Risk identification method and device for block chain address, equipment and program product

Abstract

The invention is suitable for the technical field of block chains, and provides a risk identification method and device for a block chain address, equipment and a program product. The method comprises the steps of obtaining N associated address sets of a target block chain address; the associated address set comprises a first-level associated address set to an Nth-level associated address set, the first-level associated address set comprises a first-level associated address, the Nth-level associated address set comprises an Nth-level associated address, and the first-level associated address is an address having a transaction record with the target block chain address; the Nth-level associated address is an address having a transaction record with the (N-1) th-level associated address; and determining risk factors of the target block chain address according to the risk information of the N associated address sets. According to the method, the risk factor of the target block chain address can be determined according to the risk information of the first-level association address set to the Nth-level association address set, whether the target block chain address is the shift connection address can be rapidly determined, and the security of the block chain is improved.

Description

Technical Field

[0001] The present application belongs to the field of blockchain technology, and in particular, relates to a risk identification method, device, equipment and program product for blockchain addresses. Background Art

[0002] There are many risky activities in the blockchain field, such as fraud, phishing, and money laundering. The common method to deal with these risky activities is to identify the risks of blockchain addresses, thereby identifying blockchain addresses with risk factors, and then monitoring blockchain addresses with risk factors to help users prevent risky activities.

[0003] However, since the cost of generating blockchain addresses is very low, once a risky address is identified and sanctioned, the person engaging in risky activities may continue to generate new blockchain addresses as successor addresses to replace the risky address for risky activities. Since it takes a certain amount of time to identify the risks of blockchain addresses, the existing risk identification methods for blockchain addresses are difficult to identify the risk factors of the successor address in a timely manner, and thus are unable to monitor the successor address in a timely manner, reducing the security of the blockchain. Summary of the invention

[0004] In view of this, the embodiments of the present application provide a risk identification method, device, equipment and program product for a blockchain address to solve the technical problem of low security of the existing blockchain.

[0005] In a first aspect, an embodiment of the present application provides a risk identification method for a blockchain address, comprising:

[0006] Obtain N associated address sets of the target blockchain address; wherein the N associated address sets include a first-level associated address set to an N-th-level associated address set, the first-level associated address set includes at least one first-level associated address, the N-th-level associated address set includes at least one N-th-level associated address, the first-level associated address is a blockchain address that has a transaction record with the target blockchain address, the N-th-level associated address is a blockchain address that has a transaction record with the N-1-th-level associated address, and N is an integer greater than or equal to 1;

[0007] Determine the risk factor of the target blockchain address based on the risk information of the N associated address sets.

[0008] Optionally, the risk information includes a risk address; and determining the risk factor of the target blockchain address according to the risk information of the N associated address sets includes:

[0009] Determining risk factors of the N associated address sets respectively according to the risk addresses of the N associated address sets;

[0010] Determine the risk factor of the target blockchain address according to the risk factors of each of the N associated address sets.

[0011] Optionally, determining the risk factors of the N associated address sets respectively according to the risk addresses of the N associated address sets includes:

[0012] For a first-level associated address set in the N associated address sets, obtaining each of the risky addresses in the first-level associated address set;

[0013] For each of the risk addresses, determining whether there is a risk transfer relationship between the risk address and the target blockchain address according to all transaction records between the risk address and the target blockchain address;

[0014] The risk factors of the first-level associated address set are determined according to the risk factors of all risk addresses that have a risk transfer relationship with the target blockchain address among the risk addresses.

[0015] Optionally, determining whether there is a risk transfer relationship between the risk address and the target blockchain address according to all transaction records between the risk address and the target blockchain address includes:

[0016] Determine, based on all the transaction records, a correlation score between the target blockchain address and the risk factor of the risk address;

[0017] If the association score is greater than a preset association score threshold, it is determined that there is a risk transfer relationship between the risk address and the target blockchain address.

[0018] Optionally, determining the risk factors of the first-level associated address set according to the risk factors of all risk addresses that have a risk transfer relationship with the target blockchain address among the risk addresses includes:

[0019] The union of the risk factors of all risk addresses that have a risk transfer relationship with the target blockchain address is used as the risk factors of the first-level associated address set.

[0020] Optionally, determining the risk factors of the N associated address sets respectively according to the risk addresses of the N associated address sets includes:

[0021] For an N-th level associated address set among the N associated address sets, obtaining each of the risky addresses in the N-th level associated address set;

[0022] For each of the risk addresses, determine whether there is a risk transfer relationship between the risk address and the N-1th level associated address set in the N associated address sets according to all transaction records between the risk address and the N-1th level associated address set;

[0023] The risk factor of the Nth level associated address set is determined according to the risk factors of all risk addresses that have a risk transfer relationship with the N-1th level associated address set among the risk addresses.

[0024] Optionally, determining the risk factor of the target blockchain address according to the risk factors of each of the N associated address sets includes:

[0025] The union of the risk factors of the N associated address sets is used as the risk factor of the target blockchain address.

[0026] In a second aspect, an embodiment of the present application provides a risk identification device for a blockchain address, including:

[0027] An address set acquisition unit, configured to acquire N associated address sets of a target blockchain address; wherein the N associated address sets include a first-level associated address set to an N-th-level associated address set, the first-level associated address set includes at least one first-level associated address, the N-th-level associated address set includes at least one N-th-level associated address, the first-level associated address is a blockchain address having a transaction record with the target blockchain address, the N-th-level associated address is a blockchain address having a transaction record with the N-1-th-level associated address, and N is an integer greater than or equal to 1;

[0028] A risk identification unit is used to determine the risk factor of the target blockchain address according to the risk information of the N associated address sets.

[0029] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, each step of the risk identification method for a blockchain address as described in the first aspect is implemented.

[0030] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the risk identification method for a blockchain address as described in the first aspect above are implemented.

[0031] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product is run on an electronic device, the electronic device executes each step of the risk identification method of the blockchain address as described in the first aspect above.

[0032] The blockchain address risk identification method, device, equipment and program product provided by the embodiments of the present application have the following beneficial effects:

[0033] The risk identification method of the blockchain address provided in the embodiment of the present application determines the risk factor of the target blockchain address by obtaining a first-level associated address set composed of each first-level associated address with direct transaction records with the target blockchain address, and obtaining an N-level associated address set composed of each N-level associated address with indirect transaction records with the target blockchain address, and then determining the risk factor of the target blockchain address based on the risk information of the first-level associated address set to the N-level associated address set. Considering that there must be direct or indirect transaction records between the risk address and the corresponding successor address, the risk identification method provided in the embodiment of the present application can determine the risk factor of the target blockchain address based on the risk information of each first-level associated address with direct transaction records with the target blockchain address, and the risk information of each N-level associated address with indirect transaction records with the target blockchain address, so it can quickly determine whether the target blockchain address is a successor address, and when the target blockchain address is a successor address, timely measures can be taken, thereby improving the security of the blockchain. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0035] Figure 1 A flowchart of a method for identifying risks of blockchain addresses provided in an embodiment of the present application;

[0036] Figure 2 A schematic diagram of the structure of a risk identification device for a blockchain address provided in an embodiment of the present application;

[0037] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] It should be noted that the terms used in the embodiments of the present application are only used to explain the specific embodiments of the present application, and are not intended to limit the present application. In the description of the embodiments of the present application, unless otherwise specified, "multiple" refers to two or more than two, and "at least one", "one or more" refers to one, two or more. The terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, it is defined that the "first" and "second" features can explicitly or implicitly include one or more of the features.

[0039] References to "one embodiment" or "some embodiments" etc. described in this specification mean that a particular feature, structure or characteristic described in conjunction with the embodiment is included in one or more embodiments of the present application. Thus, the phrases "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear at different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0040] The risk identification method for blockchain addresses provided in the embodiments of the present application can be applied to various scenarios where risk identification of target blockchain addresses is required.

[0041] The executors of the risk identification method for blockchain addresses provided in the embodiments of the present application may include but are not limited to electronic devices such as mobile phones, tablet computers, laptop computers, and desktop computers.

[0042] At present, when a risky address is identified and sanctioned, the person engaging in risky activities may generate a new blockchain address as a successor address to replace the risky address for risky activities. For example, the person engaging in risky activities may generate a successor address A to replace risky address B to carry out risky activities, or the person engaging in risky activities may generate a successor address A and a transit address C, and use transit address C as the transit address between successor address A and risky address B, so as to achieve the purpose of replacing risky address B with successor address A to carry out risky activities.

[0043] However, the current risk identification method for blockchain addresses requires a certain amount of time to identify whether a certain address has risk factors. Therefore, it is difficult for existing technologies to effectively and timely respond to the above-mentioned situation where a successor address is used to replace the risky address to carry out risky activities.

[0044] Based on this, an embodiment of the present application provides a risk identification method for a blockchain address. By executing the various steps of the risk identification method for a blockchain address provided by the embodiment of the present application by an electronic device, it is possible to quickly determine whether the target blockchain address is a succession address, thereby improving the security of the blockchain.

[0045] See also Figure 1 , Figure 1 The present invention provides a flowchart of a method for identifying a risk of a blockchain address in an embodiment of the present invention. The method for identifying a risk of a blockchain address in an embodiment of the present invention may include steps S101 to S102, which are described in detail as follows:

[0046] In S101, a set of N associated addresses of the target blockchain address is obtained.

[0047] In an embodiment of the present application, when risk identification of a target blockchain address is required, a set of N associated addresses of the target blockchain address can be obtained through an electronic device.

[0048] Among them, the N associated address sets include a first-level associated address set to an N-th level associated address set, the first-level associated address set includes at least one first-level associated address, the N-th level associated address set includes at least one N-th level associated address, the first-level associated address is a blockchain address that has a transaction record with the target blockchain address, the N-th level associated address is a blockchain address that has a transaction record with the N-1-th level associated address, and N is an integer greater than or equal to 1.

[0049] For example, if there are transaction records between the target blockchain address and address 1, address 2, and address 3, address 1, address 2, and address 3 can be used as the first-level associated addresses of the target blockchain address, and therefore the first-level associated address set can include address 1, address 2, and address 3.

[0050] If address 1 has transaction records with address 4 and address 5 in addition to the target blockchain address, address 2 has transaction records with address 6 in addition to the target blockchain address, and address 3 has transaction records with address 7 in addition to the target blockchain address, then address 4, address 5, address 6 and address 7 can be used as the second-level associated addresses of the target blockchain address, and therefore the second-level associated address set can include address 4, address 5, address 6 and address 7.

[0051] Similarly, if address 4 has transaction records with address 1 as well as address 10 and address 11, if address 5 has transaction records with address 1 as well as address 12, if address 6 has transaction records with address 2 as well as address 13, and if address 7 has transaction records with address 14 as well as address 3, then address 10, address 11, address 12, address 13 and address 14 can be used as the third-level associated addresses of the target blockchain address. Therefore, the third-level associated address set can include address 10, address 11, address 12, address 13 and address 14.

[0052] The Nth level associated address set can be obtained according to the same method as in the above example, which will not be described again here.

[0053] In one possible implementation, a user may input a transaction record time range into an electronic device so that the electronic device may obtain a set of N associated addresses of a target blockchain address based on the transaction record time range.

[0054] Exemplarily, the transaction record time range input by the user can be one year. If the target blockchain address has transaction records with address 1 and address 2 within one year, address 1 and address 2 can be used as the first-level associated addresses of the target blockchain address, and therefore address 1 and address 2 can be included in the first-level associated address set.

[0055] If address 1 has transaction records with address 4 within one year in addition to the target blockchain address, address 2 has transaction records with address 6 within one year in addition to the target blockchain address, and address 3 has transaction records with address 7 within one year in addition to the target blockchain address, then address 4, address 6 and address 7 can be used as the second-level associated addresses of the target blockchain address, and therefore the second-level associated address set can include address 4, address 6 and address 7.

[0056] The Nth level associated address set can be obtained according to the same method as in the above example, which will not be described again here.

[0057] The electronic device can obtain the transaction record of the target blockchain address, and can obtain a set of N associated addresses of the target blockchain address according to the method described in the above example.

[0058] In S102, the risk factor of the target blockchain address is determined based on the risk information of the N associated address sets.

[0059] In an embodiment of the present application, after obtaining a set of N associated addresses of the target blockchain address, the electronic device can determine the risk factor of the target blockchain address based on the risk information of the set of N associated addresses.

[0060] In a possible implementation, the risk information may include a risk address, based on which the electronic device may determine the risk factor of the target blockchain address through steps a to b, as described in detail as follows:

[0061] In step a, risk factors of the N associated address sets are determined respectively according to the risk addresses of the N associated address sets.

[0062] In this implementation, the electronic device can obtain each risky address in the first-level associated address set for the first-level associated address set in the N associated address sets. The risky address can be an address that the electronic device considers to be risky, that is, an address that the electronic device considers to be used for risky activities.

[0063] In a possible implementation, the electronic device can determine the risky addresses in each associated address set based on the transaction records of each associated address.

[0064] In another possible implementation, in order to improve the efficiency of risk identification, the electronic device may determine the sanctioned address as the risky address. The sanctioned address may be an address that is considered risky and sanctioned by the blockchain security agency. Based on this, the electronic device may obtain the information of the sanctioned address disclosed by the blockchain security agency, thereby determining each risky address in the first-level associated address set.

[0065] After obtaining each risk address in the first-level associated address set, the electronic device can determine, for each risk address in the risk addresses, whether there is a risk transfer relationship between the risk address and the target blockchain address based on all transaction records between the risk address and the target blockchain address.

[0066] Exemplarily, if address 1, address 2, and address 3 in the first-level associated address set are all risk addresses, the electronic device can determine whether there is a risk transfer relationship between address 1, address 2, and address 3 and the target blockchain address based on all transaction records between address 1, address 2, and address 3 and the target blockchain address.

[0067] Among them, the risk transfer relationship determination model can be called to determine whether there is a risk transfer relationship between the risk address and the target blockchain address based on all transaction records between the risk address and the target blockchain address. The risk transfer relationship determination model can be obtained in advance through training, and the input of the risk transfer relationship determination model can be the risk factor of the risk address and all transaction records between the risk address and the target blockchain address, and the output of the risk transfer relationship determination model can be whether there is a risk transfer relationship between the risk address and the target blockchain address.

[0068] To more clearly illustrate the function of the risk transfer relationship determination model, the following are some examples of determining whether a risk transfer relationship exists based on transaction records:

[0069] For example, if the risk factor of address 1 is phishing, and all transaction records between address 1 and the target blockchain address are transaction record 1, transaction record 2, and transaction record 3, and transaction record 1, transaction record 2, and transaction record 3 are all transfer transactions initiated from address 1 to the target blockchain address, the transaction currency is counterfeit currency, and the transaction quantity is very low, based on this, the risk transfer relationship determination model can determine that the target blockchain address is the phishing attack target of address 1, and therefore there is no risk transfer relationship between address 1 and the target blockchain address.

[0070] For example, if the risk factor of address 2 is fraud, and all transaction records between address 1 and the target blockchain address are transaction record 4, transaction record 5, and transaction record 6, and transaction record 4, transaction record 5, and transaction record 6 are all transfer transactions initiated by the target blockchain address to address 2, based on this, the risk transfer relationship determination model can determine that the target blockchain address is the fraud attack target of address 2, and therefore there is no risk transfer relationship between address 2 and the target blockchain address.

[0071] For example, if the risk factor of address 3 is money laundering, and all transaction records between address 3 and the target blockchain address are 100 transaction records, and these 100 transaction records include both transfer transactions initiated from the target blockchain address to address 3 and transfer transactions initiated from address 3 to the target blockchain address, and the transfer amounts of these 100 transaction records are relatively high, based on this, the risk transfer relationship determination model can determine that there is a risk transfer relationship between address 3 and the target blockchain address.

[0072] More specifically, the risk transfer relationship determination model can determine the correlation score between the target blockchain address and the risk factors of the risk address based on all transaction records between the risk address and the target blockchain address. If the correlation score is greater than a preset correlation score threshold, it is determined that there is a risk transfer relationship between the risk address and the target blockchain address. If the correlation score is less than the preset correlation score threshold, it is determined that there is no risk transfer relationship between the risk address and the target blockchain address. The specific value of the correlation score threshold and the specific method for determining the correlation score can be set according to actual needs and are not limited here.

[0073] After the electronic device determines each risk address of the first-level associated address set and determines whether there is a risk transfer relationship between each associated address and the target blockchain address, the electronic device can determine the risk factors of the first-level associated address set based on the risk factors of all risk addresses that have a risk transfer relationship with the target blockchain address among the risk addresses.

[0074] For example, if the risk addresses in the first-level associated address set that have a risk transfer relationship with the target blockchain address are address 2 and address 3, and the risk factor of address 2 is fraud, and the risk factor of address 3 is money laundering, then the risk factors of the first-level associated address set can be determined based on the risk factors of address 2 and address 3.

[0075] In a possible implementation, the electronic device may determine the risk factor of the risk address with the highest correlation score between the target blockchain address and the risk factor of the risk address as the risk factor of the first-level associated address set. Exemplarily, if the risk addresses in the first-level associated address set that have a risk transfer relationship with the target blockchain address are address 2 and address 3, and the risk factor of address 2 is fraud, the risk factor of address 3 is money laundering, and the correlation score between the target blockchain address and the risk factor of address 2 is higher than the correlation score between the target blockchain address and the risk factor of address 3, then the electronic device may determine the risk factor fraud of address 2 as the risk factor of the first-level associated address set.

[0076] In another possible implementation, the electronic device may use the union of the risk factors of all risk addresses that have a risk transfer relationship with the target blockchain address as the risk factor of the first-level associated address set. For example, if the risk addresses that have a risk transfer relationship with the target blockchain address in the first-level associated address set are address 2 and address 3, and the risk factor of address 2 is fraud, and the risk factor of address 3 is money laundering, then the electronic device may jointly determine the risk factor fraud of address 2 and the risk factor money laundering of address 3 as the risk factor of the first-level associated address set.

[0077] The above describes a specific method for determining the risk factor of the first level associated address set among N associated address sets. The following describes a specific method for determining the risk factor of the Nth level associated address set among N associated address sets.

[0078] The electronic device may obtain each risk address in the N-th level associated address set for the N-th level associated address set in the N associated address sets.

[0079] Exemplarily, if the electronic device obtains a first-level associated address set, a second-level associated address set, and a third-level associated address set in step 101, the electronic device can obtain each risk address in the second-level associated address set for the second-level associated address set, and can obtain each risk address in the third-level associated address set for the third-level associated address set.

[0080] After obtaining each risk address in the Nth level associated address set, the electronic device can determine, for each risk address among the various risk addresses, whether there is a risk transfer relationship between the risk address and the N-1th level associated address set in the N associated address sets based on all transaction records between the risk address and the N-1th level associated address set.

[0081] Exemplarily, each risk address in the second-level associated address set may be address 4 and address 5. Based on this, the electronic device can determine whether there is a risk transfer relationship between address 4 and the first-level associated address set based on all transaction records between address 4 and the first-level associated address set. In particular, since in the example of S101, there is a transaction record between address 4 and address 1 in the first-level associated address set, the electronic device can determine whether there is a risk transfer relationship between address 4 and address 1 based on all transaction records between address 4 and address 1. Similarly, the electronic device can determine whether there is a risk transfer relationship between address 4 and address 1 based on all transaction records between address 5 and address 1.

[0082] Exemplarily, each risk address in the third-level associated address set may be address 10 and address 11. Based on this, the electronic device can determine whether there is a risk transfer relationship between address 10 and the second-level associated address set based on all transaction records between address 10 and the second-level associated address set for address 10. In particular, since in the example of S101, there is a transaction record between address 10 and address 4 in the second-level associated address set, the electronic device can determine whether there is a risk transfer relationship between address 10 and address 4 based on all transaction records between address 10 and address 4. Similarly, the electronic device can determine whether there is a risk transfer relationship between address 11 and address 4 based on all transaction records between address 11 and address 4.

[0083] Among them, the specific implementation method of determining whether there is a risk transfer relationship between two addresses can refer to the relevant description provided in the above embodiment of the present application, and will not be repeated here.

[0084] After determining whether there is a risk transfer relationship between each risk address in the Nth level associated address set and the N-1th level associated address set, the electronic device can determine the risk factors of the Nth level associated address set based on the risk factors of all risk addresses in the individual risk addresses that have a risk transfer relationship with the N-1th level associated address set.

[0085] For example, if in the second-level associated address set, there is a risk transfer relationship between address 4 and the first-level associated address set, but there is no risk transfer relationship between address 5 and the first-level associated address set, then the risk factor corresponding to address 4 can be determined as the risk factor of the second-level associated address set.

[0086] For example, if in the third-level associated address set, address 10 and address 11 both have a risk transfer relationship with the second-level associated address set, the risk factors corresponding to address 10 and the risk factors corresponding to address 11 can be jointly determined as risk factors of the third-level associated address set.

[0087] Based on this, the electronic device can respectively determine the risk factors of each of the N associated address sets.

[0088] In step b, the risk factor of the target blockchain address is determined based on the risk factors of each of the N associated address sets.

[0089] In an embodiment of the present application, after respectively determining the risk factors of each of the N associated address sets, the electronic device may determine the risk factor of the target blockchain address based on the risk factors of each of the N associated address sets.

[0090] Specifically, the electronic device may use the union of the risk factors of the N associated address sets as the risk factor of the target blockchain address.

[0091] For example, if the risk factor of the first-level associated address set is fraud, the risk factor of the second-level associated address set is money laundering, and the risk factor of the third-level associated address set is phishing, then the risk factors of the target blockchain address can be determined to be fraud, money laundering, and phishing.

[0092] The following is an example to illustrate the principle that the risk identification method for blockchain addresses provided by the embodiments of the present application can quickly determine whether the target blockchain address is a successor address.

[0093] If the person engaging in risky activities generates a successor address A and a transit address C after risk address B is sanctioned, and uses transit address C as the transit address between successor address A and risk address B to achieve the purpose of using successor address A to replace risk address B to carry out risky activities, then there must be a transaction record between the successor address A and the transit address C, and there must be a transaction record between the transit address C and the risk address B. Therefore, the successor address A can be used as the target blockchain address, and the transit address C can be the first-level associated address, and the risk address B can be the second-level associated address.

[0094] By executing the various steps of the risk identification method for the blockchain address provided in the embodiment of the present application on the electronic device with the successor address A as the target blockchain address, the risk factors of the successor address A determined to be obtained must include the risk factors corresponding to the risk address B. Therefore, the successor address A can be quickly determined to be the successor address of the risk address B, so that corresponding actions can be taken in a timely manner to manage the successor address A, thereby improving the security of the blockchain.

[0095] It can be seen from the above that the risk identification method for the blockchain address provided in the embodiment of the present application determines the risk factor of the target blockchain address by obtaining a first-level associated address set composed of each first-level associated address with direct transaction records with the target blockchain address, and obtaining an N-level associated address set composed of each N-level associated address with indirect transaction records with the target blockchain address, and then determining the risk factor of the target blockchain address based on the risk information of the first-level associated address set to the N-level associated address set. Considering that there must be direct or indirect transaction records between the risk address and the corresponding successor address, the risk identification method provided in the embodiment of the present application can determine the risk factor of the target blockchain address based on the risk information of each first-level associated address with direct transaction records with the target blockchain address, and the risk information of each N-level associated address with indirect transaction records with the target blockchain address. Therefore, it can quickly determine whether the target blockchain address is a successor address, and when the target blockchain address is a successor address, timely measures can be taken, thereby improving the security of the blockchain.

[0096] Based on the risk identification method of blockchain addresses provided in the above embodiment, the present application further provides a risk identification device for blockchain addresses that implements the above method embodiment. Figure 2 , Figure 2 A schematic diagram of a risk identification device for a blockchain address provided in an embodiment of the present application. Figure 2 As shown, the risk identification device 20 for blockchain addresses may include: an address set acquisition unit 21 and a risk identification unit 22. Among them:

[0097] The address set acquisition unit 21 is used to acquire N associated address sets of the target blockchain address; wherein the N associated address sets include a first-level associated address set to an N-th-level associated address set, the first-level associated address set includes at least one first-level associated address, the N-th-level associated address set includes at least one N-th-level associated address, the first-level associated address is a blockchain address that has a transaction record with the target blockchain address, the N-th-level associated address is a blockchain address that has a transaction record with the N-1-th-level associated address, and N is an integer greater than or equal to 1.

[0098] The risk identification unit 22 is used to determine the risk factor of the target blockchain address based on the risk information of the N associated address sets.

[0099] Optionally, the risk identification unit 22 is specifically configured to:

[0100] Determine the risk factors of the N associated address sets according to their respective risk addresses;

[0101] The risk factor of the target blockchain address is determined based on the risk factors of each of the N associated address sets.

[0102] Optionally, the risk identification unit 22 is specifically configured to:

[0103] For a first-level associated address set in the N associated address sets, obtaining each risk address of the first-level associated address set;

[0104] For each risk address among the risk addresses, determine whether there is a risk transfer relationship between the risk address and the target blockchain address based on all transaction records between the risk address and the target blockchain address;

[0105] According to the risk factors of all risk addresses that have a risk transfer relationship with the target blockchain address among the risk addresses, the risk factors of the first-level associated address set are determined.

[0106] Optionally, the risk identification unit 22 is specifically configured to:

[0107] Based on all transaction records, determine the correlation score between the target blockchain address and the risk factors of the risk address;

[0108] If the correlation score is greater than the preset correlation score threshold, it is determined that there is a risk transfer relationship between the risk address and the target blockchain address.

[0109] Optionally, the risk identification unit 22 is specifically configured to:

[0110] The union of the risk factors of all risk addresses that have a risk transfer relationship with the target blockchain address is taken as the risk factor of the first-level associated address set.

[0111] Optionally, the risk identification unit 22 is specifically configured to:

[0112] For an N-th level associated address set in the N associated address sets, obtaining each risk address of the N-th level associated address set;

[0113] For each risk address among the risk addresses, determine whether there is a risk transfer relationship between the risk address and the N-1th level associated address set in the N associated address sets based on all transaction records between the risk address and the N-1th level associated address set;

[0114] The risk factors of the Nth level associated address set are determined according to the risk factors of all risk addresses that have a risk transfer relationship with the N-1th level associated address set among the risk addresses.

[0115] Optionally, the risk identification unit 22 is specifically configured to:

[0116] The union of the risk factors of the N associated address sets is used as the risk factor of the target blockchain address.

[0117] See also Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 As shown, the electronic device 3 provided in this embodiment may include: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30, such as a program corresponding to the risk identification method for blockchain addresses. When the processor 30 executes the computer program 32, the steps in the above-mentioned risk identification method embodiment applied to blockchain addresses are implemented, such as Figure 1 Alternatively, when the processor 30 executes the computer program 32, the functions of each module / unit in the risk identification device embodiment of the blockchain address are realized, for example Figure 2 The functions of the units 21 to 22 are shown.

[0118] Exemplarily, the computer program 32 may be divided into one or more modules / units, one or more modules / units are stored in the memory 31 and executed by the processor 30 to complete the present application. One or more modules / units may be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program 32 in the electronic device 3. For example, the computer program 32 may be divided into an address set acquisition unit and a risk identification unit. For the specific functions of each unit, please refer to Figure 2 The relevant descriptions in the corresponding embodiments are not repeated here.

[0119] Those skilled in the art will understand that Figure 3 This is only an example of the electronic device 3 and does not constitute a limitation on the electronic device 3 , which may include more or less components than those shown in the figure, or a combination of certain components, or different components.

[0120] The processor 30 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0121] The memory 31 may be an internal storage unit of the electronic device 3, such as a hard disk or memory of the electronic device 3. The memory 31 may also be an external storage device of the electronic device 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, or a flash card, etc., equipped on the electronic device 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the electronic device 3. The memory 31 is used to store computer programs and other programs and data required by the electronic device. The memory 31 may also be used to temporarily store data that has been output or is to be output.

[0122] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional units as needed, that is, the internal structure of the risk identification device of the blockchain address can be divided into different functional units to complete all or part of the functions described above. The functional units in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0123] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0124] An embodiment of the present application provides a computer program product. When the computer program product is executed on an electronic device, the electronic device implements the steps in the above-mentioned method embodiments.

[0125] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0126] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0127] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A risk identification method for blockchain addresses, characterized in that: include: Obtain N associated address sets of the target blockchain address; wherein the N associated address sets include a first-level associated address set to an N-th-level associated address set, the first-level associated address set includes at least one first-level associated address, the N-th-level associated address set includes at least one N-th-level associated address, the first-level associated address is a blockchain address that has a transaction record with the target blockchain address, the N-th-level associated address is a blockchain address that has a transaction record with the N-1-th-level associated address, and N is an integer greater than or equal to 1; Determine the risk factor of the target blockchain address based on the risk information of the N associated address sets.

2. The method according to claim 1, characterized in that The risk information includes a risk address; and determining the risk factor of the target blockchain address according to the risk information of the N associated address sets includes: Determining risk factors of the N associated address sets respectively according to the risk addresses of the N associated address sets; Determine the risk factor of the target blockchain address according to the risk factors of each of the N associated address sets.

3. The method according to claim 2, characterized in that The determining, according to the risk addresses of the N associated address sets, the risk factors of the N associated address sets respectively, includes: For a first-level associated address set in the N associated address sets, obtaining each of the risky addresses in the first-level associated address set; For each of the risk addresses, determining whether there is a risk transfer relationship between the risk address and the target blockchain address according to all transaction records between the risk address and the target blockchain address; The risk factors of the first-level associated address set are determined according to the risk factors of all risk addresses that have a risk transfer relationship with the target blockchain address among the risk addresses.

4. The method according to claim 3, characterized in that The determining, based on all transaction records between the risk address and the target blockchain address, whether there is a risk transfer relationship between the risk address and the target blockchain address includes: Determine, based on all the transaction records, a correlation score between the target blockchain address and the risk factor of the risk address; If the association score is greater than a preset association score threshold, it is determined that there is a risk transfer relationship between the risk address and the target blockchain address.

5. The method according to claim 3, characterized in that: Determining the risk factors of the first-level associated address set according to the risk factors of all risk addresses that have a risk transfer relationship with the target blockchain address among the risk addresses includes: The union of the risk factors of all risk addresses that have a risk transfer relationship with the target blockchain address is used as the risk factors of the first-level associated address set.

6. The method according to claim 2, characterized in that The determining, according to the risk addresses of the N associated address sets, the risk factors of the N associated address sets respectively, includes: For an N-th level associated address set among the N associated address sets, obtaining each of the risky addresses in the N-th level associated address set; For each of the risk addresses, determine whether there is a risk transfer relationship between the risk address and the N-1th level associated address set in the N associated address sets according to all transaction records between the risk address and the N-1th level associated address set; The risk factor of the Nth level associated address set is determined according to the risk factors of all risk addresses that have a risk transfer relationship with the N-1th level associated address set among the risk addresses.

7. The method according to any one of claims 2 to 6, characterized in that: The determining the risk factor of the target blockchain address according to the risk factors of each of the N associated address sets includes: The union of the risk factors of the N associated address sets is used as the risk factor of the target blockchain address.

8. A risk identification device for blockchain addresses, characterized in that: include: An address set acquisition unit, configured to acquire N associated address sets of a target blockchain address; wherein the N associated address sets include a first-level associated address set to an N-th-level associated address set, the first-level associated address set includes at least one first-level associated address, the N-th-level associated address set includes at least one N-th-level associated address, the first-level associated address is a blockchain address having a transaction record with the target blockchain address, the N-th-level associated address is a blockchain address having a transaction record with the N-1-th-level associated address, and N is an integer greater than or equal to 1; A risk identification unit is used to determine the risk factor of the target blockchain address according to the risk information of the N associated address sets.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, each step of the risk identification method for a blockchain address as described in any one of claims 1 to 7 is implemented.

10. A computer program product, when the computer program product is run on a computer device, enables the computer device to execute the risk identification method of the blockchain address as described in any one of claims 1 to 7.

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