraudulent conduct exist, the absolute value of the difference value between the total number of the electronic money transaction addresses with the fraudulent conduct and the total number of the electronic money transaction addresses without the fraudulent conduct is smaller than the preset number.
Optionally, after the training of the neural network model, the method may further include:
adjusting the neural network model by using the M optimization samples to obtain an adjusted neural network model; wherein, an optimization sample comprises: the electronic currency transaction address is used for optimizing the model and corresponding to the coding result and the fraud identification information; wherein M is more than or equal to 2;
accordingly, inputting the target encoding result to the pre-constructed neural network model may include:
and inputting the target coding result into the tuning neural network model.
Optionally, the preset neural network algorithm may include: any one of a circular neural network algorithm, a deep neural network algorithm, and a convolutional neural network algorithm.
Optionally, the preset encoding manner may include: a 2vec mode of word vector coding or a one-hot encoding mode.
Optionally, the first target transaction record comprises at least one sub-transaction record, the first sub-transaction record comprising: the target electronic money transaction address, the electronic money transaction counter address, the balance type of the target electronic money transaction address, the transaction amount, the transaction balance of the target electronic money transaction address and the transaction time; the first sub-transaction record is any one of the first target transaction records;
wherein, the electronic money transaction counterpart address includes: receiving the remittance of the target electronic money transaction address or the electronic money transaction address remittance to the target electronic money transaction address.
In a second aspect, an embodiment of the present invention further provides a data detection apparatus, where the apparatus may include:
a determination unit configured to determine a target electronic money transaction address at which a transaction is to be performed;
a first obtaining unit, configured to obtain a transaction record of a target electronic money transaction address within a first preset time period as a first target transaction record;
the coding unit is used for coding the first target transaction record in a preset coding mode to obtain a target coding result;
the input unit is used for inputting the target coding result into a pre-constructed neural network model; wherein the neural network model is to: determining a fraud detection result of the target electronic money transaction address corresponding to the target coding result;
and a second obtaining unit, configured to obtain a fraud detection result output by the neural network model as a fraud detection result of the target electronic money transaction address.
Optionally, the apparatus may further comprise a construction unit;
the building unit is used for building the neural network model before the target coding result is input to the pre-built neural network model by the input unit;
accordingly, the building unit may comprise:
the determining submodule is used for determining N preset electronic money transaction addresses, wherein the N preset electronic money transaction addresses comprise: an electronic money transaction address having fraudulent activity, and/or an electronic money transaction address not having fraudulent activity; wherein N is more than or equal to 2;
the obtaining submodule is used for respectively obtaining the transaction records of each preset electronic money transaction address in a second preset time period as second target transaction records corresponding to each preset electronic money transaction address;
the coding submodule is used for coding each second target transaction record by adopting a preset coding mode to obtain a coding result corresponding to each second target transaction record;
the training submodule is used for training by utilizing a preset neural network algorithm and N training samples to obtain a neural network model, wherein one training sample comprises: a second target transaction record corresponding to the encoded result and the fraud identification information; the fraud identification information is used to: and identifying the preset electronic currency transaction area corresponding to the second target transaction record as a fraudulent address or a non-fraudulent address.
Optionally, the obtaining sub-module is specifically configured to:
respectively obtaining a first extended transaction record of a preset electronic money transaction address with fraud and a transaction record within a second preset time period, and/or respectively obtaining a transaction record of the preset electronic money transaction address without fraud within the second preset time period as a second target transaction record corresponding to the corresponding preset electronic money transaction address; the first extended transaction record includes: receiving a transaction record of an electronic money transaction address for which an electronic money transaction address remittance is preset.
Optionally, the first obtaining unit is specifically configured to:
obtaining a second extended transaction record of the target electronic currency transaction address and a transaction record in a first preset time period as a first target transaction record; the second extended transaction record includes: a transaction record is received for an electronic money transaction address remitted by a target electronic money transaction address.
Optionally, when the N preset electronic money transaction addresses include: and when the electronic money transaction address with the fraudulent conduct and the electronic money transaction address without the fraudulent conduct exist, the absolute value of the difference value between the total number of the electronic money transaction addresses with the fraudulent conduct and the total number of the electronic money transaction addresses without the fraudulent conduct is smaller than the preset number.
Optionally, in an embodiment of the present invention, the apparatus may further include:
the adjusting unit is used for optimizing the neural network model by using M optimization samples after the neural network model is obtained through training to obtain an optimized neural network model; wherein, an optimization sample comprises: the electronic currency transaction address is used for optimizing the model and corresponding to the coding result and the fraud identification information; wherein M is more than or equal to 2;
the input unit is specifically configured to: and inputting the target coding result into the tuning neural network model.
Optionally, the preset neural network algorithm may include: any one of a circular neural network algorithm, a deep neural network algorithm, and a convolutional neural network algorithm.
Optionally, the preset encoding manner includes: a 2vec mode of word vector coding or a one-hot encoding mode.
Optionally, the first target transaction record comprises at least one sub-transaction record, the first sub-transaction record comprising: the target electronic money transaction address, the electronic money transaction counter address, the balance type of the target electronic money transaction address, the transaction amount, the transaction balance of the target electronic money transaction address and the transaction time; the first sub-transaction record is any one of the first target transaction records;
wherein, the address of the electronic money transaction counterpart is: electronic money transaction address for receiving remittance from or to target electronic money transaction address
In a third aspect, an embodiment of the present invention further provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory complete mutual communication through the communication bus;
a memory for storing a computer program;
and the processor is used for realizing the method steps of any data detection method when executing the program stored in the memory.
In a fourth aspect, the embodiment of the present invention further provides a readable storage medium, where a computer program is stored in the readable storage medium, and the computer program, when executed by a processor, implements the method steps of any of the data detection methods described above.
In a fifth aspect, an embodiment of the present invention further provides a computer program product including instructions, which when run on an electronic device, cause the electronic device to perform: method steps of any of the above data detection methods.
In the embodiment of the invention, when it is required to determine whether the transaction to be carried out is a fraudulent transaction, the target electronic money transaction address of the transaction to be carried out can be determined firstly. Then, the transaction record of the target electronic money transaction address in a first preset time period is obtained, and the obtained transaction record is recorded as a first target transaction record. And then, coding the first target transaction record by adopting a preset coding mode to obtain a target coding result. And inputting the obtained target coding result into the neural network model. Wherein, the neural network model can be used for determining the fraud detection result of the electronic currency transaction address corresponding to the coding result. Fraud detection results for the target electronic money transaction address output by the neural network model may thus be obtained. In addition, since when an electronic money transaction address is a fraudulent address, the transaction performed with the electronic money transaction address is likely to be a fraudulent transaction, the result of fraud detection for the target electronic money transaction address is also the result of fraud detection for the transaction to be performed. Therefore, whether the transaction to be carried out is a fraud transaction can be determined through the fraud detection result, so that fraud transactions are reduced, and the asset security of the user is protected.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a data detection method according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for constructing a neural network model according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a data detection apparatus according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to solve the problem that it is difficult to determine whether an electronic money transaction to be performed is a fraudulent transaction in the prior art, embodiments of the present invention provide a data detection method, an apparatus, an electronic device, and a storage medium.
The following first describes a data detection method provided by an embodiment of the present invention.
Referring to fig. 1, a data detection method provided in an embodiment of the present invention may include the following steps:
s101: determining a target electronic currency transaction address to be transacted;
s102: obtaining a transaction record of a target electronic currency transaction address in a first preset time period as a first target transaction record;
s103: coding the first target transaction record by adopting a preset coding mode to obtain a target coding result;
s104: inputting a target coding result into a pre-constructed neural network model; wherein the neural network model is to: determining a fraud detection result of the target electronic money transaction address corresponding to the target coding result;
s105: and obtaining a fraud detection result output by the neural network model as a fraud detection result of the target electronic currency transaction address.
In the embodiment of the invention, when it is required to determine whether the transaction to be carried out is a fraudulent transaction, the target electronic money transaction address of the transaction to be carried out can be determined firstly. Then, the transaction record of the target electronic money transaction address in a first preset time period is obtained, and the obtained transaction record is recorded as a first target transaction record. And then, coding the first target transaction record by adopting a preset coding mode to obtain a target coding result. And inputting the obtained target coding result into the neural network model. Wherein, the neural network model is used for determining the fraud detection result of the target electronic money transaction address corresponding to the target coding result. Fraud detection results for the target electronic money transaction address output by the neural network model may thus be obtained. In addition, since when an electronic money transaction address is a fraudulent address, the transaction performed with the electronic money transaction address is likely to be a fraudulent transaction, the result of fraud detection for the target electronic money transaction address is also the result of fraud detection for the transaction to be performed. Therefore, whether the transaction to be carried out is a fraud transaction can be determined through the fraud detection result, so that fraud transactions are reduced, and the asset security of the user is protected.
In the embodiment of the present invention, part of the electronic money is cryptocurrency based on a block chain technology. Wherein the virtual coin is one of electronic money, and the virtual coin includes but is not limited to bitcoin, ethernet coin and reiter coin. In addition, in the embodiment of the invention, one electronic money transaction address is an electronic money transaction account number.
In addition, the neural network model provided by the embodiment of the invention can detect the coding result of the transaction record corresponding to any electronic money transaction address input into the neural network model, thereby outputting the fraud detection result of the corresponding electronic money transaction address.
The following ethernet currency is an example, and details of the data detection method provided by the embodiment of the present invention are described.
In the ethernet transaction, before a user a (assuming that an electronic money transaction address corresponding to the user a is a) pays the ethernet money to a user B (assuming that an electronic money transaction address corresponding to the user B is B), since the user a cannot know identity information of the user B, it cannot be determined whether the user B is authentic, and thus it cannot be determined whether a transaction to be performed is a fraudulent transaction.
In order to determine whether the transaction to be conducted is a fraudulent transaction, thereby avoiding the fraudulent transaction causing asset loss to user a. Before the user a performs the transaction, the electronic device may determine a target electronic money transaction address, i.e. electronic money transaction address b, at which the user a is to make a transaction.
Then, the electronic device may obtain a transaction record of the electronic money transaction address b within a first preset time period through an ethernet Programming Interface (API), and record the obtained transaction record as a first target transaction record. The etherhouse is a block chain-based decentralized application platform, which is an existing concept and will not be described in detail herein.
In order to obtain a more accurate fraud detection result, the transaction records of the electronic money transaction address b can be obtained as much as possible. In order to obtain more transaction records, the first preset time period may be set to correspond to a longer time period. For example, setting the first preset time period as: the time period corresponding to the current time up to five years ago is not limited to this.
In addition, in order to increase the speed of obtaining the fraud detection result, the first preset time period may also be set to one year (for example, 2017 years), but is not limited thereto. The first preset time period may be set by a person skilled in the art according to actual needs, and is not specifically limited herein.
For example, a transaction record of the obtained electronic money transaction address b may include: the balance type of the electronic money transaction address b, the electronic money transaction counter address c, and the electronic money transaction address b: expenditure, transaction amount: transaction balance of 5 ethernet coins, target electronic money transaction address: 1 Ethernet currency, transaction time: 20180316.
the electronic money transaction counterpart address can be: the electronic money transaction counterpart address c receiving the remittance from the electronic money transaction address b may further be: it is reasonable to remit money to the electronic money transaction address e or f, etc. of the electronic money transaction address b.
Of course, the total number of transactions, such as 1000 transactions, may also be included in the transaction record, although the elements included in the transaction record are not limited thereto. Wherein, the total transaction number is: the electronic money transaction address b is the total number of transactions performed before 5 ethernet money is remitted to the electronic money transaction counterpart address c.
After the first target transaction record corresponding to the electronic money transaction address b is obtained, the first target transaction record can be encoded by using a word vector encoding item2vec mode or a one-hot encoding mode, and an encoding result is recorded as a target encoding result to obtain a target encoding result.
The one-hot encoding mode can represent elements in the transaction record in a vector form, that is, one multi-dimensional vector can be used for representing one element. Wherein, the values of multiple dimensions in the vector are 0, and only one dimension is 1. The word vector coding item2vec mode is adopted for coding, dimensionality can be reduced, context of each element in a transaction record can be mined, and accordingly vector semantic accuracy is improved.
After the target coding result is obtained, a neural network model can be constructed, the target coding result is input into the constructed neural network model, and the neural network model can determine the fraud detection result of the electronic money transaction address corresponding to the coding result, so that the fraud detection result of the electronic money transaction address b output by the neural network model can be obtained. The manner in which the neural network model is constructed is described in detail below for clarity of layout.
In an implementation manner, the fraud detection result may specifically include: the credibility is one of high, general, low and low. In another implementation, the fraud detection result may also be: the risk of fraud is high or low. Although not limited thereto.
Suppose that the fraud detection result for the e-currency transaction address b output by the neural network model is: the confidence level is low, i.e. the fraud risk level is: the reliability is low. At this time, the transaction behavior indicating the electronic money transaction address b is highly similar to the transaction behavior of the fraudulent address, or is less similar to the transaction behavior of the non-fraudulent address. That is, the electronic money transaction address b is likely to be a fraudulent address. At this time, the transaction to be performed with the electronic money transaction address b is likely to be a fraudulent transaction, and in order to secure the asset of the user, a low reliability (i.e., fraud risk) may be used as a fraud detection result of the transaction to be performed. Therefore, when the user A knows that the credibility of the transaction to be carried out is low, the transaction can be stopped, and the asset safety of the user A is ensured.
In addition, when the fraud detection result of the electronic money transaction address b is: when the credibility is high, the transaction behavior of the electronic money transaction address b is indicated to be lower in similarity with the transaction behavior of a fraudulent address or higher in similarity with the transaction behavior of a non-fraudulent address. Therefore, when the user A knows that the credibility of the transaction to be carried out is high, the transaction can be carried out, and the asset safety of the user A is ensured.
The following describes in detail a process of constructing a neural network model provided by an embodiment of the present invention.
Referring to fig. 2, the step of constructing the neural network model may include:
s201: determining N preset electronic money transaction addresses, wherein the N preset electronic money transaction addresses comprise: an electronic money transaction address having fraudulent activity, and/or an electronic money transaction address not having fraudulent activity; wherein N is more than or equal to 2;
in one implementation manner, the determined N preset electronic money transaction addresses are all electronic money transaction addresses with fraudulent behaviors. In another implementation manner, the determined N preset electronic money transaction addresses are all electronic money transaction addresses without fraudulent behavior. In yet another implementation, the determined N preset electronic money transaction addresses include both an electronic money transaction address having fraudulent activity and an electronic money transaction address having no fraudulent activity. In yet another implementation, the determined N preset electronic money transaction addresses include both an electronic money transaction address having fraudulent activity and a first extended transaction address of the electronic money transaction address having fraudulent activity. This is all reasonable.
Wherein, the first extended transaction address of the electronic currency transaction address with fraud is: receiving the electronic money transaction address remitted by the fraudulent electronic money transaction address.
It is understood that each of the determined predetermined electronic money transaction addresses corresponds to fraud identification information. The fraud identification information is used for identifying whether the preset electronic money transaction is a fraudulent address or a non-fraudulent address. It is reasonable to identify, for example, a spoofed address by 1 and a non-spoofed address by 0. Although not limited thereto.
When the N preset electronic money transaction addresses comprise: when the electronic money transaction address with the fraudulent behavior and the electronic money transaction address without the fraudulent behavior are used, in order to avoid the inaccuracy of the detection result of the trained model, the absolute value of the difference between the total number of the electronic money transaction addresses with the fraudulent behavior and the total number of the electronic money transaction addresses without the fraudulent behavior can be set to be smaller than the preset number.
The preset number can be set by a person skilled in the art according to specific requirements, and is not limited herein.
S202: respectively obtaining the transaction records of each preset electronic money transaction address in a second preset time period as second target transaction records corresponding to each preset electronic money transaction address;
in order to train and obtain a neural network model capable of accurately detecting fraud, transaction records of each preset electronic currency transaction address can be obtained as much as possible. And in order to obtain more transaction records, a second preset time period corresponding to a longer time length can be set. For example, setting the first preset time period as: the time period corresponding to the current time up to ten years ago is not limited to this. The second preset time period can be set by a person skilled in the art according to actual needs, and is not particularly limited herein.
Since the electronic money transaction address with fraud (namely, the fraud address) receives the remittance, the money is often transferred to other N electronic money transaction addresses, and the N electronic money transaction addresses execute similar money transfer operation and are finally transferred to wallet addresses which are convenient to embody.
Therefore, in order to train and obtain a neural network model capable of accurately detecting a fraudulent address, when the determined preset electronic money transaction address is the electronic money transaction address with the fraudulent behavior, a first extended transaction record of the preset electronic money transaction address with the fraudulent behavior and a transaction record in a second preset time period can be respectively obtained and used as a second target transaction record corresponding to the corresponding preset electronic money transaction address. And when the determined preset electronic money transaction address is the electronic money transaction address without the fraudulent conduct, respectively obtaining the transaction records of the preset electronic money transaction address without the fraudulent conduct in a second preset time period, and taking the transaction records as second target transaction records corresponding to the corresponding preset electronic money transaction address. When the determined preset electronic money transaction address is the electronic money transaction address with the fraud behavior and the electronic money transaction address without the fraud behavior, respectively obtaining a first extended transaction record of the preset electronic money transaction address with the fraud behavior and a transaction record in a second preset time period, and respectively obtaining the transaction record of the preset electronic money transaction address without the fraud behavior in the second preset time period as a second target transaction record corresponding to the corresponding preset electronic money transaction address.
Wherein the first extended transaction record comprises: and receiving the transaction record of the electronic money transaction address remitted by the preset electronic money transaction address.
Accordingly, in order to ensure the accuracy of the detection result, in the process of detecting whether the transaction to be carried out is a fraudulent transaction by using the neural network model, a second extended transaction record of the target electronic money transaction address and a transaction record in a first preset time period are obtained as a first target transaction record. Wherein the second extended transaction record comprises: receiving a transaction record for the electronic money transaction address remitted by the target electronic money transaction address. This is reasonable.
S203: coding each second target transaction record by adopting a preset coding mode to obtain a coding result corresponding to each second target transaction record;
the preset encoding method may include: a 2vec mode of word vector coding or a one-hot encoding mode. Although not limited thereto.
S204: training by utilizing a preset neural network algorithm and N training samples to obtain a neural network model, wherein one training sample comprises: a second target transaction record corresponding to the encoded result and the fraud identification information; the fraud identification information is used to: and identifying the preset electronic currency transaction area corresponding to the second target transaction record as a fraudulent address or a non-fraudulent address.
The preset neural network algorithm comprises the following steps: any one of a Recurrent Neural Network (RNN), a Deep Neural Network (DNN), and a Convolutional Neural Network (CNN). Accordingly, when a recurrent neural network algorithm is utilized, a recurrent neural network model can be trained; when a deep neural network algorithm is utilized, a deep neural network model can be obtained through training; when a convolutional neural network algorithm is utilized, a convolutional neural network model can be trained.
The neural network models can calculate the similarity between the target coding result input into the neural network model and the fraudulent address or the non-fraudulent address, and therefore the fraud detection result of the target electronic currency transaction address corresponding to the target coding result is output. Thus, a fraud detection result of a transaction to be conducted with the target electronic money transaction address may be determined based on the determination.
In addition, in order to improve the accuracy of the fraud detection result output by the neural network model, after the neural network model is obtained through training, M optimization samples can be used for optimizing the neural network model, wherein M is more than or equal to 2. Wherein, an optimization sample comprises: and the electronic currency transaction address used for optimizing the model corresponds to the coding result and the fraud identification information. In this way, parameters in the neural network model can be optimized, and therefore the accuracy of output results is improved.
In conclusion, whether the transaction to be carried out is a fraudulent transaction can be determined through the neural network model provided by the embodiment of the invention, so that the fraudulent transactions are reduced, and the asset security of the user is protected.
Corresponding to the above method embodiment, an embodiment of the present invention further provides a data detection apparatus, and referring to fig. 3, the apparatus may include:
a determination unit 301 for determining a target electronic money transaction address to be transacted;
a first obtaining unit 302, configured to obtain a transaction record of the target electronic money transaction address within a first preset time period as a first target transaction record;
the encoding unit 303 is configured to encode the first target transaction record in a preset encoding manner to obtain a target encoding result;
an input unit 304, configured to input a target encoding result to a pre-constructed neural network model; wherein the neural network model is to: determining a fraud detection result of the target electronic money transaction address corresponding to the target coding result;
a second obtaining unit 305 for obtaining the fraud detection result output by the neural network model as the fraud detection result of the target electronic money transaction address.
By applying the device provided by the embodiment of the invention, when whether the transaction to be carried out is a fraudulent transaction needs to be determined, the target electronic money transaction address of the transaction to be carried out can be determined firstly. Then, the transaction record of the target electronic money transaction address in a first preset time period is obtained, and the obtained transaction record is recorded as a first target transaction record. And then, coding the first target transaction record by adopting a preset coding mode to obtain a target coding result. And inputting the obtained target coding result into the neural network model. Wherein, the neural network model can be used for determining the fraud detection result of the electronic currency transaction address corresponding to the coding result. Fraud detection results for the target electronic money transaction address output by the neural network model may thus be obtained. In addition, since when an electronic money transaction address is a fraudulent address, the transaction performed with the electronic money transaction address is likely to be a fraudulent transaction, the result of fraud detection for the target electronic money transaction address is also the result of fraud detection for the transaction to be performed. Therefore, whether the transaction to be carried out is a fraud transaction can be determined through the fraud detection result, so that fraud transactions are reduced, and the asset security of the user is protected.
Optionally, in an embodiment of the present invention, the apparatus may further include a building unit;
the construction unit is used for constructing the neural network model before the input unit 304 inputs the target coding result into the pre-constructed neural network model;
the building unit may specifically include:
the determining submodule is used for determining N preset electronic money transaction addresses, wherein the N preset electronic money transaction addresses comprise: an electronic money transaction address having fraudulent activity, and/or an electronic money transaction address not having fraudulent activity; wherein N is more than or equal to 2;
the obtaining submodule is used for respectively obtaining the transaction records of each preset electronic money transaction address in a second preset time period as second target transaction records corresponding to each preset electronic money transaction address;
the coding submodule is used for coding each second target transaction record by adopting a preset coding mode to obtain a coding result corresponding to each second target transaction record;
the training submodule is used for training by utilizing a preset neural network algorithm and N training samples to obtain a neural network model, wherein one training sample comprises: a second target transaction record corresponding to the encoded result and the fraud identification information; the fraud identification information is used to: and identifying the preset electronic currency transaction area corresponding to the second target transaction record as a fraudulent address or a non-fraudulent address.
Optionally, the obtaining sub-module is specifically configured to:
respectively obtaining a first extended transaction record of a preset electronic money transaction address with fraud and a transaction record within a second preset time period, and/or respectively obtaining a transaction record of the preset electronic money transaction address without fraud within the second preset time period as a second target transaction record corresponding to the corresponding preset electronic money transaction address; the first extended transaction record includes: receiving a transaction record of an electronic money transaction address for which an electronic money transaction address remittance is preset.
Optionally, in this embodiment of the present invention, the first obtaining unit 302 may specifically be configured to:
obtaining a second extended transaction record of the target electronic currency transaction address and a transaction record in a first preset time period as a first target transaction record; the second extended transaction record includes: a transaction record is received for an electronic money transaction address remitted by a target electronic money transaction address.
Optionally, when the N preset electronic money transaction addresses include: and when the electronic money transaction address with the fraudulent conduct and the electronic money transaction address without the fraudulent conduct exist, the absolute value of the difference value between the total number of the electronic money transaction addresses with the fraudulent conduct and the total number of the electronic money transaction addresses without the fraudulent conduct is smaller than the preset number.
Optionally, in an embodiment of the present invention, the apparatus may further include:
the adjusting unit is used for optimizing the neural network model by using M optimization samples after the neural network model is obtained through training to obtain an optimized neural network model; wherein, an optimization sample comprises: the electronic currency transaction address is used for optimizing the model and corresponding to the coding result and the fraud identification information; wherein M is more than or equal to 2;
the input unit is specifically configured to: and inputting the target coding result into the tuning neural network model.
Optionally, in an embodiment of the present invention, the preset neural network algorithm may include: any one of a circular neural network algorithm, a deep neural network algorithm, and a convolutional neural network algorithm.
Optionally, in an embodiment of the present invention, the preset encoding manner includes: a 2vec mode of word vector coding or a one-hot encoding mode.
Optionally, the first target transaction record comprises at least one sub-transaction record, the first sub-transaction record comprising: the target electronic money transaction address, the electronic money transaction counter address, the balance type of the target electronic money transaction address, the transaction amount, the transaction balance of the target electronic money transaction address and the transaction time; the first sub-transaction record is any one of the first target transaction records;
wherein, the electronic money transaction counterpart address includes: receiving the remittance of the target electronic money transaction address or the electronic money transaction address remittance to the target electronic money transaction address.
Corresponding to the above method embodiment, an embodiment of the present invention further provides an electronic device, referring to fig. 4, the electronic device includes a processor 401, a communication interface 402, a memory 403, and a communication bus 404, where the processor 401, the communication interface 402, and the memory 403 complete communication with each other through the communication bus 404;
a memory 403 for storing a computer program;
the processor 401 is configured to implement the method steps of any of the data detection methods described above when executing the program stored in the memory 403.
When it is required to determine whether the transaction to be performed is a fraudulent transaction, the electronic device provided by the embodiment of the present invention may first determine the target electronic money transaction address of the transaction to be performed. Then, the transaction record of the target electronic money transaction address in a first preset time period is obtained, and the obtained transaction record is recorded as a first target transaction record. And then, coding the first target transaction record by adopting a preset coding mode to obtain a target coding result. And inputting the obtained target coding result into the neural network model. Wherein, the neural network model can be used for determining the fraud detection result of the electronic currency transaction address corresponding to the coding result. Thus, a fraud detection result for the target electronic money transaction address output by the neural network model may be obtained. In addition, since when an electronic money transaction address is a fraudulent address, the transaction performed with the electronic money transaction address is likely to be a fraudulent transaction, the result of fraud detection for the target electronic money transaction address is also the result of fraud detection for the transaction to be performed. Therefore, whether the transaction to be carried out is a fraud transaction can be determined through the fraud detection result, so that fraud transactions are reduced, and the asset security of the user is protected.
Corresponding to the above method embodiment, an embodiment of the present invention further provides a readable storage medium, where a computer program is stored in the readable storage medium, and the computer program, when executed by a processor, implements the method steps of any of the above data detection methods.
After the computer program stored in the readable storage medium provided by the embodiment of the present invention is executed by the processor of the electronic device, the electronic device may determine the target electronic money transaction address to be transacted. Then, the transaction record of the target electronic money transaction address in a first preset time period is obtained, and the obtained transaction record is recorded as a first target transaction record. And then, coding the first target transaction record by adopting a preset coding mode to obtain a target coding result. And inputting the obtained target coding result into the neural network model. Wherein, the neural network model can be used for determining the fraud detection result of the electronic currency transaction address corresponding to the coding result. Thus, a fraud detection result for the target electronic money transaction address output by the neural network model may be obtained. In addition, since when an electronic money transaction address is a fraudulent address, the transaction performed with the electronic money transaction address is likely to be a fraudulent transaction, the result of fraud detection for the target electronic money transaction address is also the result of fraud detection for the transaction to be performed. Therefore, whether the transaction to be carried out is a fraud transaction can be determined through the fraud detection result, so that fraud transactions are reduced, and the asset security of the user is protected.
Corresponding to the above method embodiment, an embodiment of the present invention further provides a computer program product including instructions, which, when run on an electronic device, cause the electronic device to perform: method steps of any of the above data detection methods.
The computer program product including instructions provided by the embodiments of the present invention, when running on an electronic device, enables the electronic device to determine a target electronic money transaction address to be transacted. Then, the transaction record of the target electronic money transaction address in a first preset time period is obtained, and the obtained transaction record is recorded as a first target transaction record. And then, coding the first target transaction record by adopting a preset coding mode to obtain a target coding result. And inputting the obtained target coding result into the neural network model. Wherein, the neural network model can be used for determining the fraud detection result of the electronic currency transaction address corresponding to the coding result. Thus, a fraud detection result for the target electronic money transaction address output by the neural network model may be obtained. In addition, since when an electronic money transaction address is a fraudulent address, the transaction performed with the electronic money transaction address is likely to be a fraudulent transaction, the result of fraud detection for the target electronic money transaction address is also the result of fraud detection for the transaction to be performed. Therefore, whether the transaction to be carried out is a fraud transaction can be determined through the fraud detection result, so that fraud transactions are reduced, and the asset security of the user is protected.
The communication bus mentioned in the electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface is used for communication between the electronic equipment and other equipment.
The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
The terminology used in the embodiments of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the examples of this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It should be understood that although the terms "first", "second", "third", etc. may be used to describe various connection ports and identification information, etc. in the embodiments of the present application, these connection ports and identification information, etc. should not be limited to these terms. These terms are only used to distinguish the connection port and the identification information and the like from each other. For example, the first connection port may also be referred to as a second connection port, and similarly, the second connection port may also be referred to as a first connection port, without departing from the scope of embodiments of the present application.
The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context. Similarly, the phrases "if determined" or "if detected (a stated condition or event)" may be interpreted as "when determined" or "in response to a determination" or "when detected (a stated condition or event)" or "in response to a detection (a stated condition or event)", depending on the context.
Through the above description of the embodiments, it is clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device may be divided into different functional modules to complete all or part of the above described functions. For the specific working processes of the system, the apparatus and the unit described above, reference may be made to the corresponding processes in the foregoing method embodiments, and details are not described here again.
In the several embodiments provided in the present application, it should be understood that the disclosed electronic device, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (14)
1. A method of data detection, the method comprising:
determining a target electronic currency transaction address to be transacted;
obtaining a transaction record of the target electronic currency transaction address in a first preset time period as a first target transaction record; the step of obtaining the transaction record of the target electronic money transaction address in a first preset time period as a first target transaction record comprises: obtaining a second extended transaction record of the target electronic money transaction address and a transaction record in the first preset time period as a first target transaction record; the second extended transaction record includes: receiving a transaction record of the electronic money transaction address remitted by the target electronic money transaction address;
coding the first target transaction record by adopting a preset coding mode to obtain a target coding result;
inputting the target coding result into a pre-constructed neural network model; wherein the neural network model is to: determining a fraud detection result of a target electronic currency transaction address corresponding to the target coding result;
obtaining a fraud detection result output by the neural network model as a fraud detection result of the target electronic currency transaction address;
before the inputting the target encoding result into the pre-constructed neural network model, the method further comprises:
constructing the neural network model;
the constructing the neural network model comprises:
determining N preset electronic money transaction addresses, wherein the N preset electronic money transaction addresses comprise: an electronic money transaction address having fraudulent activity, and/or an electronic money transaction address not having fraudulent activity; wherein N is more than or equal to 2;
respectively obtaining the transaction records of each preset electronic money transaction address in a second preset time period as second target transaction records corresponding to each preset electronic money transaction address;
coding each second target transaction record by adopting the preset coding mode to obtain a coding result corresponding to each second target transaction record;
training by utilizing a preset neural network algorithm and N training samples to obtain the neural network model, wherein one training sample comprises: a second target transaction record corresponding to the encoded result and the fraud identification information; the fraud identification information is used to: identifying the preset electronic currency transaction place corresponding to the second target transaction record as a fraudulent address or a non-fraudulent address;
the obtaining of the transaction record of each preset electronic money transaction address in a second preset time period as a second target transaction record corresponding to each preset electronic money transaction address includes:
respectively obtaining a first extended transaction record of a preset electronic money transaction address with fraud and a transaction record in the second preset time period, and respectively obtaining a transaction record of the preset electronic money transaction address without fraud in the second preset time period as a second target transaction record corresponding to the corresponding preset electronic money transaction address; the first extended transaction record includes: and receiving the transaction record of the electronic money transaction address remitted by the preset electronic money transaction address.
2. The method according to claim 1, wherein when the N preset electronic money transaction addresses include: when the electronic money transaction addresses with the fraudulent conduct and the electronic money transaction addresses without the fraudulent conduct exist, the absolute value of the difference value between the total number of the electronic money transaction addresses with the fraudulent conduct and the total number of the electronic money transaction addresses without the fraudulent conduct is smaller than the preset number.
3. The method of claim 1, wherein after training the neural network model, the method further comprises:
adjusting the neural network model by using M optimization samples to obtain an adjusted neural network model; wherein, an optimization sample comprises: the electronic currency transaction address is used for optimizing the model and corresponding to the coding result and the fraud identification information; wherein M is more than or equal to 2;
the inputting the target coding result into a pre-constructed neural network model comprises:
and inputting the target coding result into the tuning neural network model.
4. The method of claim 1, wherein the pre-set neural network algorithm comprises: any one of a circular neural network algorithm, a deep neural network algorithm, and a convolutional neural network algorithm.
5. The method according to any one of claims 1-4, wherein the preset encoding manner comprises: a 2vec mode of word vector coding or a one-hot encoding mode.
6. The method of any of claims 1-4, wherein the first target transaction record comprises at least one sub-transaction record, the first sub-transaction record comprising: the target electronic money transaction address, the electronic money transaction counter address, the balance type of the target electronic money transaction address, the transaction amount, the transaction balance of the target electronic money transaction address and the transaction time; the first sub-transaction record is any one of the first target transaction records;
wherein the electronic money transaction counterpart address includes: and receiving remittance of the target electronic money transaction address or the electronic money transaction address remittance to the target electronic money transaction address.
7. A data detection apparatus, characterized in that the apparatus comprises:
a determination unit configured to determine a target electronic money transaction address at which a transaction is to be performed;
a first obtaining unit, configured to obtain a transaction record of the target electronic money transaction address within a first preset time period as a first target transaction record; the first obtaining unit is specifically configured to: obtaining a second extended transaction record of the target electronic money transaction address and a transaction record in the first preset time period as a first target transaction record; the second extended transaction record includes: receiving a transaction record of the electronic money transaction address remitted by the target electronic money transaction address;
the coding unit is used for coding the first target transaction record in a preset coding mode to obtain a target coding result;
the input unit is used for inputting the target coding result into a pre-constructed neural network model; wherein the neural network model is to: determining a fraud detection result of a target electronic currency transaction address corresponding to the target coding result;
a second obtaining unit configured to obtain a fraud detection result output by the neural network model as a fraud detection result of the target electronic money transaction address;
the apparatus further comprises a construction unit;
the building unit is used for building the neural network model before the input unit inputs the target coding result into a pre-built neural network model;
the construction unit comprises:
the determining submodule is used for determining N preset electronic money transaction addresses, wherein the N preset electronic money transaction addresses comprise: an electronic money transaction address having fraudulent activity, and/or an electronic money transaction address not having fraudulent activity; wherein N is more than or equal to 2;
the obtaining submodule is used for respectively obtaining the transaction records of each preset electronic money transaction address in a second preset time period as second target transaction records corresponding to each preset electronic money transaction address;
the coding submodule is used for coding each second target transaction record by adopting the preset coding mode to obtain a coding result corresponding to each second target transaction record;
the training submodule is used for training by utilizing a preset neural network algorithm and N training samples to obtain the neural network model, wherein one training sample comprises: a second target transaction record corresponding to the encoded result and the fraud identification information; the fraud identification information is used to: identifying the preset electronic currency transaction place corresponding to the second target transaction record as a fraudulent address or a non-fraudulent address;
the obtaining submodule is specifically configured to:
respectively obtaining a first extended transaction record of a preset electronic money transaction address with fraud and a transaction record in the second preset time period, and respectively obtaining a transaction record of the preset electronic money transaction address without fraud in the second preset time period as a second target transaction record corresponding to the corresponding preset electronic money transaction address; the first extended transaction record includes: and receiving the transaction record of the electronic money transaction address remitted by the preset electronic money transaction address.
8. The apparatus according to claim 7, wherein when the N preset electronic money transaction addresses include: when the electronic money transaction addresses with the fraudulent conduct and the electronic money transaction addresses without the fraudulent conduct exist, the absolute value of the difference value between the total number of the electronic money transaction addresses with the fraudulent conduct and the total number of the electronic money transaction addresses without the fraudulent conduct is smaller than the preset number.
9. The apparatus of claim 7, further comprising:
the adjusting unit is used for optimizing the neural network model by using M optimization samples after the neural network model is obtained through training to obtain an optimized neural network model; wherein, an optimization sample comprises: the electronic currency transaction address is used for optimizing the model and corresponding to the coding result and the fraud identification information; wherein M is more than or equal to 2;
the input unit is specifically configured to: and inputting the target coding result into the tuning neural network model.
10. The apparatus of claim 7, wherein the predetermined neural network algorithm comprises: any one of a circular neural network algorithm, a deep neural network algorithm, and a convolutional neural network algorithm.
11. The apparatus according to any one of claims 7-10, wherein the predetermined encoding manner comprises: a 2vec mode of word vector coding or a one-hot encoding mode.
12. The apparatus of any of claims 7-10, wherein the first target transaction record comprises at least one sub-transaction record, the first sub-transaction record comprising: the target electronic money transaction address, the electronic money transaction counter address, the balance type of the target electronic money transaction address, the transaction amount, the transaction balance of the target electronic money transaction address and the transaction time; the first sub-transaction record is any one of the first target transaction records;
wherein, the address of the electronic money transaction counterpart is: and receiving remittance of the target electronic money transaction address or the electronic money transaction address remittance to the target electronic money transaction address.
13. An electronic device is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for realizing mutual communication by the memory through the communication bus;
a memory for storing a computer program;
a processor for implementing the method steps of any one of claims 1-6 when executing a program stored on a memory.
14. A readable storage medium, characterized in that a computer program is stored in the readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any one of claims 1-6.
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| KR20150061541A (en) * | 2013-11-26 | 2015-06-04 | 주식회사 씽크풀 | Providing method and system for preventing fraud trading |
| CN106447333A (en) * | 2016-11-29 | 2017-02-22 | 中国银联股份有限公司 | Fraudulent trading detection method and server |
| CN107085812A (en) * | 2016-12-06 | 2017-08-22 | 雷盈企业管理（上海）有限公司 | The anti money washing system and method for block chain digital asset |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20150061541A (en) * | 2013-11-26 | 2015-06-04 | 주식회사 씽크풀 | Providing method and system for preventing fraud trading |
| CN106447333A (en) * | 2016-11-29 | 2017-02-22 | 中国银联股份有限公司 | Fraudulent trading detection method and server |
| CN107085812A (en) * | 2016-12-06 | 2017-08-22 | 雷盈企业管理（上海）有限公司 | The anti money washing system and method for block chain digital asset |
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