KR20210110442A - Method, apparatus and computer program for clustering cryptocurrency accounts using artificial intelligence - Google Patents

Abstract

Provided are a clustering method of a cryptocurrency account using artificial intelligence, a device thereof, and a computer program thereof. According to various embodiments of the present invention, the clustering method of a cryptocurrency account using artificial intelligence is performed by a computing device. The method comprises the following steps of: collecting cryptocurrency transaction information generated between a plurality of cryptocurrency accounts; generating a clustering rule for clustering the plurality of cryptocurrency accounts by using the cryptocurrency transaction information; and clustering the plurality of cryptocurrency accounts by using the generated clustering rule. Therefore, the amount of operations required to learn and analyze data can be dramatically reduced.

Description

A method, device, and computer program for clustering cryptocurrency accounts using artificial intelligence

Various embodiments of the present invention relate to a method, apparatus, and computer program for clustering cryptocurrency accounts using artificial intelligence.

As cryptocurrency (e.g., Bitcoin), which was used among a small number of users, gradually gained attention from the media and the general public, the number of users began to increase exponentially, which caused a sharp increase in the price and transaction volume of cryptocurrency. The bubble phenomenon occurred and had a profound impact on society.

Various industries using the cryptocurrency system are developing, but the evaluation of the impact it will have on society and the prospects for future development are greatly mixed, and the responses of each country are also showing a big difference. Countries that negatively evaluate the impact of cryptocurrencies are trying to alleviate the social shock caused by the introduction of new technologies by making the entire use of cryptocurrencies illegal or restricting the use of cryptocurrencies by financial institutions.

Analyzing the price and volume of cryptocurrency, the trading volume decreased due to a sharp price drop in early 2018, but the trading volume recovered rapidly in 2019, indicating that the global cryptocurrency market has matured one step after the bubble.

In addition, considering that cryptocurrency has been confirmed as an alternative investment asset in the financial market, and Brazil, Argentina, and Venezuela are actively using cryptocurrency as an alternative to fiat currency, the cryptocurrency trading market is expected to continue to grow in the future. do.

On the other hand, since cryptocurrencies are inherently decentralized and anonymous, they are highly likely to be abused as criminal means (eg, ICO fraud, money laundering, and black market payment methods, etc.). In fact, it is estimated that the expected damage caused by the abused cryptocurrency in 2019 was about 5.2 trillion won, a 2.5-fold increase from the previous year.

Accordingly, government agencies and cryptocurrency-related organizations in each country are working to prevent and track cryptocurrency-related crimes in various ways, as well as create new regulations to prevent cryptocurrency crimes such as money laundering. Trying hard.

However, there is a problem that the subject to comply with the regulations is very broad, including governments and cryptocurrencies handling companies. In addition, although technology development should be done according to the trend of strengthening regulations, there is still a problem that there is a limit to crime prevention and tracking due to technical limitations. For example, in the prior art, accounts were tracked using a manual data collection method, but due to advanced money laundering technology, the amount of transactions that must be viewed during tracking increases, making it practically impossible to view all transaction volumes. In addition, in the case of the manual data collection method, only a limited number of accounts can be identified, and there is a problem that a huge cost is required for account tracking.

Korean Patent Publication No. 10-2019-0123952 (published on November 4, 2019)

The problem to be solved by the present invention is that one person can own several cryptocurrency accounts, and unlike banking transactions, not only 1-to-1 but also 1-to-n, n-to-1, n Clustering to cluster cryptocurrency accounts from a specific management pattern of cryptocurrency wallet software, considering that -to-n transactions are possible and multiple cryptocurrency accounts are managed using cryptocurrency wallet software due to the characteristics of cryptocurrency wallets. It is to provide a method, apparatus, and computer program for clustering cryptocurrency accounts using artificial intelligence capable of generating rules and clustering cryptocurrency accounts accordingly.

The problems to be solved by the present invention are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the following description.

The method for clustering cryptocurrency accounts using artificial intelligence according to an embodiment of the present invention for solving the above problems is a method performed by a computing device, and collects cryptocurrency transaction information generated between a plurality of cryptocurrency accounts generating a clustering rule for clustering the plurality of cryptocurrency accounts using the cryptocurrency transaction information, and clustering the plurality of cryptocurrency accounts using the generated clustering rule. can do.

In various embodiments, the generating of the clustering rule includes generating a first clustering rule for clustering a cryptocurrency account according to a cryptocurrency deposit history based on the cryptocurrency transaction information, The step of clustering the currency accounts includes, using the first clustering rule, when there are a plurality of cryptocurrency deposit histories in one transaction, a plurality of cryptocurrency accounts respectively corresponding to the plurality of cryptocurrency deposit histories. may include the step of clustering into cryptocurrency accounts of the same owner.

In various embodiments, the generating of the clustering rule includes determining whether to reuse each of a plurality of cryptocurrency account addresses based on the cryptocurrency transaction information, and clustering the cryptocurrency accounts according to whether or not the reuse is performed. and generating a clustering rule, wherein the clustering of the plurality of cryptocurrency accounts uses the second clustering rule to pay a cryptocurrency account in which an address is reused among the plurality of cryptocurrency accounts. It may include clustering into accounts, and clustering cryptocurrency accounts whose addresses are not reused as balance (Change) accounts.

In various embodiments, the generating of the clustering rule may include selecting a cryptocurrency account according to whether the total amount of cryptocurrency withdrawn from each of a plurality of cryptocurrency accounts is a Round-Number based on the cryptocurrency transaction information. and generating a third clustering rule for clustering, wherein the clustering of the plurality of cryptocurrency accounts includes using the third clustering rule, when the total amount of cryptocurrency withdrawn is a round number, the round number of Clustering the cryptocurrency account in which the cryptocurrency is deposited into a payment account and using the third clustering rule, if the total amount of cryptocurrency withdrawn is not a round number, the cryptographic currency in which the non-rounded number is deposited It may include clustering a monetary account into a change account.

In various embodiments, the generating of the clustering rule includes generating a fourth clustering rule for clustering cryptocurrency accounts according to the size of the cryptocurrency to be deposited and withdrawn based on the cryptocurrency transaction information, The clustering of the cryptocurrency accounts may include clustering the plurality of cryptocurrency accounts into any one of a payment account and a balance account using the fourth clustering rule.

In various embodiments, the generating of the clustering rule includes: generating a fifth clustering rule for clustering cryptocurrency accounts according to a script method of a transaction indicating cryptocurrency withdrawal based on the cryptocurrency transaction information The step of clustering the plurality of cryptocurrency accounts includes, using the fifth clustering rule, when the script methods of the plurality of transactions indicating cryptocurrency withdrawal are different from each other, the same script as the transaction indicating cryptocurrency deposit It may include selecting a cryptocurrency withdrawal method having a method, and clustering a cryptocurrency account that is a target of the selected cryptocurrency withdrawal into a balance account.

In various embodiments, the generating of the clustering rule includes the step of generating a sixth clustering rule for clustering cryptocurrency accounts based on the order of the cryptocurrency transaction history included in the transaction based on the cryptocurrency transaction information. The step of clustering the plurality of cryptocurrency accounts includes, by using the sixth clustering rule, a cryptocurrency account corresponding to the last cryptocurrency transaction history among the plurality of cryptocurrency transaction histories included in the transaction. It may include clustering into balance accounts.

In various embodiments, the generating of the clustering rule includes generating a seventh clustering rule for clustering cryptocurrency accounts according to the value of the cryptocurrency to be withdrawn based on the cryptocurrency transaction information, In the step of clustering the cryptocurrency account, clustering the cryptocurrency account corresponding to the cryptocurrency withdrawal history in which the value of the cryptocurrency withdrawn from among the plurality of cryptocurrency withdrawal histories is different from the balance account by using the seventh clustering rule. may include the step of

In various embodiments, the collecting of the cryptocurrency transaction information includes: obtaining information on two or more cryptocurrency accounts of the same owner among the plurality of cryptocurrency accounts; and cryptocurrency transaction information between the two or more cryptocurrency accounts. and obtaining a cryptocurrency transaction pattern between the two or more cryptocurrency accounts based on and learning the information and the cryptocurrency transaction pattern, wherein the artificial intelligence model may be a model for determining whether a transaction is between cryptocurrency accounts owned by the same owner by analyzing the cryptocurrency transaction information.

In various embodiments, the generating of the clustering rule may include extracting the same owner clustering rule for clustering the same cryptocurrency account among the cryptocurrency accounts by using the cryptocurrency transaction information, and the two or more cryptocurrency accounts. The method may include verifying the extracted same-owner clustering rule using account information and the cryptocurrency transaction pattern, and generating the same-owner clustering rule based on the verification result.

In various embodiments, the step of the owner obtaining information on the same two or more cryptocurrency accounts includes visualizing the cryptocurrency transaction information and analyzing the visualized cryptocurrency transaction information to use each of the plurality of cryptocurrency accounts Using the method for determining the usage of the cryptocurrency account to determine It may include the step of obtaining.

A cryptocurrency account clustering apparatus using artificial intelligence according to another embodiment of the present invention for solving the above-described problems includes a memory for storing one or more instructions and a processor for executing the one or more instructions stored in the memory, The processor may perform the method of clustering cryptocurrency accounts using artificial intelligence according to an embodiment of the present invention by executing the one or more instructions.

A computer program according to another embodiment of the present invention for solving the above problems is combined with a computer that is hardware, so that the computer can perform the method of clustering cryptocurrency accounts using artificial intelligence according to an embodiment of the present invention. may be stored in a readable recording medium.

Other specific details of the invention are included in the detailed description and drawings.

According to various embodiments of the present invention, by creating a clustering rule for clustering cryptocurrency accounts and clustering cryptocurrency accounts accordingly, data for learning and analyzing cryptocurrency transaction history using an artificial intelligence model is generated Thus, there is an advantage that the amount of computation required to learn and analyze data can be dramatically reduced.

Effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

1 is a diagram illustrating a system for clustering cryptocurrency accounts using artificial intelligence according to an embodiment of the present invention.
2 is a hardware configuration diagram of a cryptocurrency account clustering apparatus using artificial intelligence according to another embodiment of the present invention.
3 is a flowchart of a method for clustering a cryptocurrency account using artificial intelligence according to another embodiment of the present invention.
4 is a flowchart of a method of clustering cryptocurrency accounts using a first clustering rule, according to various embodiments.
5 is a flowchart of a method of clustering cryptocurrency accounts using a second clustering rule, according to various embodiments.
6 is a flowchart of a method of clustering cryptocurrency accounts using the third clustering rule, in various embodiments.
7 is a flowchart of a method of clustering cryptocurrency accounts using the fourth clustering rule, in various embodiments.
8 is a flowchart of a method of clustering cryptocurrency accounts using the fifth clustering rule, in various embodiments.
9 is a flowchart of a method of clustering cryptocurrency accounts using the sixth clustering rule, according to various embodiments.
10 is a flowchart of a method of clustering cryptocurrency accounts using the seventh clustering rule, according to various embodiments.
11 is a flowchart of a method of determining the usage of a cryptocurrency account performed by the cryptocurrency account clustering apparatus in various embodiments.
12 is a diagram illustrating cryptocurrency transaction information visualized according to a method of determining the use of a cryptocurrency account, in various embodiments.
13 is a diagram illustrating a configuration for determining the use of a cryptocurrency account using a graph-based semi-map learning model in various embodiments.
14 is a diagram illustrating a configuration for determining the use of a cryptocurrency account using an account address-based supervised learning model, in various embodiments.
15 is a diagram illustrating a configuration for determining the use of a cryptocurrency account using an owner-based semi-map learning model, in various embodiments.
16 is a diagram illustrating a configuration for determining the use of a cryptocurrency account by using image-type cryptocurrency account information and keywords obtained from a website, in various embodiments.

Advantages and features of the present invention and methods of achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and only these embodiments allow the disclosure of the present invention to be complete, and those of ordinary skill in the art to which the present invention pertains. It is provided to fully understand the scope of the present invention to those skilled in the art, and the present invention is only defined by the scope of the claims.

The terminology used herein is for the purpose of describing the embodiments and is not intended to limit the present invention. As used herein, the singular also includes the plural unless specifically stated otherwise in the phrase. As used herein, “comprises” and/or “comprising” does not exclude the presence or addition of one or more other components in addition to the stated components. Like reference numerals refer to like elements throughout, and "and/or" includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various elements, these elements are not limited by these terms, of course. These terms are only used to distinguish one component from another. Accordingly, it goes without saying that the first component mentioned below may be the second component within the spirit of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used herein will have the meaning commonly understood by those of ordinary skill in the art to which this invention belongs. In addition, terms defined in a commonly used dictionary are not to be interpreted ideally or excessively unless specifically defined explicitly.

As used herein, the term “unit” or “module” refers to a hardware component such as software, FPGA, or ASIC, and “unit” or “module” performs certain roles. However, “part” or “module” is not meant to be limited to software or hardware. A “unit” or “module” may be configured to reside on an addressable storage medium or to reproduce one or more processors. Thus, by way of example, “part” or “module” refers to components such as software components, object-oriented software components, class components and task components, processes, functions, properties, Includes procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays and variables. Components and functionality provided within “parts” or “modules” may be combined into a smaller number of components and “parts” or “modules” or as additional components and “parts” or “modules”. can be further separated.

Spatially relative terms "below", "beneath", "lower", "above", "upper", etc. It can be used to easily describe the correlation between a component and other components. A spatially relative term should be understood as a term that includes different directions of components during use or operation in addition to the directions shown in the drawings. For example, when a component shown in the drawing is turned over, a component described as “beneath” or “beneath” of another component may be placed “above” of the other component. can Accordingly, the exemplary term “below” may include both directions below and above. Components may also be oriented in other orientations, and thus spatially relative terms may be interpreted according to orientation.

In this specification, a computer means all types of hardware devices including at least one processor, and may be understood as encompassing software configurations operating in the corresponding hardware device according to embodiments. For example, a computer may be understood to include, but is not limited to, smart phones, tablet PCs, desktops, notebooks, and user clients and applications running on each device.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Each step described in this specification is described as being performed by a computer, but the subject of each step is not limited thereto, and at least a portion of each step may be performed in different devices according to embodiments.

1 is a diagram illustrating a system for clustering cryptocurrency accounts using artificial intelligence according to an embodiment of the present invention.

Referring to FIG. 1 , a system for clustering cryptocurrency accounts using artificial intelligence according to an embodiment of the present invention may include a cryptocurrency account clustering apparatus 100 , a user terminal 200 , and an external server 300 . .

Here, the clustering system of a cryptocurrency account using artificial intelligence shown in FIG. 1 is according to an embodiment, and its components are not limited to the embodiment shown in FIG. 1, and add, change, or delete as necessary. can be

In one embodiment, the cryptocurrency account clustering device 100 collects cryptocurrency transaction information (eg, a transaction) generated between a plurality of cryptocurrency accounts, and uses the collected cryptocurrency transaction information to obtain a plurality of passwords. Each of the monetary accounts can be clustered.

In various embodiments, the cryptocurrency account clustering apparatus 100 may generate a clustering rule for clustering a plurality of cryptocurrency accounts using cryptocurrency transaction information, and a plurality of cryptocurrency accounts using the generated clustering rule. can be clustered. Here, the clustering rule may be a heuristic clustering rule, but is not limited thereto.

In various embodiments, the cryptocurrency account clustering apparatus 100 uses cryptocurrency transaction information for a first clustering rule for clustering cryptocurrency accounts according to cryptocurrency input history, a plurality of cryptocurrency account addresses for each A second clustering rule for determining whether to reuse, and clustering cryptocurrency accounts according to whether or not to reuse, and whether the total amount of cryptocurrency withdrawn from each of a plurality of cryptocurrency accounts is a Round-Number The third clustering rule for clustering, the fourth clustering rule for clustering cryptocurrency accounts according to the size of the cryptocurrency to be deposited and withdrawn, the third clustering rule for clustering cryptocurrency accounts according to the script method of the transaction indicating cryptocurrency output Create a 6th clustering rule for clustering cryptocurrency accounts based on the order of the cryptocurrency transaction history included in the transaction based on the 5 clustering rule, and a 7th clustering rule for clustering cryptocurrency accounts according to the value of the cryptocurrency to be withdrawn can do. However, the present invention is not limited thereto.

In one embodiment, the user terminal 200 may be connected to the cryptocurrency account clustering apparatus 100 through the network 400, and various information related to the clustering result of the cryptocurrency account from the cryptocurrency account clustering apparatus 100 can be provided For example, the user terminal 200 may include at least one of a smartphone, a tablet PC, a notebook computer, and a desktop having a display in at least a partial area of the user terminal 200, and a cryptocurrency account clustering device through the display Various information provided from the cryptocurrency account clustering apparatus 100 may be output by outputting a user interface (UI) provided by 100 .

In one embodiment, the external server 300 may be connected to the cryptocurrency account clustering device 100 through the network 400, and various information ( e.g., cryptocurrency transaction information between cryptocurrency accounts). For example, the external server 300 may be a cryptocurrency transaction brokerage server that provides a service that brokers cryptocurrency transactions, and performs transactions between cryptocurrency accounts through a cryptocurrency transaction brokerage service that brokers cryptocurrency transactions. It is possible to store the cryptocurrency transaction information generated by the process, and provide the stored cryptocurrency transaction information to the cryptocurrency account clustering device 100 . However, the present invention is not limited thereto.

In various embodiments, the external server 300 may receive and store various data (eg, cryptocurrency account clustering result data) from the cryptocurrency account clustering apparatus 100 . For example, the external server 300 may be a storage device (eg, storage) separately provided outside the cryptocurrency account clustering device 100 .

In various embodiments, the cryptocurrency account clustering apparatus 100 may include a storage device (not shown) for storing various data generated by the cryptocurrency account clustering apparatus 100 therein. For example, the cryptocurrency account clustering apparatus 100 may systematically store and manage tens of billions of account information and transaction information using a storage device provided therein. However, the present invention is not limited thereto. Hereinafter, referring to FIG. 2 , a cryptocurrency account clustering apparatus 100 according to another embodiment of the present invention will be described.

2 is a hardware configuration diagram of a cryptocurrency account clustering apparatus using artificial intelligence according to another embodiment of the present invention.

Referring to FIG. 2 , the cryptocurrency account clustering device 100 (hereinafter, “computing device 100”) according to another embodiment of the present invention may include a processor 110 and a memory 120 . In various embodiments, the computing device 100 may further include a network interface (or communication interface) (not shown), storage (not shown), and a bus (not shown).

In an embodiment, the processor 110 may control the overall operation of each component of the computing device 100 . The processor 110 may include a central processing unit (CPU), a micro processor unit (MPU), a micro controller unit (MCU), or any type of processor well known in the art.

In various embodiments, the processor 110 may perform an operation on at least one application or program for executing the method according to the embodiments of the present invention. In various embodiments, the processor 110 includes one or more cores (not shown) and a graphic processing unit (not shown) and/or a connection path (eg, a bus, etc.) for transmitting and receiving signals to and from other components. can do.

In various embodiments, the processor 110 temporarily and/or permanently stores a signal (or data) processed inside the processor 110 , a random access memory (RAM) and a read access memory (ROM). -Only Memory, not shown) may be further included. In addition, the processor 110 may be implemented in the form of a system on chip (SoC) including at least one of a graphic processing unit, a RAM, and a ROM.

In one embodiment, the processor 110 executes one or more instructions stored in the memory 120, so that a method to be described with reference to FIGS. 3 to 10 (eg, a method for clustering cryptocurrency accounts using artificial intelligence) can be performed. For example, the processor 110 executes one or more instructions stored in the memory 120 to collect cryptocurrency transaction information generated between a plurality of cryptocurrency accounts, and a plurality of cryptocurrency using the cryptocurrency transaction information. A method for clustering cryptocurrency accounts using artificial intelligence, including generating a clustering rule for clustering accounts, and clustering a plurality of cryptocurrency accounts using the generated clustering rule may be performed. However, the present invention is not limited thereto.

In one embodiment, memory 120 may store various data, commands, and/or information. The memory 120 may store programs (one or more instructions) for processing and controlling the processor 110 . Programs stored in the memory 120 may be divided into a plurality of modules according to functions.

In various embodiments, steps of a method or algorithm described in connection with an embodiment of the present invention may be implemented directly in hardware, as a software module executed by hardware, or by a combination thereof. A software module may contain random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any type of computer-readable recording medium well known in the art to which the present invention pertains.

The components of the present invention may be implemented as a program (or application) to be executed in combination with a computer, which is hardware, and stored in a medium. The components of the present invention may be implemented as software programming or software components, and similarly, embodiments may include various algorithms implemented as data structures, processes, routines, or combinations of other programming constructs, including C, C++ , Java, assembler, etc. may be implemented in a programming or scripting language. Functional aspects may be implemented in an algorithm running on one or more processors. Hereinafter, a method of clustering a cryptocurrency account using artificial intelligence performed by the computing device 100 will be described with reference to FIG. 3 .

3 is a flowchart of a method for clustering a cryptocurrency account using artificial intelligence according to another embodiment of the present invention.

Referring to FIG. 3 , in step S110 , the computing device 100 may collect cryptocurrency transaction information generated between a plurality of cryptocurrency accounts. For example, the computing device 100 generates cryptocurrency transaction information (eg, a cryptocurrency transaction : Transactions including cryptocurrency withdrawal history and deposit history) can be collected. However, the present invention is not limited thereto.

In various embodiments, the computing device 100 provides a cryptocurrency between two or more cryptocurrency accounts based on information on two or more cryptocurrency accounts with the same owner among a plurality of cryptocurrency accounts and cryptocurrency transaction information between two or more cryptocurrency accounts. You can get a trading pattern.

In operation S120 , the computing device 100 may generate a clustering rule for clustering a plurality of cryptocurrency accounts by using the cryptocurrency transaction information collected in operation S110 .

In various embodiments, the computing device 100 may generate a first clustering rule (Common-input-ownership heuristic rule) for clustering a cryptocurrency account according to a cryptocurrency deposit history based on cryptocurrency transaction information. For example, the computing device 100 may generate a first clustering rule capable of clustering a plurality of cryptocurrency accounts for each owner according to a cryptocurrency deposit history using an artificial intelligence model.

In various embodiments, the computing device 100 may generate a second clustering rule (shadow heuristic, peel chain rule) for clustering cryptocurrency accounts according to whether each of a plurality of cryptocurrency account addresses is reused. For example, the computing device 100 may use an artificial intelligence model to cluster a plurality of cryptocurrency accounts into a payment account or a change account according to whether the cryptocurrency address is reused or not according to the second clustering rule. can create

In various embodiments, the computing device 100 clusters the cryptocurrency accounts according to whether the total amount of cryptocurrency withdrawn from each of the plurality of cryptocurrency accounts is a Round-Number third clustering rule (Round numbers rule) can create For example, the computing device 100 may cluster a plurality of cryptocurrency accounts into a payment account or a balance account according to whether the total amount of cryptocurrency withdrawn using an artificial intelligence model is a round-number. 3 You can create a clustering rule.

In various embodiments, the computing device 100 may generate a fourth clustering rule (unnecessary input heuristic rule (optimal change heuristic rule)) for clustering cryptocurrency accounts according to the size of the cryptocurrency to be deposited and withdrawn. For example, the computing device 100 may generate a fourth clustering rule capable of clustering a plurality of cryptocurrency accounts into a payment account or a balance account according to the size of the cryptocurrency to be deposited and withdrawn using the artificial intelligence model.

In various embodiments, the computing device 100 may generate a fifth clustering rule (Sending to a different script type rule) for clustering cryptocurrency accounts according to a script method of a transaction indicating cryptocurrency withdrawal. For example, the computing device 100 may generate a fifth clustering rule capable of clustering a plurality of cryptocurrency accounts into balance accounts according to a script method of a transaction indicating cryptocurrency withdrawal using an artificial intelligence model.

In various embodiments, the computing device 100 may generate a sixth clustering rule (Wallet bugs rule) for clustering cryptocurrency accounts based on the order of the cryptocurrency transaction history included in the transaction. For example, the computing device 100 may generate a sixth clustering rule capable of clustering a plurality of cryptocurrency accounts into a balance account based on the order of the cryptocurrency transaction history included in the transaction using the artificial intelligence model. have.

In various embodiments, the computing device 100 may generate a seventh clustering rule (Equal-output CoinJoin rule) for clustering the cryptocurrency account according to the value of the cryptocurrency to be withdrawn. For example, the computing device 100 may generate a seventh clustering rule for clustering a plurality of cryptocurrency accounts into balance accounts according to the value of the cryptocurrency to be withdrawn using the artificial intelligence model.

In various embodiments, the computing device 100 analyzes cryptocurrency transaction information to generate an eighth clustering rule (Wallet fingerprinting rule) for clustering cryptocurrency accounts based on the type of software that processed the cryptocurrency transaction. . For example, the computing device 100 uses an artificial intelligence model to infer which software is used for a specific transaction, and according to the inference result, an eighth clustering rule capable of clustering a plurality of cryptocurrency accounts into balance accounts can create

Here, the computing device 100 is described as generating the first clustering rule to the eighth clustering rule using the same single artificial intelligence model, but the present invention is not limited thereto. can create

In step S130 , the computing device 100 may cluster a plurality of cryptocurrency accounts using the clustering rule generated in step S120 . Hereinafter, a method for the computing device 100 to cluster a plurality of cryptocurrency accounts using different clustering rules will be described with reference to FIGS. 4 to 10 .

4 is a flowchart of a method of clustering cryptocurrency accounts using a first clustering rule, according to various embodiments.

Referring to FIG. 4 , in step S210 , the computing device 100 may generate a first clustering rule for clustering a cryptocurrency account according to a cryptocurrency deposit history based on cryptocurrency transaction information using an artificial intelligence model. (eg, step S120 of FIG. 3 ).

In step S220, the computing device 100 analyzes each of the plurality of cryptocurrency transaction information using the first clustering rule, and determines whether there is a plurality of cryptocurrency deposit history in each cryptocurrency transaction information, that is, each transaction. can judge

In step S230 , the computing device 100 may cluster a plurality of cryptocurrency accounts for each owner based on whether there are a plurality of cryptocurrency deposit histories in one transaction determined in step S220 . For example, the computing device 100 deposits a plurality of cryptocurrency deposit histories in one transaction (eg, deposits to the first cryptocurrency account, deposits to the second cryptocurrency account, and deposits to the third cryptocurrency account) history) exists, a cryptocurrency owned by the same owner of a cryptocurrency account (eg, a first cryptocurrency account, a second cryptocurrency account, and a third cryptocurrency account) corresponding to a plurality of cryptocurrency deposit histories, respectively. It can be clustered by determining that it is an account. However, the present invention is not limited thereto.

5 is a flowchart of a method of clustering cryptocurrency accounts using a second clustering rule, according to various embodiments.

Referring to FIG. 5 , in step S310 , the computing device 100 determines whether to reuse each of a plurality of cryptocurrency account addresses using an artificial intelligence model, and a second clustering for clustering cryptocurrency accounts according to whether or not to reuse. A rule may be created (eg, step S120 of FIG. 3 ).

In step S320 , the computing device 100 may determine whether addresses are reused for each address of a plurality of cryptocurrency accounts using the second clustering rule. For example, the computing device 100 may determine that the same cryptocurrency address is used twice or more for each address of a plurality of cryptocurrency accounts as being reused.

In general, in the case of a balance account among cryptocurrency accounts, the same address cannot be reused repeatedly because it is automatically created by the cryptocurrency wallet software and continuously updated with a new address. However, in the case of a payment account, the same address can be reused repeatedly because it is transmitted manually between people who want to trade cryptocurrency.

Based on this content, in step S330, the computing device 100 may cluster the cryptocurrency account determined that the address is reused in step S320 as a payment account, and in step S340, the computing device 100 in step S320 Cryptocurrency accounts whose addresses are judged not to be reused can be clustered as balance accounts. However, the present invention is not limited thereto.

6 is a flowchart of a method of clustering cryptocurrency accounts using the third clustering rule, in various embodiments.

Referring to FIG. 6 , in step S410 , the computing device 100 uses an artificial intelligence model to cluster the cryptocurrency accounts according to whether the total amount of cryptocurrency withdrawn from each of the plurality of cryptocurrency accounts is a round number. Third clustering A rule may be created (eg, step S120 of FIG. 3 ).

In operation S420 , the computing device 100 may determine whether the total amount of cryptocurrency withdrawn from each of the plurality of cryptocurrency accounts is a round number using the third clustering rule generated in operation S410 . For example, the computing device 100 determines whether the total amount of cryptocurrency withdrawn from each of the plurality of cryptocurrency accounts is a number (eg, the last digit of the total amount or a number obtained by rounding up, rounding off, and rounding up two or more digits) can be judged However, the present invention is not limited thereto, and the computing device 100 may convert the total amount of cryptocurrency into another currency (eg, KRW, dollar, yen, etc.), and determine whether the total amount of the converted other currency is a round number.

In general, the total amount of cryptocurrencies deposited into the payment account is likely to be a round number because it is bound to be a falling number (e.g. 0.1 BTC), and the remaining cryptocurrencies deposited into the balance account are non-round numbers. -round number).

Based on these contents, in step S430, when it is determined that the total amount of cryptocurrency withdrawn through step S420 is a round number, the cryptocurrency account in which the round number of cryptocurrency is deposited can be clustered as a payment account. have.

Also, in step S440 , the computing device 100 may cluster the cryptocurrency account in which the cryptocurrency is deposited, the total amount of which is not a round number, as the balance account in operation S420 . However, the present invention is not limited thereto.

7 is a flowchart of a method of clustering cryptocurrency accounts using the fourth clustering rule, in various embodiments.

Referring to FIG. 7 , in step S510 , the computing device 100 may generate a fourth clustering rule for clustering cryptocurrency accounts according to the size of the cryptocurrency to be deposited and withdrawn using the artificial intelligence model (eg, FIG. 3 ). step S120).

In step S520, the computing device 100 may analyze the size of the cryptocurrency to be deposited and withdrawn using the fourth clustering rule, and in step S530, the computing device 100 may analyze a plurality of cryptograms based on the result of the analysis in step S520. The monetary accounts can be clustered into either a payment account or a balance account. For example, when a transaction occurs with "2 + 3 → 4 + 1", if the cryptocurrency (eg Bitcoin) deposited in the payment account is 1 BTC, you can only use a cryptocurrency account with 2 BTC, Analyzing the transaction, it can be seen that a cryptocurrency account with 2 BTC and a cryptocurrency account with 3 BTC were used. That is, the computing device 100 may know that the cryptocurrency deposited into the payment account is 4 BTC and the cryptocurrency deposited into the balance account is 1 BTC. Through this, the computing device 100 may cluster a cryptocurrency account in which 4 BTC is deposited as a payment account, and cluster a cryptocurrency account in which 1 BTC is deposited as a balance account. However, the present invention is not limited thereto.

8 is a flowchart of a method of clustering cryptocurrency accounts using the fifth clustering rule, in various embodiments.

Referring to FIG. 8 , in step S610 , the computing device 100 may generate a fifth clustering rule for clustering cryptocurrency accounts according to a script method of a transaction indicating cryptocurrency withdrawal using an artificial intelligence model (eg, : step S120 of FIG. 3).

In step S620 , the computing device 100 may determine whether script methods of a transaction indicating cryptocurrency withdrawal are different from each other using the fifth clustering rule.

In step S630 , if the computing device 100 determines that the script method between the transactions indicating the cryptocurrency withdrawal is different through the step S620 , the same cryptocurrency as the script method of the transaction indicating the cryptocurrency deposit among the transactions indicating the cryptocurrency withdrawal You can select a transaction that points to a withdrawal.

In step S640, the computing device 100 may cluster the cryptocurrency account, which is the target of the withdrawal of the transaction indicating the withdrawal of the cryptocurrency selected in step S630, as the balance account. However, the present invention is not limited thereto.

9 is a flowchart of a method of clustering cryptocurrency accounts using the sixth clustering rule, according to various embodiments.

Referring to FIG. 9 , in step S710 , the computing device 100 may generate a sixth clustering rule for clustering cryptocurrency accounts based on the order of the cryptocurrency transaction history included in the transaction using the artificial intelligence model. (eg, step S120 of FIG. 3 ).

In step S720 , the computing device 100 may analyze the order of the cryptocurrency transaction history included in the transaction by using the sixth clustering rule.

In general, the transaction history deposited into the balance account is likely to be created at the very end of the transaction.

Based on these contents, in step S730, the computing device 100 may cluster the cryptocurrency account corresponding to the last cryptocurrency transaction history as the balance account through the analysis result through the step S720. However, the present invention is not limited thereto.

10 is a flowchart of a method of clustering cryptocurrency accounts using the seventh clustering rule, according to various embodiments.

Referring to FIG. 10 , in step S810 , the computing device 100 may generate a seventh clustering rule for clustering the cryptocurrency account according to the value of the cryptocurrency to be withdrawn using the artificial intelligence model (eg, FIG. 3 ). step S120).

In operation S820 , the computing device 100 may select a cryptocurrency withdrawal history having a different value of the cryptocurrency from among the plurality of cryptocurrency withdrawal histories using the seventh clustering rule generated in operation S810 .

In step S830, the computing device 100 may cluster the cryptocurrency withdrawal history selected in operation S820 (eg, cryptocurrency withdrawal history in which the value of the withdrawn cryptocurrency is different) into the balance account. However, the present invention is not limited thereto.

In various embodiments, the computing device 100 obtains information on two or more cryptocurrency accounts with the same owner among a plurality of cryptocurrency accounts, and uses two or more cryptocurrencies based on cryptocurrency transaction information between two or more cryptocurrency accounts with the same owner. Cryptocurrency trading patterns between currency accounts can be obtained. For example, the computing device 100 visualizes the cryptocurrency transaction information, analyzes the visualized cryptocurrency transaction information to determine the use of each of a plurality of cryptocurrency accounts, or uses a method for determining the use of a cryptocurrency account. Based on the established clustering rule, information on two or more cryptocurrency accounts with the same owner can be obtained by using the result data of clustering a plurality of cryptocurrency accounts for each owner.

In various embodiments, the computing device 100 may use machine learning to learn information about two or more cryptocurrency accounts and a cryptocurrency transaction pattern in an artificial intelligence model. Here, the artificial intelligence model may be a model for determining whether a transaction is between cryptocurrency accounts owned by the same owner by analyzing the cryptocurrency transaction information, but is not limited thereto.

For example, the computing device 100 may machine-lear the artificial intelligence model by using transaction information between cryptocurrency accounts owned by the same owner as training data, and through this, the artificial intelligence model may be converted into a cryptocurrency owned by the same owner. It is possible to detect transaction information between accounts, or extract characteristics of transactions between cryptocurrency accounts owned by the same owner. In addition, the computing device 100 may cluster transactions between cryptocurrency accounts owned by the same owner among various different types of transaction information, and according to an embodiment, among the transaction types clustered by the computing device 100 , the user Based on the selection or input of information, or the account type classification method described below, information on the transaction type determined to be a transaction between the same owners can be obtained, and the clustering rule can be obtained by extracting the characteristics.

In various embodiments, the computing device 100 may extract the same owner clustering rule for clustering the same cryptocurrency account among the cryptocurrency accounts by using the cryptocurrency transaction information, and information about two or more cryptocurrency accounts and The extracted same-owner clustering rule can be verified using the cryptocurrency transaction pattern, and the same-owner clustering rule can be created based on the verification result.

In various embodiments, the computing device 100 may perform clustering for each of the plurality of cryptocurrency accounts by using only the verified clustering rule among the plurality of clustering rules. However, the present invention is not limited thereto. Hereinafter, a method of determining the use of a cryptocurrency account performed by the computing device 100 will be described with reference to FIGS. 11 to 16 .

11 is a flowchart of a method of determining the usage of a cryptocurrency account performed by the cryptocurrency account clustering apparatus in various embodiments.

Referring to FIG. 11 , in step S1100 , the computing device 100 may collect cryptocurrency transaction information generated between a plurality of cryptocurrency accounts. For example, the computing device 100 generates cryptocurrency transaction information (eg, transaction) generated between a plurality of cryptocurrency accounts from the external server 300 (eg, a server that mediates cryptocurrency transactions between a plurality of cryptocurrency accounts). can be provided. However, the present invention is not limited thereto (eg, step S110 of FIG. 3 ).

In step S1200 , the computing device 100 may visualize cryptocurrency transaction information generated between the plurality of cryptocurrencies collected in step S1100 .

In various embodiments, the computing device 100 may visualize cryptocurrency transaction information generated between a plurality of cryptocurrencies in a graph form. However, the present invention is not limited thereto, and the computing device 100 may visualize cryptocurrency transaction information in various forms. Hereinafter, a configuration in which the computing device 100 visualizes cryptocurrency transaction information will be described with reference to FIG. 12 .

12 is a diagram illustrating cryptocurrency transaction information visualized according to a method of determining the use of a cryptocurrency account, in various embodiments.

Referring to FIG. 12 , in one embodiment, the computing device 100 may generate a plurality of nodes 10 corresponding to each of a plurality of cryptocurrency accounts. For example, the computing device 100 may generate one node corresponding to one cryptocurrency account.

In various embodiments, the computing device 100 may generate a node in the form of a square having a predetermined size, but is not limited thereto, and various forms (eg: round, oval, or rectangular).

In various embodiments, the computing device 100 generates a node corresponding to each of the plurality of cryptocurrency accounts, but based on the attribute (eg, use) of the cryptocurrency account determined through step S1400 to be described later. A property of a node corresponding to each can be determined. For example, the computing device 100 selects a node 11 corresponding to a cryptocurrency account (eg, a black market account, a fraudulent account, and a gambling account) used for crime and fraud based on the use of each of the plurality of cryptocurrency accounts. A first color (eg, red) may be set, and a node 12 corresponding to a cryptocurrency account associated with a criminal and fraudulent use account may be set as a second color (eg, yellow). Also, the computing device 100 may set the node 13 corresponding to the normally used cryptocurrency account (eg, an exchange account, a mining account, and a commerce account) to a third color (eg, white). Through this, a user who receives and monitors visualized cryptocurrency transaction information can more easily track and monitor cryptocurrency accounts used for crime and fraud. However, the present invention is not limited thereto.

In an embodiment, the computing device 100 may connect the sender's cryptocurrency account and the receiver's cryptocurrency account among nodes corresponding to a plurality of cryptocurrency accounts with an edge based on the cryptocurrency transaction information. For example, when it is determined that the remittance is made from the first cryptocurrency account to the second cryptocurrency account among the plurality of cryptocurrency accounts based on the cryptocurrency transaction information, the computing device 100 corresponds to the first cryptocurrency account. The node 11 and the node 12 corresponding to the second cryptocurrency account may be connected with a line.

In various embodiments, the computing device 100 connects a line (eg, a line in the form of an arrow) from the node of the sender's cryptocurrency account to the node of the receiver's cryptocurrency account, and the cryptocurrency is positioned adjacent to the connected line. Transaction quantity, number of transactions, and transaction time information according to transaction information can be output together. However, the present invention is not limited thereto.

According to an embodiment, the computing device 100 connects the node of the sender's cryptocurrency account and the node of the receiver's cryptocurrency account according to the transaction quantity, number of transactions, and transaction time information according to the cryptocurrency transaction information. can change the form of For example, the thickness of the line may change depending on the amount of cryptocurrency transaction (or transaction amount), and the shape of the line (eg, broken line, double line, etc.) may change depending on the number of cryptocurrency transactions, and the cryptocurrency transaction time or The length of the line may be changed according to the transaction frequency, but is not limited thereto.

The shape of the line connecting the nodes may be variously set so that at least some of the transaction quantity, number of transactions, and transaction time information according to the cryptocurrency transaction information can be visualized and read according to a preset rule.

The computing device 100 may extract various feature values based on the connection relationship between nodes, the characteristics of each node, and the shape of a line connecting each node using the pre-learned artificial intelligence model, and accordingly, The attributes of the corresponding accounts may be determined.

In an embodiment, the computing device 100 may determine the attribute of a transaction corresponding to each line by using a pre-learned artificial intelligence model. For example, when a node-to-node remittance is made, it may be a remittance for a cryptocurrency transaction for investment purposes, a remittance to pay for a product or service, asset management, tax evasion, or illegal transfer of funds. It may be a remittance for

In order to determine this, the computing device 100 may utilize not only information on the quantity, number, frequency, and time of each transaction, but also cryptocurrency price information at the time of the transaction. Unlike traditional currencies, cryptocurrency has a characteristic that its value can change significantly from moment to moment. Therefore, by utilizing the market price information at the time of the transaction of the cryptocurrency, the value of the amount actually transferred can be determined, and accordingly, the attribute of the transaction can be determined.

For example, if there are multiple remittances between different nodes and the value of the remittance calculated by reflecting the market price of cryptocurrency is constant, it can be determined that the price has been paid for a product or service with a specific value. . On the other hand, if a certain amount of cryptocurrency is being remitted despite fluctuations in the price of cryptocurrency, it can be determined that the remittance has occurred regardless of the actual value and has other purposes. However, the purpose of remittance may be evaluated differently depending on specific situation information, and the criteria for determination are not limited to the above example.

In addition, if a large number of transactions occur in a situation in which the cryptocurrency market price changes rapidly, it may be judged to be a transaction for the purpose of investment. Transactions between accounts, asset management, tax evasion, etc.)

According to an embodiment, as described above, a line connecting each node may be visualized according to a preset rule, and the computing device 100 uses a pre-learned artificial intelligence model to display a transaction attribute of each line. can be judged For example, the computing device 100 may extract various features from the visualized shape of each line, and estimate properties of a transaction corresponding thereto.

In the disclosed embodiment, the artificial intelligence model may be learned based on a deep learning model and training data including information on a visualized image and an answer (eg, account attribute, transaction attribute, etc.) corresponding to each image. However, the present invention is not limited thereto.

Referring back to Figure 11, in step S1300, the computing device 100 may analyze the cryptocurrency transaction information visualized in step S1200, and in step S1400, the computing device 100 is visualized through step S1300. Based on the analysis result of the cryptocurrency transaction information, it is possible to determine the use for each of the plurality of cryptocurrency accounts.

In various embodiments, the computing device 100 automatically extracts a feature value from the cryptocurrency transaction information visualized in a graph form in step S1200 using a pre-trained artificial intelligence model (eg, a deep learning model). and the use of each of the plurality of cryptocurrency accounts can be determined using the extracted feature value. For example, the feature value may be in the overall form of a graph, and the computing device 100 determines which cryptocurrency account is located on the graph according to the overall shape of the cryptocurrency transaction information visualized in the graph form. It can be determined whether the account is performing a role. However, the present invention is not limited thereto.

In one embodiment, the pre-trained artificial intelligence model is based on the training data in which the properties of the cryptocurrency account corresponding to one or more nodes included in the visualized cryptocurrency transaction information and the visualized cryptocurrency transaction information in the form of a graph are labeled. can be learned As another example, the pre-trained artificial intelligence model clusters the attributes of accounts corresponding to one or more nodes from the cryptocurrency transaction information visualized in the form of a graph, and receives or automatically determines the attribute information of each clustered account. It is also possible to perform classification on the properties of .

In various embodiments, the computing device 100 uses a graph-based semi-map learning model based on the visualized state of the cryptocurrency transaction information (eg, the number of nodes, the number of lines connecting the nodes, etc.) The use of a cryptocurrency account can be determined by using any one of the determination method, the method of determining the use of a cryptocurrency account using an account address-based supervised learning model, and the method of determining the use of a cryptocurrency account using an owner-based semi-supervised learning model. have.

In various embodiments, the computing device 100 uses any one of a graph-based semi-supervised learning model, an account address-based supervised learning model, and an owner-based semi-supervised learning model based on the type of input data to generate a cryptocurrency account use can be determined. Hereinafter, it will be described with reference to FIGS. 13 to 16 .

13 is a diagram illustrating a configuration for determining the use of a cryptocurrency account using a graph-based semi-map learning model in various embodiments.

Referring to FIG. 13 , in various embodiments, the computing device 100 determines the use of a cryptocurrency account by using a graph-based semi-map learning model when the number of interconnected nodes in the visualized cryptocurrency transaction information is not large. can

The graph-based semi-supervised learning model is a model that can process tens of billions of cryptocurrency transaction information stored in graph format into meaningful information. : Deep learning analysis) may be a model that can determine the use of a cryptocurrency account. For example, the graph-based semi-supervised learning model in-depth analysis (e.g., clustering, classification) of the node corresponding to the cryptocurrency account and the characteristics of the line representing the cryptocurrency transaction information. It is possible to derive the necessary information in determining the use. In addition, the graph-based semi-supervised learning model may be a model to which semi-supervised learning is applied so that it can have higher accuracy using a small amount of correct answer data. However, the present invention is not limited thereto.

In various embodiments, the computing device 100 uses a graph-based semi-map learning model when it is determined that the number of a plurality of nodes included in the visualized cryptocurrency transaction information exceeds a preset first reference value (eg : Using the visualized cryptocurrency transaction information as an input value), it is possible to determine the form in which a plurality of nodes are connected by a line (eg, the overall shape of the graph), and a cryptocurrency account based on the form in which a plurality of nodes are connected by a line use can be determined. For example, when the overall shape of the graph has the graph pattern of the exchange, the computing device 100 may determine the use of the corresponding cryptocurrency account as the exchange account. However, the present invention is not limited thereto.

14 is a diagram illustrating a configuration for determining the use of a cryptocurrency account using an account address-based supervised learning model, in various embodiments.

Referring to FIG. 14 , in the visualized cryptocurrency transaction information, when the number of interconnected nodes is not large, the computing device 100 may determine the use of the cryptocurrency account by using the account address-based supervised learning model.

The account address-based supervised learning model may be a model for identifying the use of each cryptocurrency account by analyzing a transaction pattern based on individual cryptocurrency accounts. The account address-based supervised learning model may be a model in which a cryptocurrency transaction pattern is learned using only a cryptocurrency account whose use is determined in advance through the above method and a cryptocurrency account with 100% certainty through manual operation.

In various embodiments, when the number of a plurality of nodes connected by lines is less than or equal to a first reference value, and the number of lines for each of the plurality of nodes exceeds a second reference value, the computing device 100 performs account address-based supervised learning Using the model, a transaction pattern for each cryptocurrency account can be analyzed according to the transaction history for each cryptocurrency account, and the use of each of the plurality of cryptocurrency accounts can be determined based on the analyzed transaction pattern. For example, the computing device 100 may determine the use of the cryptocurrency account as an ICO account when the transaction pattern for the cryptocurrency account shows a wave shape, and determines the form in which the transaction pattern for the cryptocurrency account occurs frequently. If it is visible, the use of the cryptocurrency account can be determined as an exchange hot wallet account, and when the trading pattern for the cryptocurrency account is in the form of a staircase, the use of the cryptocurrency account can be determined as a personal account and an exchange cold wallet account, If the deposit and withdrawal amounts show the same form in the transaction pattern for the cryptocurrency account, the use of the cryptocurrency account can be determined as the exchange user wallet account. However, the present invention is not limited thereto.

15 is a diagram illustrating a configuration for determining the use of a cryptocurrency account using an owner-based semi-map learning model, in various embodiments.

Referring to FIG. 15 , in various embodiments, when the number of transaction details between cryptocurrency accounts for determining a transaction pattern is a small number (eg, one), the computing device 100 uses an owner-based semi-map learning model to encrypt You can decide the purpose of the monetary account.

The owner-based semi-supervised learning model is a technology that processes reclassified cryptocurrency transaction information into meaningful information based on the owner of the cryptocurrency account. It may be a model that learned the owner's transaction pattern identified through Entity Heuristic Clustering. The owner-based semi-supervised learning model can be a model that has learned various data through semi-supervised learning with high accuracy with only a small amount of correct data, and can determine the use of a cryptocurrency account by performing semi-supervised deep learning analysis.

In various embodiments, when the number of the plurality of nodes connected by lines is less than or equal to the first reference value and the number of lines for each of the plurality of nodes is less than or equal to the second reference value, the computing device 100 generates an owner-based semi-supervised learning model. Grouping at least one cryptocurrency account among a plurality of cryptocurrency accounts based on the owner of the cryptocurrency account using A pattern for the currency account may be analyzed, and a use for each of the grouped at least one cryptocurrency account may be determined based on the grouped pattern (eg, FIG. 8 ) for the at least one cryptocurrency account. However, the present invention is not limited thereto.

In various embodiments, the computing device 100 may collect image form of cryptocurrency account information and one or more keywords from an external website, and use the collected information to determine the use of the cryptocurrency account.

16 is a diagram illustrating a configuration for determining the use of a cryptocurrency account by using image-type cryptocurrency account information and keywords obtained from a website, in various embodiments.

Referring to FIG. 16 , the computing device 100 may collect image form of cryptocurrency account information and one or more keywords from an external website, and analyze the image form of cryptocurrency account information to obtain a text form of cryptocurrency account. Information may be extracted, and one or more keywords may be analyzed to determine the use of a cryptocurrency account corresponding to the extracted text-type cryptocurrency account information.

In general, transactions using cryptocurrency value anonymity, so the address of the cryptocurrency account is often stored in the form of an image. In this case, the computing device 100 may search for the cryptocurrency account information in the form of an image, and extract the cryptocurrency account in the form of a text from the found cryptocurrency account information in the form of an image (eg, by using OCR character reading technology). .

In addition, the computing device 100 may extract one or more main keywords from a website on which the cryptocurrency account address is posted, and may determine a use for the cryptocurrency account extracted in text form according to the extracted keywords. For example, when the keywords extracted from the website on which the cryptocurrency account address is posted are Drugs and Weeds, the computing device 100 may determine the use of the cryptocurrency account extracted in text form as a black market account.

In various embodiments, the computing device 100 may collect information related to cryptocurrency from an illegal dark web site, and use it to determine the use of a cryptocurrency account.

In general, a dark web site refers to a web that requires the use of a specific program to access, and is used for cyber crimes because the visitor or server cannot be identified in a normal way. In particular, various illegal crimes using cryptocurrency as a payment method are prevalent in the dark web. In order to monitor the cryptocurrency account that conducts such illegal transactions, the computing device 100 uses pre-stored Darkweb access software (using Tor browser, software that accesses domains ending in *.onion). Access the dark web site, automatically collect cryptocurrency-related information from the dark web site at preset intervals, and determine the use of each of a plurality of cryptocurrency accounts based on the cryptocurrency-related information collected from the dark web site can For example, the computing device 100 may set a cryptocurrency account corresponding to a cryptocurrency account address obtained from a dark web site among a plurality of cryptocurrency accounts as a dark web account. However, the present invention is not limited thereto.

The above-described method for clustering cryptocurrency accounts using artificial intelligence has been described with reference to the flowchart shown in the drawings. For a simple explanation, the method of clustering a cryptocurrency account using artificial intelligence has been described by showing a series of blocks, but the present invention is not limited to the order of the blocks, and some blocks are in an order different from that shown and operated in this specification may be performed or may be performed simultaneously. In addition, new blocks not described in this specification and drawings may be added, or some blocks may be deleted or changed.

As mentioned above, although embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art to which the present invention pertains can realize that the present invention can be embodied in other specific forms without changing its technical spirit or essential features. you will be able to understand Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

100: Cryptocurrency account clustering device (computing device)
200: user terminal
300 : external server
400: network

Claims (13)

A method performed by a computing device, comprising:
collecting cryptocurrency transaction information generated between a plurality of cryptocurrency accounts;
generating a clustering rule for clustering the plurality of cryptocurrency accounts by using the cryptocurrency transaction information; and
clustering the plurality of cryptocurrency accounts using the generated clustering rule;
A method of clustering cryptocurrency accounts using artificial intelligence. According to claim 1,
The step of generating the clustering rule comprises:
and generating a first clustering rule for clustering cryptocurrency accounts according to cryptocurrency deposit history based on the cryptocurrency transaction information,
The step of clustering the plurality of cryptocurrency accounts comprises:
When there are a plurality of cryptocurrency deposit histories in one transaction using the first clustering rule, a plurality of cryptocurrency accounts corresponding to the plurality of cryptocurrency deposit histories are transferred to the cryptocurrency account of the same owner. comprising the step of clustering,
A method of clustering cryptocurrency accounts using artificial intelligence. According to claim 1,
The step of generating the clustering rule comprises:
Determining whether to reuse each of a plurality of cryptocurrency account addresses based on the cryptocurrency transaction information, and generating a second clustering rule for clustering cryptocurrency accounts according to the reuse or not;
The step of clustering the plurality of cryptocurrency accounts comprises:
Using the second clustering rule, clustering a cryptocurrency account whose address is reused among the plurality of cryptocurrency accounts as a payment account, and clustering a cryptocurrency account whose address is not reused as a change account. comprising steps,
A method of clustering cryptocurrency accounts using artificial intelligence. According to claim 1,
The step of generating the clustering rule comprises:
Creating a third clustering rule for clustering cryptocurrency accounts according to whether the total amount of cryptocurrency withdrawn from each of a plurality of cryptocurrency accounts is a round number based on the cryptocurrency transaction information,
The step of clustering the plurality of cryptocurrency accounts comprises:
using the third clustering rule, when the total amount of the withdrawn cryptocurrency is a round number, clustering the cryptocurrency account in which the round number of cryptocurrency is deposited as a payment account; and
Using the third clustering rule, if the total amount of cryptocurrency withdrawn is not a round number, clustering the cryptocurrency account in which the non-rounded number is deposited into a balance (Change) account,
A method of clustering cryptocurrency accounts using artificial intelligence. According to claim 1,
The step of generating the clustering rule comprises:
and generating a fourth clustering rule for clustering cryptocurrency accounts according to the size of the cryptocurrency to be deposited and withdrawn based on the cryptocurrency transaction information,
The step of clustering the plurality of cryptocurrency accounts comprises:
Using the fourth clustering rule, clustering the plurality of cryptocurrency accounts into any one of a payment account and a balance account,
A method of clustering cryptocurrency accounts using artificial intelligence. According to claim 1,
The step of generating the clustering rule comprises:
Creating a fifth clustering rule for clustering cryptocurrency accounts according to a script method of a transaction indicating cryptocurrency withdrawal based on the cryptocurrency transaction information,
The step of clustering the plurality of cryptocurrency accounts comprises:
Using the fifth clustering rule, if the script methods of a plurality of transactions indicating cryptocurrency withdrawal are different from each other, a cryptocurrency withdrawal having the same script method as a transaction indicating cryptocurrency deposit is selected, and Including the step of clustering the target cryptocurrency account into a balance account,
A method of clustering cryptocurrency accounts using artificial intelligence. According to claim 1,
The step of generating the clustering rule comprises:
and generating a sixth clustering rule for clustering cryptocurrency accounts based on the order of the cryptocurrency transaction history included in the transaction based on the cryptocurrency transaction information,
The step of clustering the plurality of cryptocurrency accounts comprises:
Using the sixth clustering rule, clustering a cryptocurrency account corresponding to the last cryptocurrency transaction history among a plurality of cryptocurrency transaction histories included in the transaction as a balance account,
A method of clustering cryptocurrency accounts using artificial intelligence. According to claim 1,
The step of generating the clustering rule comprises:
and generating a seventh clustering rule for clustering cryptocurrency accounts according to the value of the cryptocurrency to be withdrawn based on the cryptocurrency transaction information,
The step of clustering the plurality of cryptocurrency accounts comprises:
Using the seventh clustering rule, clustering a cryptocurrency account corresponding to a cryptocurrency withdrawal history having a different value of the cryptocurrency from among a plurality of cryptocurrency withdrawal histories as a balance account,
A method of clustering cryptocurrency accounts using artificial intelligence. According to claim 1,
The step of collecting the cryptocurrency transaction information includes:
obtaining information on two or more cryptocurrency accounts of the same owner among the plurality of cryptocurrency accounts; and
Comprising the step of obtaining a cryptocurrency transaction pattern between the two or more cryptocurrency accounts based on the cryptocurrency transaction information between the two or more cryptocurrency accounts,
The step of generating the clustering rule comprises:
Using machine learning (Machine Learning) to learn the information on the two or more cryptocurrency accounts and the cryptocurrency transaction pattern in an artificial intelligence model,
The artificial intelligence model is a model that analyzes the cryptocurrency transaction information to determine whether it is a transaction between cryptocurrency accounts owned by the same owner,
A method of clustering cryptocurrency accounts using artificial intelligence. 10. The method of claim 9,
The step of generating the clustering rule comprises:
extracting a same-owner clustering rule for clustering crypto-currency accounts with the same owner among the crypto-currency accounts by using the crypto-currency transaction information; and
verifying the extracted same-owner clustering rule using the information on the two or more cryptocurrency accounts and the cryptocurrency transaction pattern, and generating the same-owner clustering rule based on the verification result,
A method of clustering cryptocurrency accounts using artificial intelligence. 10. The method of claim 9,
The step of obtaining information about two or more cryptocurrency accounts that the owner is the same,
Visualizing the cryptocurrency transaction information and analyzing the visualized cryptocurrency transaction information to determine the use of each of the plurality of cryptocurrency accounts using a method for determining the use of a cryptocurrency account or based on a previously generated clustering rule Comprising the step of obtaining information about two or more cryptocurrency accounts of the same owner by using the result data of clustering a plurality of cryptocurrency accounts by owner,
A method of clustering cryptocurrency accounts using artificial intelligence. a memory storing one or more instructions; and
a processor executing the one or more instructions stored in the memory;
The processor by executing the one or more instructions,
An apparatus for performing the method of claim 1 . A computer program stored in a computer-readable recording medium in combination with a computer, which is hardware, to perform the method of claim 1.

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