KR102112798B1 - Method, apparatus and computer program for clustering cryptocurrency accounts using artificial intelligence - Google Patents

Abstract

Provided are a method for clustering cryptocurrency accounts using artificial intelligence, a device thereof and a computer program thereof. According to various embodiments of the present invention, the method for clustering cryptocurrency accounts using artificial intelligence, in a method performed by a computing device, comprises the steps of: collecting cryptocurrency transaction information generated among a plurality of cryptocurrency accounts; generating a clustering rule 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.

Description

METHOD, APPARATUS AND COMPUTER PROGRAM FOR CLUSTERING CRYPTOCURRENCY ACCOUNTS USING ARTIFICIAL INTELLIGENCE}

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

The number of users began to increase exponentially as the cryptocurrency (such as Bitcoin), which was used among a small number of users, gradually gained the attention of the media and the general public, and as a result, the price and transaction volume of cryptocurrency surged. The bubble phenomenon has caused a huge impact on society.

Various industries using cryptocurrency systems are evolving, but the evaluation of the impact on this society and the prospects for future development are mixed, and the responses of each country are also showing great differences. Countries that negatively evaluate the impact of cryptocurrencies are trying to mitigate the social impact of introducing new technologies by taking measures such as illegally regulating the entire use of cryptocurrencies or restricting the use of cryptocurrencies by financial institutions.

Analyzing the price and transaction volume of cryptocurrency, it was found that the volume of transactions declined due to a sharp decline in prices in early 2018, but the volume of transactions in 2019 recovered rapidly, and through this, the global cryptocurrency market has matured one step after the bubble.

Also, considering that cryptocurrency is valued as an alternative investment asset in the financial market, and considering that Brazil, Argentina, and Venezuela actively use cryptocurrency as a substitute for statutory currency, the cryptocurrency trading market is expected to continue to grow in the future. do.

On the other hand, cryptocurrency has a nature of decentralization and anonymity, so it is highly likely to be exploited as a means of crime (e.g., ICO fraud, money laundering and black market settlement). In fact, it is estimated that the expected damage caused by the exploited cryptocurrency in 2019 was 2.5 trillion won, up 2.5 times from the previous year.

Accordingly, government agencies and cryptocurrency handling agencies in various countries related to cryptocurrency are not only trying to prevent and track cryptocurrency-related crimes in various ways, but also create new regulations to prevent cryptocurrency crimes such as money laundering. Trying hard.

However, there is a problem that the targets for compliance with regulations are very wide as governments and cryptocurrency handling companies in each country. In addition, although technology development must be made in accordance with the trend of strengthening regulations, there is a problem that there is a limit to crime prevention and tracking due to technical limitations. For example, in the prior art, the account was tracked using a manual data collection method, but due to the advanced money laundering technology, the amount of transactions to be read during tracking increases, making it impossible to view all the transactions. 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 in that it takes a great amount of money to track the accounts.

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

The problem to be solved by the present invention is that one person can own multiple cryptocurrencies accounts, and unlike bank transactions, 1-to-1, n-to-1, n, as well as 1-to-1 Clustering to cluster cryptocurrency accounts from specific management patterns of cryptocurrency wallet software, considering that it is possible to trade and to manage multiple cryptocurrency accounts using cryptocurrency wallet software due to the nature of cryptocurrency wallet It is to provide a clustering method, apparatus and computer program for a cryptocurrency account using artificial intelligence capable of generating rules and clustering a cryptocurrency account 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 of clustering a cryptocurrency account using artificial intelligence according to an embodiment of the present invention for solving the above-described problem 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, and the plurality of ciphers In the step of clustering a currency account, when there is a history of depositing a plurality of cryptocurrencies in one transaction using the first clustering rule, a plurality of cryptocurrency accounts respectively corresponding to the plurality of cryptocurrency deposit histories It may include the step of clustering a cryptocurrency account of the same owner.

In various embodiments, the generating of the clustering rule may include determining whether to reuse each of a plurality of cryptocurrency account addresses based on the cryptocurrency transaction information, and clustering a cryptocurrency account according to the reuse. A step of generating a clustering rule, and the step of clustering the plurality of cryptocurrency accounts, using the second clustering rule, paying a cryptocurrency account whose address is reused among the plurality of cryptocurrency accounts (Payment). The method may include clustering an account and clustering a cryptocurrency account whose address is not reused into a change account.

In various embodiments, the step of generating the clustering rule, based on the transaction information of the cryptocurrency, the cryptocurrency account depending on whether the total amount of cryptocurrency withdrawn from each of the plurality of cryptocurrency accounts is a round-number And generating a third clustering rule for clustering, wherein clustering the plurality of cryptocurrency accounts comprises: using the third clustering rule, when the total amount of the withdrawn cryptocurrency is a rounded number, Clustering a cryptocurrency account in which a cryptocurrency has been deposited into a payment account, and using the third clustering rule, when the total amount of the withdrawn cryptocurrency is not a round number, the password in which the cryptocurrency other than the round number is deposited And clustering the money account into a change account.

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

In various embodiments, the generating of the clustering rule may include generating a fifth clustering rule clustering a cryptocurrency account according to a script method of a transaction indicating withdrawal of cryptocurrency based on the cryptocurrency transaction information. Containing, wherein the step of clustering the plurality of cryptocurrency accounts, if the scripting method of the plurality of transactions indicating the withdrawal of cryptocurrency is different from each other using the fifth clustering rule, the same script as the transaction indicating the deposit of cryptocurrency And selecting a cryptocurrency withdrawal having a method and clustering the cryptocurrency account that is the target of the selected cryptocurrency withdrawal into a balance account.

In various embodiments, the generating of the clustering rule may include generating a sixth clustering rule clustering a cryptocurrency account 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, using the sixth clustering rule, the cryptocurrency account corresponding to the last cryptocurrency transaction history among the plurality of cryptocurrency transaction histories included in the transaction. And clustering the balance account.

In various embodiments, the generating of the clustering rule includes generating a seventh clustering rule clustering a cryptocurrency account according to the value of the cryptocurrency withdrawn based on the cryptocurrency transaction information, and the plurality of The step of clustering the cryptocurrency account of the clustering, using the seventh clustering rule, clustering the cryptocurrency account corresponding to the different cryptocurrency withdrawal history among the cryptocurrency withdrawal history among the plurality of cryptocurrency withdrawal history into the balance account It may include the steps.

In various embodiments, the step of collecting the cryptocurrency transaction information may include obtaining information about two or more cryptocurrency accounts whose owners are the same among the plurality of cryptocurrency accounts, and cryptocurrency transaction information between the two or more cryptocurrency accounts. Obtaining a cryptocurrency transaction pattern between the two or more cryptocurrency accounts on the basis of the steps, and generating the clustering rule comprises: using the machine learning (Machine Learning) to the artificial intelligence model for the two or more cryptocurrency accounts And learning information and the cryptocurrency transaction pattern, and the artificial intelligence model may be a model that analyzes the cryptocurrency transaction information to determine whether it is a transaction between cryptocurrency accounts owned by the same owner.

In various embodiments, the generating of the clustering rule may include extracting the same owner clustering rule in which the owner clusters the same cryptocurrency account among the cryptocurrency accounts using the cryptocurrency transaction information and the two or more cryptocurrencies. And verifying the extracted same-owner clustering rule using the information on the account and the cryptocurrency transaction pattern, and generating the same-owner clustering rule based on the verification result.

In various embodiments, the step of obtaining information about two or more cryptocurrency accounts of the same owner may visualize the cryptocurrency transaction information and analyze the visualized cryptocurrency transaction information to use each of the plurality of cryptocurrency accounts. Using the method of determining the purpose of the cryptocurrency account for determining or using the result of clustering the plurality of cryptocurrency accounts for each owner based on the previously generated clustering rules, the owner can obtain information about two or more cryptocurrency accounts that are the same. 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, wherein the The processor may perform a clustering method of a cryptocurrency account using artificial intelligence according to an embodiment of the present invention by executing the one or more instructions.

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

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

According to various embodiments of the present invention, a clustering rule for clustering a cryptocurrency account is generated, and accordingly, a cryptocurrency account is clustered to generate data for learning and analyzing cryptocurrency transaction history using an artificial intelligence model. Thus, there is an advantage that it is possible to significantly reduce the amount of computation required to learn and analyze data.

The 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 view showing a clustering system of a cryptocurrency account 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 of clustering a cryptocurrency account using artificial intelligence according to another embodiment of the present invention.
4 is a flowchart of a method of clustering a cryptocurrency account using a first clustering rule in various embodiments.
5 is a flowchart of a method of clustering a cryptocurrency account using a second clustering rule in various embodiments.
6 is a flowchart of a method of clustering a cryptocurrency account using a third clustering rule in various embodiments.
7 is a flowchart of a method of clustering a cryptocurrency account using a fourth clustering rule in various embodiments.
8 is a flowchart of a method of clustering a cryptocurrency account using a fifth clustering rule in various embodiments.
9 is a flowchart of a method of clustering a cryptocurrency account using a sixth clustering rule in various embodiments.
10 is a flowchart of a method of clustering a cryptocurrency account using a seventh clustering rule in various embodiments.
11 is a flowchart of a method for determining a use of a cryptocurrency account performed by a cryptocurrency account clustering device in various embodiments.
12 is a diagram illustrating cryptocurrency transaction information visualized according to a method for determining a usage 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 ring diagram learning model in various embodiments.
14 is a diagram illustrating a configuration of determining a 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 ring diagram learning model in various embodiments.
16 is a diagram illustrating a configuration for determining the use of a cryptocurrency account by using cryptocurrency account information and keywords in the form of an image obtained from a website in various embodiments.

Advantages and features of the present invention, and methods for achieving them will be clarified with reference to embodiments described below in detail together 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 the present embodiments allow the disclosure of the present invention to be complete, and are common in the technical field to which the present invention pertains. It is provided to fully inform the skilled person of the scope of the present invention, and the present invention is only defined by the scope of the claims.

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

Unless otherwise defined, all terms (including technical and scientific terms) used in the present specification may be used as meanings commonly understood by those skilled in the art to which the present invention pertains. In addition, terms defined in the commonly used dictionary are not ideally or excessively interpreted unless explicitly defined.

The term "part" or "module" as used in the specification refers to a hardware component such as software, FPGA, or ASIC, and "part" or "module" performs certain roles. However, "part" or "module" is not meant to be limited to software or hardware. The "unit" or "module" may be configured to be in an addressable storage medium or may be configured to reproduce one or more processors. Thus, as an example, "part" or "module" means components, processes, functions, attributes, such as software components, object-oriented software components, class components and task components. Includes procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, database, data structures, tables, arrays and variables. The functionality provided within components and "parts" or "modules" can be combined into a smaller number of components and "parts" or "modules" or into additional components and "parts" or "modules" Can be further separated.

The spatially relative terms “below”, “beneath”, “lower”, “above”, “upper”, etc., are as shown in the figure. It can be used to easily describe a correlation between a component and other components. Spatially relative terms should be understood as terms including different directions of components in use or operation in addition to the directions shown in the drawings. For example, if a component shown in the drawing is turned over, a component described as "below" or "beneath" of another component will be placed "above" the other component. Can be. Accordingly, the exemplary term “below” can include both the directions below and above. The component can also be oriented in other directions, so that spatially relative terms can be interpreted according to the orientation.

In the present specification, the computer means all kinds of hardware devices including at least one processor, and may be understood as a meaning encompassing software configurations operating in the corresponding hardware device according to embodiments. For example, a computer may be understood as meaning including, but not limited to, a smart phone, a tablet PC, a desktop, a laptop, and a user client and application 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 according to an embodiment, at least some of the steps may be performed in different devices.

1 is a view showing a clustering system of a cryptocurrency account using artificial intelligence according to an embodiment of the present invention.

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

Here, the clustering system of the cryptocurrency account using the artificial intelligence shown in FIG. 1 is in accordance with one embodiment, and its components are not limited to the embodiment shown in FIG. 1, and may be added, changed or deleted as necessary. Can be.

In one embodiment, the cryptocurrency account clustering device 100 collects cryptocurrency transaction information (for example, transactions) generated between a plurality of cryptocurrency accounts, and uses the collected cryptocurrency transaction information to encrypt a plurality of cryptocurrencies. Each currency account 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 rules 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 the cryptocurrency transaction information to perform a first clustering rule for clustering a cryptocurrency account according to the cryptocurrency input history, for each of a plurality of cryptocurrency account addresses A second clustering rule for determining whether to reuse and clustering a cryptocurrency account according to the reuse or not, depending on whether the total amount of cryptocurrency withdrawn from each of the 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 cryptocurrencies to be deposited and withdrawn, and the system for clustering cryptocurrency accounts according to the script method of the transaction indicating cryptocurrency withdrawal (output). 5 Clustering rules, based on the history of cryptocurrency transactions included in the transaction. Based on standing may generate a seventh clustering rule to cluster the password money account based on the value of the sixth clustering rules and cryptocurrency that withdrawal to cluster password currency account. However, it is not limited to this.

In one embodiment, the user terminal 200 may be connected to the cryptocurrency account clustering device 100 through the network 400, and receives various information related to the clustering result of the cryptocurrency account from the cryptocurrency account clustering device 100. Can be provided. For example, the user terminal 200 may include at least one of a smartphone, a tablet PC, a laptop, and a desktop having a display in at least a portion of the user terminal 200, and a cryptocurrency account clustering device through the display By outputting a user interface (UI) provided by the (100), it is possible to output various information provided from the cryptocurrency account clustering device 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 necessary for the cryptocurrency account clustering device 100 to cluster the cryptocurrency account ( Example: cryptocurrency transaction information between cryptocurrency accounts). For example, the external server 300 may be a cryptocurrency transaction intermediary server that provides a service for brokering cryptocurrency transactions, and transactions between cryptocurrency accounts through a cryptocurrency transaction brokerage service that brokers cryptocurrency transactions. The cryptocurrency transaction information generated by proceeding may be stored, and the stored cryptocurrency transaction information may be provided to the cryptocurrency account clustering device 100. However, it is not limited to this.

In various embodiments, the external server 300 may receive and store various data (eg, cryptocurrency account clustering result data) from the cryptocurrency account clustering device 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 have 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, it is not limited to this. 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, a 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 one embodiment, the processor 110 may control the overall operation of each component of the computing device 100. The processor 110 may include a CPU (Central Processing Unit), an MPU (Micro Processor Unit), an MCU (Micro Controller Unit), or any type of processor well known in the art.

In various embodiments, the processor 110 may perform operations on at least one application or program for executing the method according to 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 passage (for example, a bus) for transmitting and receiving signals to and from other components. can do.

In various embodiments, the processor 110 may temporarily and / or permanently store a signal (or data) processed inside the processor 110 in a random access memory (RAM) and a ROM (ROM). -Only Memory (not shown). 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, RAM, and ROM.

In one embodiment, the processor 110 executes one or more instructions stored in the memory 120 to explain the method to be described with reference to FIGS. 3 to 10 (eg, a method of clustering cryptocurrency accounts using artificial intelligence). You can do For example, the processor 110 collects cryptocurrency transaction information generated between a plurality of cryptocurrency accounts by executing one or more instructions stored in the memory 120, and uses the cryptocurrency transaction information to collect a plurality of cryptocurrency A method of clustering a cryptocurrency account using artificial intelligence may be performed, including generating a clustering rule for clustering accounts and clustering a plurality of cryptocurrency accounts using the generated clustering rule. However, it is not limited to this.

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

In various embodiments, the steps of a method or algorithm described in connection with an embodiment of the present invention may be implemented directly in hardware, a software module executed by hardware, or a combination thereof. The software modules may include 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 The present invention may reside in any form of computer readable recording medium well known in the art.

The components of the present invention may be implemented as a program (or application) to be executed in combination with a hardware computer, and stored in a medium. The components of the present invention can be implemented in software programming or software components, and similarly, embodiments include C, C ++, including various algorithms implemented in a combination of data structures, processes, routines or other programming components. , Can be implemented in programming or scripting languages such as Java, assembler, etc. Functional aspects can be implemented with algorithms 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 of 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 is cryptocurrency transaction information generated by trading cryptocurrency from an external server 300 (for example, a cryptocurrency transaction brokerage server that provides a service for brokering cryptocurrency transactions between users) : Transactions including cryptocurrency withdrawal history and deposit history). However, it is not limited to this.

In various embodiments, the computing device 100 may perform cryptocurrency between two or more cryptocurrency accounts based on information on two or more cryptocurrency accounts whose owners are the same among the plurality of cryptocurrency accounts and cryptocurrency transaction information between the two or more cryptocurrency accounts. You can get trading patterns.

In operation S120, the computing device 100 may generate a clustering rule for clustering a plurality of cryptocurrency accounts 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) that clusters a cryptocurrency account according to a cryptocurrency deposit history based on cryptocurrency transaction information. For example, the computing device 100 may use the artificial intelligence model to generate a first clustering rule that can cluster a plurality of cryptocurrency accounts for each owner according to the cryptocurrency deposit history.

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 to reuse each of a plurality of cryptocurrency account addresses. 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. Can generate

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

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. 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 that is 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 that can cluster a plurality of cryptocurrency accounts into a balance account 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 that can cluster 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 cryptocurrency accounts according to the value of the withdrawn cryptocurrency. For example, the computing device 100 may generate a seventh clustering rule for clustering a plurality of cryptocurrency accounts into a balance account according to the value of the cryptocurrency withdrawn using the artificial intelligence model.

In various embodiments, the computing device 100 may analyze cryptocurrency transaction information to generate an eighth clustering rule clustering a cryptocurrency account based on the type of software processing the cryptocurrency transaction. . For example, the computing device 100 infers what software is used for a specific transaction using an artificial intelligence model, and implements an eighth clustering rule that can cluster a plurality of cryptocurrency accounts into a balance account according to the reasoning result. Can be created.

Here, although the computing device 100 is described as generating the first clustering rule to the eighth clustering rule using the same one artificial intelligence model, the present invention is not limited thereto, and the clustering rules are respectively used using different artificial intelligence models. Can generate

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 of clustering a plurality of cryptocurrency accounts using the different clustering rules by the computing device 100 will be described with reference to FIGS. 4 to 10.

4 is a flowchart of a method of clustering a cryptocurrency account using a first clustering rule in 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. (Example: Step S120 in FIG. 3).

In step S220, the computing device 100 analyzes each of the plurality of cryptocurrency transaction information using the first clustering rule, thereby determining whether each cryptocurrency transaction information, that is, each transaction has a plurality of cryptocurrency deposit history. I can judge.

In operation S230, the computing device 100 may cluster a plurality of cryptocurrency accounts for each owner based on whether or not a plurality of cryptocurrency deposit histories exist in one transaction determined in operation S220. For example, the computing device 100 may record a plurality of cryptocurrency deposit histories in one transaction (for example, a history deposited into a first cryptocurrency account, a history deposited into a second cryptocurrency account, and a deposit into a third cryptocurrency account). Cryptocurrency accounts (e.g., first cryptocurrency account, second cryptocurrency account, and third cryptocurrency account) corresponding to a plurality of cryptocurrency deposit history, respectively It can be determined to be an account and clustered. However, it is not limited to this.

5 is a flowchart of a method of clustering a cryptocurrency account using a second clustering rule in 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 second clustering to cluster cryptocurrency accounts according to whether or not they are reused. A rule may be generated (for example, step S120 in FIG. 3).

In step S320, the computing device 100 may determine whether to reuse the address for each of the addresses of the plurality of cryptocurrency accounts using the second clustering rule. For example, the computing device 100 may determine that if the same cryptocurrency address is used more than once for each address of a plurality of cryptocurrency accounts, it is determined that it has been reused.

In general, the balance account of the cryptocurrency account cannot be reused repeatedly because it is automatically generated by the cryptocurrency wallet software and continuously updated with the new address. However, in the case of a payment account, since people who wish to trade cryptocurrency are handed over manually, the same address can be reused repeatedly.

Based on these contents, in step S330, the computing device 100 may cluster a cryptocurrency account determined to have been reused in step S320 into a payment account, and in step S340, the computing device 100 in step S320 It is possible to cluster a cryptocurrency account that has been determined that the address has not been reused into a balance account. However, it is not limited to this.

6 is a flowchart of a method of clustering a cryptocurrency account using a third clustering rule in various embodiments.

Referring to FIG. 6, in step S410, the computing device 100 uses the artificial intelligence model to cluster the cryptocurrency account according to whether the total amount of cryptocurrency withdrawn from each of the plurality of cryptocurrency accounts is a rounded number. A rule may be generated (for example, step S120 in FIG. 3).

In step S420, the computing device 100 may determine whether the total amount of cryptocurrency withdrawn from each of the plurality of cryptocurrency accounts is a rough number using the third clustering rule generated in step S410. For example, the computing device 100 is whether or not the total amount of cryptocurrency withdrawn from each of a plurality of cryptocurrency accounts is just a falling number (for example, the number rounded up, down, or rounded up to two or more digits at the end or end of the sum) Can judge. However, the present invention is not limited thereto, and the computing device 100 may convert the total amount of cryptocurrency into other currencies (eg, Korean won, dollars, yen, etc.), and determine whether the total sum of other converted currencies is a rounded number.

In general, the total amount of cryptocurrency deposited into the payment account is only likely to be a falling number (e.g. 0.1 BTC), so it is highly likely to be a round number, and the remaining cryptocurrency deposited into the balance account is a non-falling number (non -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 rough number, the computing device 100 may cluster the cryptocurrency account in which the cryptocurrency of the round number is deposited as a payment account. have.

In addition, in step S440, the computing device 100 may cluster the cryptocurrency account in which the cryptocurrency deposited with the total amount of cryptocurrency withdrawn through the step S420 into a balance account. However, it is not limited to this.

7 is a flowchart of a method of clustering a cryptocurrency account using a fourth clustering rule in various embodiments.

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

In step S520, the computing device 100 may analyze the size of the cryptocurrency that is deposited and withdrawn using the fourth clustering rule, and in step S530, the computing device 100 may encrypt a plurality of passwords based on the analysis result in step S520. Currency 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 deposited into the payment account (eg Bitcoin) is 1 BTC, you may use only a cryptocurrency account with 2 BTC. When analyzing the transaction, you can see that a cryptocurrency account with 2 BTC and a cryptocurrency account with 3 BTC was used. That is, the computing device 100 can 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 BTCs are deposited into a payment account, and a cryptocurrency account in which 1 BTC is deposited into a balance account. However, it is not limited to this.

8 is a flowchart of a method of clustering a cryptocurrency account using a fifth clustering rule in various embodiments.

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

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

In step S630, when it is determined that the scripting method between the transactions indicating the withdrawal of the cryptocurrency is different through the step S620, the computing device 100 is the same cryptocurrency as the scripting method of the transaction indicating the deposit of the cryptocurrency among the transactions indicating the withdrawal of the cryptocurrency. You can choose a transaction that points to a withdrawal.

In step S640, the computing device 100 may cluster the cryptocurrency account that is the target of withdrawal of the transaction indicating the withdrawal of the cryptocurrency selected in step S630 as the balance account. However, it is not limited to this.

9 is a flowchart of a method of clustering a cryptocurrency account using a sixth clustering rule in 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 cryptocurrency transaction history included in the transaction using an artificial intelligence model. (Example: Step S120 in FIG. 3).

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

Generally, the transaction history deposited into the balance account is likely to be generated at the 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 into the balance account through the analysis result through step S720. However, it is not limited to this.

10 is a flowchart of a method of clustering a cryptocurrency account using a seventh clustering rule in various embodiments.

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

In operation S820, the computing device 100 may select a history of withdrawal of cryptocurrency with different values of the cryptocurrency withdrawn from a plurality of cryptocurrency withdrawal history using the seventh clustering rule generated in operation S810.

In operation S830, the computing device 100 may cluster the cryptocurrency withdrawal history selected in operation S820 (eg, a history of withdrawal of cryptocurrency with different values of the withdrawn cryptocurrency) into a balance account. However, it is not limited to this.

In various embodiments, the computing device 100 may obtain information about two or more cryptocurrency accounts whose owners are the same among a plurality of cryptocurrency accounts, and two or more cryptocurrency based on cryptocurrency transaction information between two or more cryptocurrency accounts where the owner is the same. You can get cryptocurrency trading patterns between currency accounts. For example, the computing device 100 visualizes the cryptocurrency transaction information, and analyzes the visualized cryptocurrency transaction information to use a method for determining the purpose of each cryptocurrency account or to generate a group. As a result of clustering a plurality of cryptocurrency accounts for each owner based on the clustered rules, the owner of the data can obtain information about two or more cryptocurrency accounts that are the same.

In various embodiments, the computing device 100 may learn information about two or more cryptocurrency accounts and cryptocurrency transaction patterns in an artificial intelligence model using machine learning. Here, the artificial intelligence model may be a model that analyzes the cryptocurrency transaction information and determines whether the transaction is between cryptocurrency accounts owned by the same owner, but is not limited thereto.

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

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

In various embodiments, the computing device 100 may perform clustering for each of the plurality of cryptocurrency accounts using only the verified clustering rule among the plurality of clustering rules. However, it is not limited to this. Hereinafter, a method for 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 for determining a use of a cryptocurrency account performed by a cryptocurrency account clustering device 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 may perform cryptocurrency transaction information (eg, transaction) generated between multiple cryptocurrency accounts from an external server 300 (eg, a server that mediates cryptocurrency transactions between multiple cryptocurrency accounts). Can be provided. However, it is not limited to this (eg, step S110 in 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 for determining a usage 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 a square shape having a predetermined size, but is not limited thereto, and various shapes (for example, based on various information about each of a plurality of cryptocurrency accounts) Round, oval, rectangular).

In various embodiments, the computing device 100 generates nodes corresponding to each of the plurality of cryptocurrency accounts, but the plurality of cryptocurrency accounts is based on the attributes (for example, purposes) of the cryptocurrency account determined through step S1400 described below. The attribute of the node corresponding to each can be determined. For example, the computing device 100 may provide a node 11 corresponding to a cryptocurrency account (eg, black market account, fraud account, and gambling account) used for crime and fraud based on the purpose of each of the plurality of cryptocurrency accounts. The first color (eg, red) may be set, and the node 12 corresponding to the cryptocurrency account associated with the crime and fraudulent account may be set to the second color (eg, yellow). Also, the computing device 100 may set the node 13 corresponding to a normal use cryptocurrency account (eg, exchange account, mining account, and commerce account) to a third color (eg, white). Through this, it is possible to make it easier for a user who receives and monitors visualized cryptocurrency transaction information to monitor and monitor cryptocurrency accounts used for crime and fraud. However, it is not limited to this.

In one embodiment, the computing device 100 may connect a sender's cryptocurrency account and a recipient's cryptocurrency account with an edge among nodes corresponding to a plurality of cryptocurrency accounts, based on cryptocurrency transaction information. For example, when it is determined that the computing device 100 is transferred 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 can be connected by a line.

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

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

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

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

In one embodiment, the computing device 100 may determine a property of a transaction corresponding to each line using a pre-trained artificial intelligence model. For example, when a node-to-node remittance is made, it may be a remittance for cryptocurrency trading for investment purposes, a remittance to pay for products or services, asset management or tax evasion, or illegal transfer of funds. It may be a money transfer.

To determine this, the computing device 100 may utilize not only information on the quantity, frequency, frequency, and time of each transaction, but also cryptocurrency quote information at the time of the transaction. Cryptocurrency has the characteristic that its value can change greatly from time to time unlike the traditional currency. Therefore, by using the market price information at the time of the transaction of the cryptocurrency, it is possible to determine the value of the money actually transferred, and accordingly the attribute of the transaction.

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

In addition, if a large number of transactions occur in a situation where the cryptocurrency market is rapidly changing, it may be judged that it is a transaction for the purpose of investment, and transactions that occur regardless of changes in the cryptocurrency market have a distance from the investment (e.g., individual multiple It can also be judged by transactions between accounts, asset management, tax evasion, etc.).

According to an embodiment, as described above, the line connecting each node may be visualized according to a preset rule, and the computing device 100 uses the pre-trained artificial intelligence model to attribute the transaction represented by each line. Can judge. For example, the computing device 100 may extract various features from the shape of each visualized line and estimate the attribute of the 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 about a visualized image and a solution corresponding to each image (eg, account attributes, transaction attributes, etc.). However, it is not limited thereto.

Referring back to FIG. 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 may be visualized derived through step S1300. Based on the analysis result of the cryptocurrency transaction information, the use of each of the plurality of cryptocurrency accounts may be determined.

In various embodiments, the computing device 100 automatically extracts feature values from cryptocurrency transaction information visualized in a graph form at step S1200 using a pre-trained artificial intelligence model (eg, a deep learning model). It is possible to determine the purpose of each of the plurality of cryptocurrency accounts using the extracted feature values. For example, the feature value may be the overall form of the graph, and the computing device 100 may determine which cryptocurrency account is located on the graph according to the overall shape of the cryptocurrency transaction information visualized in the graph form. It is possible to determine whether the account performs a role. However, it is not limited to this.

In one embodiment, the pre-trained artificial intelligence model is based on learning data labeled with properties of a cryptocurrency account corresponding to one or more nodes included in the visualized cryptocurrency transaction information and the visualized cryptocurrency transaction information in a graph form. Can be learned. As another example, the pre-trained artificial intelligence model clusters the attributes of the account corresponding to one or more nodes from the cryptocurrency transaction information visualized in the graph form, and receives or automatically determines the attribute information of each clustered account to encrypt the cryptocurrency account. You can also classify the properties of.

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

In various embodiments, the computing device 100 may use a learning model of any one of a graph-based ring diagram learning model, an account address-based map learning model, and an owner-based ring diagram learning model based on the type of input data. You can decide the use of. 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 ring diagram learning model in various embodiments.

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

The graph-based ring diagram learning model is a model that can process tens of billions of cryptocurrency transaction information stored in graph form as meaningful information, and analyzes the cryptocurrency transaction information converted into graphs and the characteristics of graphs automatically extracted therefrom. : Deep learning analysis) may be a model that can determine the purpose of the cryptocurrency account. For example, the graph-based ring diagram learning model provides an in-depth analysis (eg, clustering, classification) of the nodes corresponding to the cryptocurrency account and the characteristics of the line representing the cryptocurrency transaction information. You can derive the information you need to determine your use. In addition, the graph-based semi-supervised learning model may be applied to a graph-based semi-supervised learning by using a small amount of correct answer data. However, it is not limited to this.

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

14 is a diagram illustrating a configuration of determining a 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 using an account address-based supervised learning model.

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

In various embodiments, when the number of the plurality of nodes connected by a line is equal to or less than 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-based map learning Using the model, it is possible to analyze the transaction pattern for each of the cryptocurrency accounts according to the transaction history for each of the plurality of cryptocurrency accounts, and determine the purpose of each of the plurality of cryptocurrency accounts based on the analyzed transaction pattern. For example, when the transaction pattern for the cryptocurrency account shows a wave form, the computing device 100 may determine the purpose of the cryptocurrency account as an ICO account, and the transaction pattern for the cryptocurrency account frequently occurs. If it is visible, the purpose of the cryptocurrency account can be determined as an exchange hot-willet account, and if the transaction pattern for the cryptocurrency account shows a staircase shape, the purpose of the cryptocurrency account can be determined as a personal account and an exchange cold wallet account. If the deposit and withdrawal amounts are the same in the transaction pattern for the cryptocurrency account, the purpose of the cryptocurrency account can be determined as an exchange user's Willett account. However, it is not limited to this.

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

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

The owner-based ring diagram learning model is a technology that processes cryptocurrency transaction information that has been reclassified based on the owner of the cryptocurrency account into meaningful information, and identifies the cases where the owners of multiple cryptocurrency accounts are the same and reclassifies them as one owner. It may be a model that learns the transaction pattern of the owner identified through Entity Heuristic Clustering. The owner-based ring diagram learning model may be a model that learns various data through ring diagram learning with high accuracy with only a few correct answer data, and ring deep learning analysis can be used to determine the use of a cryptocurrency account.

In various embodiments, when the number of the plurality of nodes connected by a line 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 may use an owner-based ring diagram learning model. At least one cryptocurrency account is grouped using a plurality of cryptocurrency accounts based on the owner of the cryptocurrency account, and at least one cryptocurrency grouped using cryptocurrency transaction information generated from the at least one cryptocurrency account grouped. The pattern for the currency account may be analyzed, and the use for each of the at least one group of cryptocurrency accounts grouped may be determined based on the pattern for the at least one grouped cryptocurrency account (eg, FIG. 8). However, it is not limited to this.

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

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

Referring to FIG. 16, the computing device 100 may collect cryptocurrency account information in the form of an image and one or more keywords from an external website, and analyze the cryptocurrency account information in the form of an image to analyze the textual cryptocurrency account Information may be extracted, and one or more keywords may be analyzed to determine the purpose of the cryptocurrency account corresponding to the extracted textual cryptocurrency account information.

In general, transactions using cryptocurrencies value anonymity, so the addresses of cryptocurrency accounts are often stored in the form of images. At this time, the computing device 100 may search for cryptocurrency account information in the form of an image and extract a cryptocurrency account in text form from the found cryptographic account information in the form of an image (for example, using OCR character reading technology). .

In addition, the computing device 100 may extract one or more major keywords from the website where the cryptocurrency account address is posted, and determine the purpose of the extracted cryptocurrency account in text form according to the extracted keyword. For example, when the keyword extracted from the website where the cryptocurrency account address is posted is 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 website and use it to determine the purpose of the cryptocurrency account.

In general, dark web sites are used for crimes in cyberspace because they refer to the web, which requires the use of a specific program to access, and cannot identify the accessor or server in the usual way. In particular, various illegal crimes using cryptocurrency as a payment method are prevalent in the Dark Web. In order to monitor cryptocurrency accounts performing such illegal transactions, the computing device 100 uses externally stored Darkweb access software (using a Tor browser, software accessing a domain ending in * .onion). It accesses the dark website, automatically collects information related to cryptocurrency from the dark website every predetermined period, and determines the purpose of each of the plurality of cryptocurrency accounts based on the information related to the cryptocurrency collected from the dark website. Can be. For example, the computing device 100 may set a cryptocurrency account corresponding to a cryptocurrency account address obtained from a dark website among a plurality of cryptocurrency accounts as a dark web account. However, it is not limited to this.

The above-described method for clustering cryptocurrency accounts using artificial intelligence has been described with reference to the flowchart shown in the drawings. For simplicity, the method of clustering a cryptocurrency account using artificial intelligence is illustrated and illustrated as a series of blocks, but the present invention is not limited to the order of the blocks, and some blocks are in a different order from the one shown and described herein. Or can be performed simultaneously. In addition, new blocks not described in the present specification and drawings may be added, or some blocks may be deleted or changed.

The embodiments of the present invention have been described with reference to the accompanying drawings, but a person skilled in the art to which the present invention pertains may implement the present invention in other specific forms without changing its technical spirit or essential features. You will understand. Therefore, it should be understood that the above-described embodiments 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, the method comprising:
Collecting cryptocurrency transaction information generated between a plurality of cryptocurrency accounts;
Visualizing the collected cryptocurrency transaction information;
Using a pre-trained artificial intelligence model, wherein the artificial intelligence model is learned based on visualized cryptocurrency transaction information and learning data labeled with attributes of one or more cryptocurrency accounts included in the visualized cryptocurrency transaction information. Determining an attribute of each of the plurality of cryptocurrency accounts;
Acquiring data obtained by clustering the plurality of cryptocurrency accounts for each owner by using at least one of the determined attributes of each of the plurality of cryptocurrency accounts and a previously generated clustering rule;
Generating a clustering rule for clustering the plurality of cryptocurrency accounts using the result data; And
Clustering the plurality of cryptocurrency accounts using the generated clustering rule,
Generating the clustering rule,
Generating a first clustering rule clustering a cryptocurrency account according to the cryptocurrency deposit history based on the cryptocurrency transaction information;
Determining whether to reuse each of a plurality of cryptocurrency account addresses based on the cryptocurrency transaction information, and generating a second clustering rule to cluster the cryptocurrency accounts according to the reuse; And
And generating a third clustering rule clustering the cryptocurrency account according to whether the total amount of cryptocurrency withdrawn from each of the plurality of cryptocurrency accounts is a round number based on the cryptocurrency transaction information,
The step of clustering the plurality of cryptocurrency accounts,
Using the first clustering rule, when a plurality of cryptocurrency deposit histories exist in one transaction, a plurality of cryptocurrency accounts respectively corresponding to the plurality of cryptocurrency deposit histories are used as cryptocurrency accounts of the same owner. Clustering;
By using the second clustering rule, a cryptocurrency account whose address is reused among the plurality of cryptocurrency accounts is clustered as a payment account, and a cryptocurrency account whose address is not reused is clustered as a change account. step;
Using the third clustering rule, when the total amount of the withdrawal cryptocurrency is a rounded number, clustering the cryptocurrency account in which the encrypted number of the cryptocurrency is deposited into a payment account; And
If the total amount of the withdrawn cryptocurrency is not a rounded number using the third clustering rule, clustering the cryptocurrency account in which the non-rounded cryptocurrency is deposited into a change account,
A method of clustering cryptocurrency accounts using artificial intelligence. delete delete delete According to claim 1,
Generating the clustering rule,
And generating a fourth clustering rule clustering the cryptocurrency account according to the size of the cryptocurrency that is deposited and withdrawn based on the cryptocurrency transaction information.
The step of clustering the plurality of cryptocurrency accounts,
Clustering the plurality of cryptocurrency accounts into one of a payment account or a balance account using the fourth clustering rule,
A method of clustering cryptocurrency accounts using artificial intelligence. According to claim 1,
Generating the clustering rule,
And generating a fifth clustering rule clustering a cryptocurrency account 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,
When the scripting methods of a plurality of transactions indicating the withdrawal of cryptocurrency are different from each other by using the fifth clustering rule, the withdrawal of the cryptocurrency having the same scripting method as the transaction indicating the deposit of cryptocurrency is selected, and Clustering the target cryptocurrency account into a balance account,
A method of clustering cryptocurrency accounts using artificial intelligence. According to claim 1,
Generating the clustering rule,
Generating a sixth clustering rule clustering a cryptocurrency account based on the order of cryptocurrency transaction history included in the transaction based on the cryptocurrency transaction information,
The step of clustering the plurality of cryptocurrency accounts,
Clustering a cryptocurrency account corresponding to the last cryptocurrency transaction history among a plurality of cryptocurrency transaction histories included in the transaction using the sixth clustering rule, into a balance account,
A method of clustering cryptocurrency accounts using artificial intelligence. According to claim 1,
Generating the clustering rule,
And generating a seventh clustering rule clustering a cryptocurrency account according to the value of the cryptocurrency withdrawn based on the cryptocurrency transaction information.
The step of clustering the plurality of cryptocurrency accounts,
Using the seventh clustering rule, clustering a cryptocurrency account corresponding to a different cryptocurrency withdrawal history value of the withdrawn cryptocurrency among the plurality of cryptocurrency withdrawal histories into a balance account,
A method of clustering cryptocurrency accounts using artificial intelligence. According to claim 1,
The step of collecting the cryptocurrency transaction information,
Obtaining information on two or more cryptocurrency accounts of which the owner is the same among the plurality of cryptocurrency accounts; And
And 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,
Generating the clustering rule,
And learning information about the two or more cryptocurrency accounts and the cryptocurrency transaction patterns in an artificial intelligence model using machine learning,
The AI model is a model that analyzes the cryptocurrency transaction information and determines whether the transaction is between cryptocurrency accounts owned by the same owner.
A method of clustering cryptocurrency accounts using artificial intelligence. The method of claim 9,
Generating the clustering rule,
Extracting the same owner clustering rule in which the owner clusters the same cryptocurrency account among the cryptocurrency accounts using the cryptocurrency 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 a verification result,
A method of clustering cryptocurrency accounts using artificial intelligence. delete A memory for storing one or more instructions; And
And a processor executing the one or more instructions stored in the memory.
The processor executes the one or more instructions,
An apparatus for performing the method of claim 1. A computer program stored in a recording medium readable by a computer to perform the method of claim 1 in combination with a computer that is hardware.

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