CN112926993B - Information generation method based on block chain safety big data and block chain service system - Google Patents

Information generation method based on block chain safety big data and block chain service system Download PDF

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CN112926993B
CN112926993B CN202110396267.7A CN202110396267A CN112926993B CN 112926993 B CN112926993 B CN 112926993B CN 202110396267 A CN202110396267 A CN 202110396267A CN 112926993 B CN112926993 B CN 112926993B
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CN112926993A (en
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郭栋
花壮林
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Harbin Harbin Bank Consumption Finance Co.,Ltd.
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    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/382—Payment protocols; Details thereof insuring higher security of transaction
    • G06Q20/3829—Payment protocols; Details thereof insuring higher security of transaction involving key management

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Abstract

The disclosed embodiment provides an information generation method based on blockchain security big data and a blockchain service system, which start with payment service data covering a large number of payment services and relations among the payment services, determine risk behavior association degrees among the payment services in the payment service data, convert the risk behavior association degrees among the payment services into risk behavior association degrees among security protection operation units according to mapping information between the payment services and the security protection operation units, and update a protection association rule of the security protection operation units based on the risk behavior association degrees among the security protection operation units; therefore, the protection association rule of the safety protection operation unit is optimized under the condition of payment generation on the block chain line, and further, the more accurate safety portrait generation of the key payment safety data collected according to the updated protection association rule of the safety protection operation unit is facilitated based on the safety protection operation program.

Description

Information generation method based on block chain safety big data and block chain service system
Technical Field
The disclosure relates to the technical field of blockchain payment security, and exemplarily relates to an information generation method based on blockchain security big data and a blockchain service system.
Background
The block chain is used as an integrated innovation of a plurality of technologies such as a point-to-point network, cryptography, a sharing mechanism, an intelligent contract and the like, and provides a trusted channel for information and value transfer and exchange in an untrusted network. The block chain technology is a popular concept in the world no matter in the aspect of constructing the internet with free value circulation or in the aspect of data sharing based on 'establishing a joint multi-center' of an enterprise, and has wide market prospect.
In the related art, under a security protection operation program based on block chain payment, security portrait generation needs to be performed on key payment security data, so that a reference basis can be provided for a subsequent research and development plan conveniently.
Disclosure of Invention
In order to overcome at least the above disadvantages in the prior art, an object of the present disclosure is to provide an information generating method based on big data for blockchain security and a blockchain service system.
In a first aspect, the present disclosure provides an information generating method based on blockchain security big data, which is applied to a blockchain service system, where the blockchain service system is in communication connection with a plurality of blockchain node terminals, and the method includes:
acquiring block chain security big data generated by online payment of a block chain, wherein the block chain security big data is used for representing a payment security verification event between block chain payment services, and the block chain payment services are payment services related to a target security protection operation program;
generating a blockchain payment service sequence based on the blockchain security big data, wherein the blockchain payment service sequence is a sequence formed by a plurality of blockchain payment services with payment security verification events in the blockchain security big data;
determining payment risk behavior information corresponding to the blockchain payment service in the blockchain security big data according to the blockchain payment service sequence, and determining a first risk behavior association degree between safety protection operation units on the target safety protection operation program according to risk behavior association degree between the payment risk behavior information corresponding to the blockchain payment service in the blockchain security big data and mapping information between the blockchain payment service and the safety protection operation units on the target safety protection operation program;
and updating the protection association rule of the safety protection operation unit on the target safety protection operation program based on the first risk behavior association degree, collecting key payment safety data based on the updated protection association rule of the safety protection operation unit, and generating a safety portrait of the key payment safety data based on the safety protection operation rule corresponding to the safety protection operation program.
In a second aspect, an embodiment of the present disclosure further provides an information generating system based on blockchain security big data, where the information generating system based on blockchain security big data includes a blockchain service system and a plurality of blockchain link point terminals communicatively connected to the blockchain service system;
the block chain service system is configured to:
acquiring block chain security big data generated by online payment of a block chain, wherein the block chain security big data is used for representing a payment security verification event between block chain payment services, and the block chain payment services are payment services related to a target security protection operation program;
generating a blockchain payment service sequence based on the blockchain security big data; the blockchain payment service sequence is a sequence consisting of a plurality of blockchain payment services with payment security verification events in the blockchain security big data;
determining payment risk behavior information corresponding to the blockchain payment service in the blockchain security big data according to the blockchain payment service sequence, and determining a first risk behavior association degree between safety protection operation units on the target safety protection operation program according to risk behavior association degree between the payment risk behavior information corresponding to the blockchain payment service in the blockchain security big data and mapping information between the blockchain payment service and the safety protection operation units on the target safety protection operation program;
and updating the protection association rule of the safety protection operation unit on the target safety protection operation program based on the first risk behavior association degree, collecting key payment safety data based on the updated protection association rule of the safety protection operation unit, and generating a safety portrait of the key payment safety data based on the safety protection operation rule corresponding to the safety protection operation program.
Based on any one of the aspects, the method and the system start with payment service data covering a large number of payment services and relations among the payment services, determine the risk behavior association degree among the payment services in the payment service data, convert the risk behavior association degree among the payment services into the risk behavior association degree among the safety protection operation units according to mapping information between the payment services and the safety protection operation units, and update the protection association rule of the safety protection operation units based on the risk behavior association degree among the safety protection operation units; therefore, the protection association rule of the safety protection operation unit is optimized under the condition of payment generation on the block chain line, and further, the more accurate safety portrait generation of the key payment safety data collected according to the updated protection association rule of the safety protection operation unit is facilitated based on the safety protection operation program.
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In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings that need to be called in the embodiments are briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present disclosure, and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic view of an application scenario of an information generation system based on big block chain security data according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of an information generating method based on big block chain security data according to an embodiment of the present disclosure;
fig. 3 is a functional module schematic diagram of an information generating apparatus based on big data block chain security according to an embodiment of the present disclosure;
fig. 4 is a schematic block diagram of structural components of a blockchain service system for implementing the above information generation method based on blockchain security big data according to an embodiment of the present disclosure.
Detailed Description
The present disclosure is described in detail below with reference to the drawings, and the specific operation methods in the method embodiments can also be applied to the device embodiments or the system embodiments.
Fig. 1 is an interaction diagram of an information generation system 10 based on blockchain security big data according to an embodiment of the present disclosure. The information generating system 10 based on the blockchain security big data may include a blockchain service system 100 and a blockchain node terminal 200 communicatively connected to the blockchain service system 100. The information generating system 10 based on blockchain security big data shown in fig. 1 is only one possible example, and in other possible embodiments, the information generating system 10 based on blockchain security big data may also include only at least some of the components shown in fig. 1 or may also include other components.
In one embodiment, the blockchain service system 100 and the blockchain node terminal 200 in the information generation system 10 based on blockchain security big data may cooperatively perform the information generation method based on blockchain security big data described in the following method embodiments, and the detailed description of the method embodiments may be referred to in the following steps for the specific blockchain service system 100 and the blockchain node terminal 200.
To solve the technical problem in the foregoing background, fig. 2 is a schematic flowchart of an information generating method based on blockchain security big data according to an embodiment of the present disclosure, and the information generating method based on blockchain security big data according to the present embodiment may be executed by the blockchain service system 100 shown in fig. 1, and the information generating method based on blockchain security big data is described in detail below.
Step S110, acquiring block chain safety big data generated by paying on a block chain line; the blockchain security big data is used for representing a payment security verification event between blockchain payment services, and the blockchain payment services are payment services related to a target security protection operating program.
In one embodiment, the payment service data may be data to be analyzed, wherein the association between the payment services is represented by a knowledge graph, payment items in the payment service data correspond to the payment services, and payment security verification events between the payment services correspond to the payment security verification events between the payment services; for example, "payment service a" is one payment service in the payment service data, "payment service B" is another payment service in the payment service data, and the payment verification process authentication is performed between "payment service a" and "payment service B" in the payment service data through "payment verification process S", and "payment service a" - "payment verification process S" - "payment service B" constitutes one payment security verification event.
In one embodiment, the blockchain service system 100 may first obtain blockchain security big data generated by online payment of blockchains, where the blockchain security big data can represent a payment security verification event between blockchain payment services, where the blockchain payment services are payment services related to a target security protection operating program. Considering that payment service data constructed based on all information in a network is usually very huge, a lot of calculation amount is consumed for updating a protection association rule of a safety protection operation unit on a target safety protection operation program based on the payment service data, and a lot of information irrelevant to the target safety protection operation program exists in the network; based on this, the manner provided by the embodiment of the present disclosure starts with big blockchain security data used for characterizing a payment security verification event between blockchain payment services related to a target security protection operating program, and updates a protection association rule of a security protection operating unit on the target security protection operating program.
In one embodiment, in practical applications, the blockchain service system 100 can autonomously extract blockchain security big data from initial payment service data, where the initial payment service data is payment service data constructed based on all information in the network; the blockchain security big data can also be directly acquired from other devices, and the disclosure does not make any limitation on the implementation manner of acquiring the blockchain security big data generated by paying on the blockchain line by the blockchain service system 100.
An implementation of the blockchain service system 100 extracting big blockchain security data from the initial payment service data is described below.
The blockchain service system 100 may select a payment service satisfying a predetermined condition from the initial payment service data as a blockchain payment service, where the predetermined condition includes at least one of the following: the payment service type is a preset type, and the online rate of the payment service exceeds a preset online rate threshold value; and then, determining the big block chain security data according to the payment security verification event of the selected block chain payment service in the initial payment service data.
In one embodiment, the initial payment service data may be comprised of a number of payment security verification events having payment security verification events, each payment security verification event comprised of an initial payment service, a payment service relationship, and an ending payment service. For example, assume that the initial payment service included in the payment security verification event is "payment service a", the payment service relationship is "payment verification process S", and the terminating payment service is "payment service B". Each payment service in the initial payment service data further includes a set of associated data corresponding thereto, and in one embodiment, the associated data included in each payment service includes, but is not limited to, a payment service type, a payment service name, a payment service online rate, and the like.
When the blockchain service system 100 extracts the big blockchain security data from the initial payment service data, a payment service meeting a preset condition may be selected from the initial payment service data as a blockchain payment service. In one embodiment, the blockchain service system 100 may select a payment service with a payment service type of a preset type from the initial payment service data as a blockchain payment service; in one embodiment, the blockchain service system 100 may also select a payment service with a payment service rate exceeding a preset online rate threshold from the initial payment service data as the blockchain payment service.
In an embodiment, in practical application, the blockchain service system 100 may screen the blockchain payment service based on only the payment service type, may also screen the blockchain payment service based on only the online rate of the payment service, may also screen the blockchain payment service based on both the payment service type and the online rate of the payment service, or the blockchain service system 100 may also screen the blockchain payment service based on other payment service related data, where the disclosure does not make any limitation on a preset condition according to when the blockchain payment service is selected from the initial payment service data.
After the block chain service system 100 selects the block chain payment service related to the target security protection operating program from the initial payment service data, the payment security verification event of each selected block chain payment service in the initial payment service data can be extracted, and further, based on each selected block chain payment service and the payment security verification event of each block chain payment service in the initial payment service data, big block chain security data suitable for updating the protection association rule of the security protection operating unit for the target security protection operating program is constructed.
Step S120, generating a block chain payment service sequence based on the block chain safety big data; the blockchain payment service sequence is a sequence consisting of a plurality of the blockchain payment services with payment security verification events in the blockchain security big data.
After the blockchain service system 100 obtains the blockchain security big data, several blockchain payment service sequences may be generated based on the blockchain security big data. In one embodiment, the blockchain service system 100 may compose a sequence of blockchain payment services with a series of blockchain payment services having a payment security verification event therebetween based on a payment security verification event between blockchain payment services in the blockchain security big data.
In practical applications, the block chain service system 100 may generate the block chain payment service sequence based on the big block chain security data by using an irregular walking algorithm, where the irregular walking algorithm is substantially a mathematical statistical model, and a series of tracks may be generated based on the irregular walking algorithm, and each step in the walking process is random. Considering that a large number of long blockchain payment service sequences may be generated based on the randomness of the random walk algorithm, in order to limit the number and range of generated blockchain payment service sequences to a certain extent, a random walk condition may be set in the generation of blockchain payment service sequences based on the random walk algorithm.
In one embodiment, when generating a blockchain payment service sequence based on blockchain security big data, the generating may be implemented by at least one of the following:
and generating a block chain payment service sequence based on the block chain payment service with the subscription payment security verification event in the block chain security big data through a random walking algorithm.
And generating a block chain payment service sequence based on the block chain payment service belonging to the same subscription attribute in the block chain security big data through a random walking algorithm. Namely, several block chain payment services corresponding to the same hypernym in the block chain security big data can be used to form a block chain payment service sequence.
In an embodiment, in practical application, when a random walk algorithm is used to generate a block chain payment service sequence based on block chain security big data, other conditions for limiting payment security verification events among block chain payment services in the block chain payment service sequence can be set according to practical requirements.
Step S130, determining, according to the blockchain payment service sequence, payment risk behavior information corresponding to the blockchain payment service in the blockchain security big data, and determining, according to a risk behavior association degree between the payment risk behavior information corresponding to the blockchain payment service in the blockchain security big data and mapping information between the blockchain payment service and a security protection operating unit on the target security protection operating program, a first risk behavior association degree between the security protection operating units on the target security protection operating program.
After the blockchain service system 100 generates a plurality of blockchain payment service sequences based on the blockchain security big data, the payment risk behavior information corresponding to the blockchain payment service in the blockchain payment service sequence may be correspondingly determined based on each generated blockchain payment service sequence, and thus, the payment risk behavior information corresponding to each blockchain payment service in the blockchain security big data is determined through traversal in the above manner. Since the payment risk behavior information corresponding to the above blockchain payment service is determined by the blockchain service system 100 based on the blockchain payment service sequence including the blockchain payment service, the payment risk behavior information corresponding to the blockchain payment service can reflect the relevance between the blockchain payment service and other blockchain payment services to a certain extent.
A specific implementation manner of determining the payment risk behavior information corresponding to the blockchain payment service is described below.
The blockchain service system 100 may first perform unique hot code feature extraction, such as unique hot coding, on each blockchain payment service in the blockchain security big data, so as to obtain unique hot code feature information corresponding to each blockchain payment service; then, training a payment risk behavior extraction network based on the unique hot coded feature information corresponding to the block chain payment service in the block chain payment service sequence, and continuously adjusting an embedded word vector (payment risk behavior prediction information) of the block chain payment service in the process of training the payment risk behavior extraction network; and finally, taking the payment risk behavior prediction information of the block chain payment service after the training of the payment risk behavior extraction network as the payment risk behavior information corresponding to the block chain payment service.
In one embodiment, in order to convert the blockchain payment service in the big blockchain security data into a form that can be identified by a machine, the blockchain service system 100 needs to extract the unique hot code feature of the blockchain payment service in the big blockchain security data to obtain the unique hot code feature information corresponding to each blockchain payment service. Since the one-hot coded feature information obtained by the one-hot coded feature extraction cannot reflect the risk behavior association degree between the block chain payment services, while the embodiment of the present disclosure needs to obtain a feature that can reflect the relevance between the blockchain payment services (i.e. the payment risk behavior information corresponding to the blockchain payment services), therefore, the embodiment of the disclosure needs to initialize the payment risk behavior prediction information corresponding to the blockchain payment service, then continuously updating the payment risk behavior prediction information corresponding to the blockchain payment service in the process of training the payment risk behavior extraction network by utilizing the unique hot code characteristic information of the blockchain payment service in the blockchain payment service sequence, the weights in the features are adjusted, and after the training of the payment risk behavior extraction network is completed, the payment risk behavior information capable of reflecting the relevance between the block chain payment services can be obtained correspondingly.
For example, in the process of a block chain payment service adjacent to a block chain payment service in a preset window in a block chain payment service prediction block chain payment service sequence through a payment risk behavior extraction network, unique hot coded feature information corresponding to the block chain payment service is input into the payment risk behavior extraction network, the block chain payment service can be mapped into corresponding payment risk behavior prediction information through a hidden layer in the payment risk behavior extraction network, and then the probability that all block chain payment services in block chain security big data are adjacent to the block chain payment service is output through an output layer in the payment risk behavior extraction network. The characteristic obtained after the unique hot code characteristic information of the block chain payment service is processed by a hidden layer in the payment risk behavior extraction network is the payment risk behavior information corresponding to the block chain payment service required by the embodiment of the disclosure.
After the block chain service system 100 calculates and obtains the payment risk behavior information corresponding to each target vector in the block chain security big data, the block chain payment services in the block chain security big data can be combined pairwise, and the risk behavior association degree between the payment risk behavior information corresponding to each two block chain payment services is calculated. Furthermore, the blockchain service system 100 may obtain mapping information between the blockchain payment service and the security protection operating units on the target security protection operating program, convert the risk behavior association degree between the payment risk behavior information corresponding to the blockchain payment service into the risk behavior association degree between the security protection operating units on the target security protection operating program, and record the risk behavior association degree between the security protection operating units thus determined as the first risk behavior association degree between the security protection operating units.
In an embodiment, the mapping information between the blockchain payment service and the security protection operating unit in the target security protection operating program may be determined according to associated data such as a payment service name of the blockchain payment service. The mapping information may be determined temporarily when determining the first risk behavior association degree between the safety protection operating units, or may be determined in advance. The present disclosure does not set any limit to the determination method and determination timing of the mapping information between the blockchain payment service and the security defense operating unit.
In an embodiment, in practical application, if two blockchain payment services are mapped to the same security protection operating unit, when determining the first risk behavior association degree between the security protection operating units, the risk behavior association degree between the payment risk behavior information corresponding to each of the two blockchain payment services may not be considered. In other words, the first risk behavior association degree between the safety protection operating units is substantially determined based on the risk behavior association degree between the payment risk behavior information corresponding to the blockchain payment services respectively mapped to different safety protection operating units.
A specific implementation manner corresponding to the first risk behavior association degree between the safety protection operating units on the target safety protection operating program is determined as follows.
The blockchain service system 100 may first determine, according to the payment risk behavior information corresponding to each blockchain payment service in the blockchain security big data, first risk information (which may be distributed in a form of a matrix) corresponding to a first risk behavior association degree, where each risk node in the first risk information corresponding to the first risk behavior association degree is used to represent a risk behavior association degree between a blockchain payment service corresponding to a first risk dimension (e.g., a matrix row) where the risk node is located and a blockchain payment service corresponding to a second risk dimension (e.g., a matrix column) where the risk node is located. Furthermore, the blockchain service system 100 may convert, according to mapping information between each blockchain payment service and each safety protection operating unit on the target safety protection operating program, the first risk information corresponding to the first risk behavior association degree into second risk information corresponding to a second risk behavior association degree, where each risk node in the second risk information corresponding to the second risk behavior association degree is used to represent a risk behavior association degree between a safety protection operating unit corresponding to a first risk dimension where the risk node is located and a safety protection operating unit corresponding to a second risk dimension where the risk node is located.
In an embodiment, after determining the payment risk behavior information corresponding to each blockchain payment service in the blockchain security big data, the blockchain service system 100 may combine every two blockchain payment services in the blockchain security big data to obtain a plurality of blockchain payment service pairs; then, calculating cosine risk behavior association degree between payment risk behavior information corresponding to two block chain payment services for each block chain payment service pair to serve as the risk behavior association degree corresponding to the block chain payment service pair; furthermore, first risk information corresponding to the first risk behavior association degree is constructed on the basis of the risk behavior association degree corresponding to each block chain payment service, the first risk information corresponding to the first risk behavior association degree takes each block chain payment service as a row, and takes each block chain payment service as a second risk dimension, and risk nodes located in the x-th first risk dimension and the y-th second risk dimension in the first risk information corresponding to the first risk behavior association degree are actually cosine risk behavior association degrees between the payment risk behavior information of the block chain payment service corresponding to the x-th first risk dimension and the payment risk behavior information of the block chain payment service corresponding to the y-th second risk dimension.
In an embodiment, in practical application, the block chain service system 100 may construct the first risk information corresponding to the first risk behavior association degree by using the cosine risk behavior association degree between the payment risk behavior information, and may also construct the first risk information corresponding to the first risk behavior association degree by using the risk behavior association degree between the payment risk behavior information calculated based on other algorithms.
After the blockchain service system 100 constructs the first risk information corresponding to the first risk behavior association degree, the first risk information corresponding to the first risk behavior association degree may be converted into the second risk information corresponding to the second risk behavior association degree corresponding to the risk behavior association degree between the safety protection operation units for representing according to mapping information between each blockchain payment service and each safety protection operation unit on the target safety protection operation program. In an embodiment, when the blockchain service system 100 converts the second risk information corresponding to the second risk behavior association degree, for the risk behavior association degree between the payment risk behavior information corresponding to the two blockchain payment services mapped to the same safety protection operation unit, the risk behavior association degree may be discarded without adding the second risk information corresponding to the second risk behavior association degree.
It should be noted that, in practical applications, in order to more accurately update the protection association rule of the security protection operating unit on the target security protection operating program based on the risk behavior association degree between the security protection operating units on the target security protection operating program, the method provided in the embodiment of the present disclosure may determine the risk behavior association degree between the security protection operating units on the target security protection operating program from the dimension of the payment service data, and may also determine the risk behavior association degree between the security protection operating units on the target security protection operating program from the protection association rule of the existing security protection operating unit on the target security protection operating program.
That is, the blockchain service system 100 may determine the second risk behavior association degree between the safety protection operating units on the target safety protection operating program according to the protection association rule of the safety protection operating unit running on the target safety protection operating program. In one embodiment, the blockchain service system 100 may obtain the protection association rules of the safing operation units of some or all of the risk generating behaviors on the target safing operation program, and then analyze the risk behavior association degrees between the safing operation units based on the obtained protection association rules of the safing operation units, for example, if the protection association rules of the safing operation unit of the risk generating behavior a include the safing operation unit 1, the safing operation unit 2, the safing operation unit 3, and the safing operation unit 4, and the protection association rules of the safing operation unit of the risk generating behavior B include the safing operation unit 2, the safing operation unit 3, the safing operation unit 4, and the safing operation unit 5, the blockchain service system 100 may, in the process of analyzing the protection association rules of the safing operation units, the safety protection operation unit 1 and the safety protection operation unit 5 can be considered to have a certain risk behavior association degree; based on the above basic idea, the blockchain service system 100 may determine the risk behavior association degree between the safety protection operating units on the target safety protection operating program according to the protection association rule of the existing safety protection operating unit on the target safety protection operating program, and the risk behavior association degree between the safety protection operating units thus determined may be recorded as a second risk behavior association degree between the safety protection operating units.
A specific implementation manner corresponding to the second risk behavior association degree between the safety protection operating units on the target safety protection operating program is determined as follows.
The blockchain service system 100 may first construct risk information of a protection association rule of an initial security protection operation unit according to a protection association rule of a security protection operation unit on a target security protection operation program, where each risk node in the risk information of the protection association rule of the initial security protection operation unit is used to represent an influence weight of a risk subject behavior corresponding to a first risk dimension where the risk node is located on a security protection operation unit corresponding to a second risk dimension where the risk node is located. Furthermore, the blockchain service system 100 may train a target feature vector based on the risk information of the protection association rule of the starting safety protection operation unit, and use the trained target feature vector as third risk information corresponding to a third risk behavior association degree, where each risk node in the third risk information corresponding to the third risk behavior association degree is used to represent the risk behavior association degree between a label corresponding to the first risk dimension where the risk node is located and a safety protection operation unit corresponding to the second risk dimension where the risk node is located.
In an embodiment, the blockchain service system 100 may construct, according to the protection association rules of the security protection operating units of all risk generation behaviors on the target security protection operating program, risk information V of the protection association rule of the initial security protection operating unit, where a first risk dimension in the risk information of the protection association rule of the initial security protection operating unit represents a risk generation behavior user, a second risk dimension represents a security protection operating unit tag, and each risk node in the risk information of the protection association rule of the initial security protection operating unit represents an influence weight of a risk subject behavior corresponding to the first risk dimension where the risk node is located on the security protection operating unit corresponding to the second risk dimension where the risk node is located. In other words, a risk node of a first risk dimension in the risk information of the protection association rule of the initial safety protection operation unit can represent the influence weight of a risk generation behavior on each safety protection operation unit on the target safety protection operation program, and a risk node of a second risk dimension in the initial image matrix can represent the influence weight of all risk generation behaviors on one safety protection operation unit on the target safety protection operation program.
Step S140, updating the protection association rule of the safety protection operation unit on the target safety protection operation program based on the first risk behavior association degree, collecting key payment safety data based on the updated protection association rule of the safety protection operation unit, and generating a safety portrait of the key payment safety data based on the safety protection operation rule corresponding to the safety protection operation program.
After determining the first risk behavior association degree between the safety protection operating units on the target safety protection operating program, the block chain service system 100 may update the protection association rule of the safety protection operating unit on the target safety protection operating program based on the first risk behavior association degree between the safety protection operating units. The idea is that based on the first risk behavior association degree between the safety protection operation units, the safety protection operation unit which is similar to the original safety protection operation unit is updated in the protection association rule of the safety protection operation unit.
In a case that the first risk behavior association degree between the safety protection operating units on the target safety protection operating program represents second risk information corresponding to the second risk behavior association degree, the blockchain service system 100 may update the protection association rule of the safety protection operating unit on the target safety protection operating program in the following manner:
determining risk information for updating the protection association rule of the safety protection operation unit according to second risk information corresponding to the second risk behavior association degree and risk information of the protection association rule of the starting safety protection operation unit; the risk information of the protection association rule of the initial safety protection operation unit is constructed according to the protection association rule of the existing safety protection operation unit on the target safety protection operation program, and each risk node in the risk information of the protection association rule of the initial safety protection operation unit and the risk information of the protection association rule of the updated safety protection operation unit is used for representing the influence weight of the risk subject behavior corresponding to the first risk dimension where the risk node is located on the safety protection operation unit corresponding to the second risk dimension where the risk node is located.
In an embodiment, the blockchain service system 100 may multiply the risk information V of the protection association rule of the initial security protection operating unit by the second risk information L corresponding to the second risk behavior association degree to obtain the risk information V' of the protection association rule of the updated security protection operating unit. The risk information V of the protection association rule of the initial safety protection operation unit may be constructed by the blockchain service system 100 according to the protection association rule of the safety protection operation unit of all risk generating behaviors on the target safety protection operation program, a first risk dimension in the risk information V of the protection association rule of the initial safety protection operation unit corresponds to the risk generating behavior, a second risk dimension corresponds to the safety protection operation unit, and a risk node Rij in the risk information V of the protection association rule of the initial safety protection operation unit is used to represent an influence weight of an xth risk generating behavior on an yth safety protection operation unit. The second risk information L corresponding to the second risk behavior association degree is determined by the blockchain service system 100 based on the risk behavior association degree between the payment risk behavior information corresponding to each blockchain payment service in the blockchain security big data, a first risk dimension and a second risk dimension in the second risk information L corresponding to the second risk behavior association degree both correspond to the security protection operating unit, and a risk node Pij in the second risk information L corresponding to the second risk behavior association degree is used for representing the risk behavior association degree between the xth security protection operating unit and the yth security protection operating unit. The risk information V' for updating the protection association rule of the safety protection operation unit, which is obtained by multiplying the risk information V of the protection association rule of the starting safety protection operation unit by the second risk information L corresponding to the second risk behavior association degree, can reflect the safety protection operation unit updated in the protection association rule of the safety protection operation unit, the risk information V' of the protection association rule of the updated safety protection operating unit is similar to the risk information V of the protection association rule of the starting safety protection operating unit, the first risk dimension corresponds to a risk generating behavior, the second risk dimension corresponds to a safety protection operation unit, and a risk node R 'ij in risk information V' of a protection association rule of the updated safety protection operation unit is used for representing influence weight of an x-th risk generating behavior on a y-th safety protection operation unit after the updating processing of the safety protection operation unit.
If the blockchain service system 100 determines the second risk behavior association degree between the safety protection operating units on the target safety protection operating program according to the protection association rule of the safety protection operating unit on the target safety protection operating program, the blockchain service system 100 may update the protection association rule of the safety protection operating unit on the target safety protection operating program based on the first risk behavior association degree and the second risk behavior association degree between the safety protection operating units at this time.
In an embodiment, the block chain service system 100 may fuse the first risk behavior association degree and the second risk behavior association degree between the safety protection operation units on the target safety protection operation program to obtain the target risk behavior association degree between the safety protection operation units, which is matched with the service mining target on the target safety protection operation program and is matched with the relationship between the payment services in the block chain safety big data, and further, the block chain service system 100 may update the protection association rule of the safety protection operation units on the target safety protection operation program according to the target risk behavior association degree between the safety protection operation units.
Under the condition that the first risk behavior association degree between the safety protection operation units on the target safety protection operation program represents second risk information corresponding to the second risk behavior association degree, and the second risk behavior association degree between the safety protection operation units represents third risk information corresponding to the third risk behavior association degree, the block chain service system 100 may update the protection association rule of the safety protection operation unit on the target safety protection operation program in the following manner:
fusing second risk information corresponding to the second risk behavior association degree and third risk information corresponding to the third risk behavior association degree to obtain target risk information; and determining the risk information for updating the protection association rule of the safety protection operation unit according to the target risk information and the risk information of the protection association rule of the starting safety protection operation unit.
In an embodiment, the blockchain service system 100 may perform weighted summation processing on the second risk information L corresponding to the second risk behavior association degree and the third risk information W corresponding to the third risk behavior association degree according to a preset weight; for example, assuming that the blockchain service system 100 sets a weight value x1 for the second risk information L corresponding to the second risk behavior association degree and sets a weight value x2 for the third risk information W corresponding to the third risk behavior association degree, the blockchain service system 100 may calculate the target risk information Q by the following formula:
Q＝P*x1+W*x2
in one embodiment, the weighted values x1 and x2 are determined by the blockchain service system 100 according to the attention degrees of the second risk information corresponding to the second risk behavior association degree and the third risk information corresponding to the third risk behavior association degree, if the protection association rule of the safety protection operation unit is updated, the risk behavior association degree between the safety protection operation units determined based on the payment safety verification events among the payment services in the blockchain safety big data is concerned more, x1 may be set to be greater than x2, if the risk behavior association degree between the safety protection operation units determined based on the protection association rule of the safety protection operation unit on the target safety protection operation program is more concerned when updating the protection association rule of the safety protection operation unit, x2 may be set to be greater than x1, and the weight values x1 and x2 set are not specifically limited by this disclosure.
Further, the blockchain service system 100 may multiply the target risk information Q by the risk information V of the protection association rule of the initial security protection operation unit to obtain the risk information V' of the protection association rule of the updated security protection operation unit.
It should be noted that, in practical application, after determining, by the blockchain service system 100, the risk information of the protection association rule of the updated safety protection operation unit in any one of the manners described above, the block chain service system 100 needs to update the protection association rule of the existing safety protection operation unit on the target safety protection operation program based on the risk information of the protection association rule of the updated safety protection operation unit. In order to enable the blockchain service system 100 to update the protection association rule of the existing safety protection operation unit more conveniently based on the risk information of the protection association rule of the updated safety protection operation unit, after the blockchain service system 100 generates the risk information of the protection association rule of the updated safety protection operation unit, the generated risk information of the protection association rule of the updated safety protection operation unit may be updated in the following implementation manner.
In a possible implementation manner, the blockchain service system 100 may determine, for a risk node in the same dimension in the risk information of the protection association rule updating the security protection operating unit and the risk information of the protection association rule of the initial security protection operating unit, whether a risk node in the risk information of the protection association rule of the initial security protection operating unit, which is in the node, is greater than a first target value, and if so, may set the risk node in the risk information of the protection association rule updating the security protection operating unit, which is in the node, to 0.
In one embodiment, if it is determined that the risk generating action a has an operational tendency for the security protection operating unit 1 according to the risk information of the protection association rule of the starting security protection operating unit, it is also determined that the risk generating action a has an operational tendency for the security defending operating unit 1 according to the risk information for updating the defending association rule of the security defending operating unit, the blockchain service system 100 needs to set a risk node in the risk information for updating the protection association rule of the safeguard operating unit for characterizing the influence weight of the risk generating action a on the safeguard operating unit 1 to 0, thereby avoiding that when the risk information based on the protection association rule of the updated safety protection operation unit is updated to the risk generation behavior safety protection operation unit, and adding the original safety protection operation unit in the risk information of the protection association rule of the safety protection operation unit.
In general, in the risk information of the protection association rule of the initial security protection operation unit, if a certain risk node is not 0, the security protection operation unit corresponding to the second risk dimension where the risk node is located should be in the protection association rule of the security protection operation unit of the risk subject behavior corresponding to the first risk dimension where the risk node is located, in this case, the blockchain service system 100 needs to set the first target value to 0. Of course, in some cases, in the risk information of the protection association rule of the initial safety protection operation unit, only if a certain risk node is greater than the preset value a, the safety protection operation unit corresponding to the second risk dimension where the risk node is located will be in the protection association rule of the safety protection operation unit of the risk subject behavior corresponding to the first risk dimension where the risk node is located, and in this case, the blockchain service system 100 needs to set the first target value as a. The first target value is not specifically limited by the present disclosure.
In another possible implementation manner, the blockchain service system 100 may determine, for each risk node in the risk information of the protection association rule updating the safety protection operating unit, whether the risk node is less than or equal to the second target value, and if so, set the risk node in the risk information of the protection association rule updating the safety protection operating unit to 0.
In one embodiment, in order to ensure sparsity of the risk information of updating the protection association rule of the safety protection operation unit, the blockchain service system 100 needs to set a second target value between 0 and 1, and for the risk nodes in the risk information of updating the protection association rule of the safety protection operation unit, which are less than or equal to the second target value, the blockchain service system 100 needs to set 0 accordingly.
After the risk information of the protection association rule of the updated safety protection operation unit is processed through the two implementation manners, the safety protection operation unit corresponding to the second risk dimension of the non-zero item in each first risk dimension in the risk information of the protection association rule of the updated safety protection operation unit is actually the safety protection operation unit updated according to the risk subject behavior corresponding to the first risk dimension.
In an embodiment, in practical application, in addition to performing update processing on the risk information of the protection association rule of the updated security protection operating unit through the two implementation manners, other manners may also be used to perform update processing on the risk information of the protection association rule of the updated security protection operating unit.
The protection association rule updating method of the safety protection operation unit starts with payment service data covering a large number of payment services and relations among the payment services, determines the risk behavior association degree among the payment services in the payment service data, converts the risk behavior association degree among the payment services into the risk behavior association degree among the safety protection operation units according to mapping information between the payment services and the safety protection operation unit, and updates the protection association rule of the safety protection operation unit based on the risk behavior association degree among the safety protection operation units. Therefore, the protection association rule of the safety protection operation unit is quickly and accurately updated, and further, the method is favorable for generating a more accurate safety portrait for the key payment safety data collected according to the updated protection association rule of the safety protection operation unit based on the safety protection operation program.
In order to further understand the protection association rule updating method for the safety protection operation unit provided in the embodiment of the present disclosure, the block chain service system 100 is still taken as an example as an execution subject, and a whole example of the protection association rule updating method for the safety protection operation unit provided in the embodiment of the present disclosure is described below.
The method for updating the protection association rule of the security protection operation unit provided by the embodiment of the present disclosure is mainly implemented by four steps, which are, step 1, generating risk information L of the risk behavior association degree (i.e., second risk information corresponding to the second risk behavior association degree in the foregoing embodiment) by using the payment service data, step 2, generating risk information W of the risk behavior association degree (i.e., third risk information corresponding to the third risk behavior association degree in the foregoing embodiment) by using the protection association rule of the security protection operation unit, step 3, fusing the risk information of the risk behavior association degree, and step 4, generating and updating the protection association rule of the security protection operation unit, where the four steps are introduced below.
Step 1, generating risk information L of risk behavior association degree by using payment service data:
(1) extracting effective information from the initial payment service data to form block chain safety big data: the initial payment service data is composed of a plurality of payment security verification events (initial payment service/payment service relation/termination payment service) with payment security verification events, wherein each payment service comprises a group of associated data, and typical associated data comprises payment service type, payment service name, payment service online rate and the like. The method mainly extracts the block chain payment service with the payment service type being higher in the online rate of the payment service from the initial payment service data to form the block chain safety big data.
(2) Random walk based on block chain security big data: and randomly walking in the block chain security big data according to the relation among the block chain payment services to form a plurality of block chain payment service sequences. In one embodiment, the sequence of blockchain payment services may be generated based on a one-degree random walk between blockchain payment services.
(3) Training the payment risk behavior information and payment risk behavior prediction information of the block chain payment service: and training on a block chain payment service sequence generated by random walk by adopting a natural language description algorithm to obtain payment risk behavior prediction information of each block chain payment service in the block chain security big data.
(4) Calculating the risk behavior association degree between the payment risk behavior information of the block chain payment service: calculating cosine risk behavior association degrees between every two block chain payment services to obtain risk information of the risk behavior association degrees from the block chain payment services to the block chain payment services (namely first risk information corresponding to the first risk behavior association degrees).
(5) Generating risk information L of the risk behavior association degree of the safety protection operation unit: and mapping the blockchain payment service to the safety protection operation unit according to the associated data such as the payment service name of the blockchain payment service, and obtaining the risk information L of the risk behavior association degree from the safety protection operation unit to the safety protection operation unit.
Step 2, generating risk information W of the risk behavior association degree by using the protection association rule of the safety protection operation unit:
(1) inputting a protection association rule of a safety protection operation unit: and constructing risk information V (user-tag) of the protection association rule of the initial safety protection operation unit according to the protection association rule of the safety protection operation unit on the target safety protection operation program, wherein the first risk dimension represents a risk generation behavior, the second risk dimension represents a safety protection operation unit, and a risk node value in the risk information V of the protection association rule of the initial safety protection operation unit represents the influence weight of the risk generation behavior on the safety protection operation unit.
(2) Training a target feature vector: training risk information W of the risk behavior association degree of the safety protection operation unit by using risk information V of the protection association rule of the starting safety protection operation unit, wherein the risk information V of the protection association rule of the starting safety protection operation unit is still approximately equal to the risk information V of the protection association rule of the starting safety protection operation unit after being multiplied by the risk information W of the risk behavior association degree of the safety protection operation unit; and a risk node Wij in the risk information W of the risk behavior association degree of the safety protection operation unit represents the risk behavior association degree between the x-th safety protection operation unit and the y-th safety protection operation unit. When the risk information W of the risk behavior association degree of the safety protection operation unit is trained, the diagonal risk node of the risk information W of the risk behavior association degree of the safety protection operation unit needs to be kept to be 0, so as to avoid trivial solution (i.e. a matrix with the diagonal risk node being 0 and other risk nodes being 0) during training.
Step 3, fusing risk information of risk behavior association degree:
setting the weights [ x1, x2] to obtain the final target risk information Q ═ P × 1+ W × 2.
Step 4, generating a protection association rule for updating the safety protection operation unit:
and multiplying the risk information V of the protection association rule of the initial safety protection operation unit by the target risk information Q to obtain risk information V' of the protection association rule of the updated safety protection operation unit.
In one embodiment, regarding step S140, in the process of collecting the critical payment security data based on the updated security association rule of the security protection operating unit, the following exemplary sub-steps can be implemented, which are described in detail below.
And a substep S141 of performing cross-attribute-based safety protection attribute feature extraction on the updated protection association rule of the safety protection operation unit to obtain a plurality of safety protection attribute features combined with the protection attribute information.
In this embodiment, the cross-attribute may refer to performing safety protection attribute feature extraction for different service programs, for example, for different service programs, the features of general protection are different, so that the cross-attribute-based safety protection attribute feature extraction may be performed in a targeted manner to obtain a plurality of safety protection attribute features combined with protection attribute information.
And a substep S142, based on any one of the plurality of safety protection attribute features, performing anti-fraud feature-based update processing on the protection association rule of the target safety protection operation unit with different protection attribute information to obtain the protection association rules of the plurality of first safety protection operation units corresponding to the safety protection attribute features.
And a substep S143 of performing update processing based on the anti-phishing feature on the protection association rule of the target safety protection operation unit to obtain protection association rules of a plurality of second safety protection operation units corresponding to the protection association rules of the safety protection operation unit.
And a substep S144 of fusing the protection association rules of the safety protection operation unit with the protection association rules of the first safety protection operation units and the protection association rules of the second safety protection operation units respectively to obtain the protection association rules of the matched target safety protection operation unit.
And the substep S145 is to collect the key payment safety data corresponding to the protection association rule of the target safety protection operation unit as the key payment safety data collection characteristic for responding to the protection association rule of the safety protection operation unit and collect the key payment safety data based on the key payment safety data collection characteristic.
For example, the protection association rule of the target safety protection operation unit may include a data distribution relationship between a plurality of target safety protection operation units and a plurality of target safety protection operation units, so that a key payment security data collection characteristic of a collection track corresponding to each target safety protection operation unit may be obtained by combining the data distribution relationship between the plurality of target safety protection operation units, and then the key payment security data collection characteristic is used as a key payment collection security data characteristic for responding to the protection association rule of the safety protection operation unit, and key payment security data collection is performed based on the key payment security data collection characteristic.
Based on the sub-steps, the embodiment obtains the protection association rules of the updated safety protection operation units with different protection attribute information by performing the update processing based on the anti-fraud feature and the update processing based on the anti-phishing feature, so that the data processing strategies brought by each label are handled through the protection association rules of the updated safety protection operation units with different protection attribute information; and by the updating processing based on the anti-fraud characteristic and the updating processing based on the anti-phishing characteristic, the protection association rule of the updated safety protection operation unit obtained by two optimization modes is integrated, and the diversity of the protection association rule of the updated safety protection operation unit obtained by optimization can be improved, so that the accuracy of collecting the key payment safety data is improved.
In one embodiment, an artificial intelligence learning model for critical payment security data gathering may include a plurality of model branch structures.
Step S141 may be implemented by the following exemplary embodiments.
(1) And carrying out safety protection attribute feature extraction on the protection association rule of the safety protection operation unit through any one model branch structure in the plurality of model branch structures based on the protection attribute of the protection association rule different from the safety protection operation unit to obtain the safety protection attribute feature of the corresponding model branch structure.
(2) And taking the safety protection attribute characteristics of the corresponding model branch structures as a plurality of safety protection attribute characteristics.
The artificial intelligence learning model for collecting the key payment safety data comprises a feature connection branch structure, wherein in the training phase, the embodiment can extract the safety protection attribute feature of the protection association rule of the preset safety protection operation unit through any one model branch structure in a plurality of model branch structures based on the protection attribute different from the protection association rule of the preset safety protection operation unit to obtain the safety protection attribute feature corresponding to the model branch structure.
And then, performing characteristic connection on the safety protection attribute characteristics corresponding to the model branch structure through the characteristic connection branch structure to obtain cross-attribute safety protection characteristics corresponding to the protection association rules of the preset safety protection operation unit, constructing model evaluation parameters of the artificial intelligent learning model based on the cross-attribute safety protection characteristics and cross-attribute safety protection characteristic labels of the protection association rules of the preset safety protection operation unit, updating the parameters of the artificial intelligent learning model until the model evaluation parameters are converged, and taking the updated parameters of the artificial intelligent learning model when the model evaluation parameters are converged as the parameters of the trained artificial intelligent learning model.
The method comprises the steps of conducting attribute prediction on safety protection attribute characteristics of a corresponding model branch structure to obtain prediction attributes of protection association rules corresponding to a preset safety protection operation unit, and constructing category model evaluation parameters of an artificial intelligent learning model based on the prediction attributes and the preset attributes of the protection association rules of the preset safety protection operation unit.
In the process of constructing the model evaluation parameter of the artificial intelligent learning model based on the cross-attribute safety protection feature and the cross-attribute safety protection feature label of the protection association rule of the preset safety protection operation unit, the cross-attribute model evaluation parameter of the artificial intelligent learning model can be constructed based on the cross-attribute safety protection feature and the cross-attribute safety protection feature label of the protection association rule of the preset safety protection operation unit, and the class model evaluation parameter and the cross-attribute model evaluation parameter are weighted and summed to obtain the model evaluation parameter of the artificial intelligent learning model.
In one embodiment, for step S142, the following process may be performed for any one of the plurality of security attribute features in combination with the security attribute information:
(1) and determining anti-fraud characteristic parameters between the security protection attribute characteristics and the security protection attribute characteristics of the protection association rules of the target security protection operation units with different protection attribute information respectively.
(2) And sequencing the protection association rules of the target safety protection operation units according to the priority order based on the anti-fraud characteristic parameters between the safety protection attribute characteristics and the safety protection attribute characteristics of the protection association rules of the target safety protection operation units, and taking the protection association rules of the first N target safety protection operation units sequenced in the sequencing information as the protection association rules of the plurality of first safety protection operation units. Wherein N is a natural number.
In one embodiment, for step S143, the overall protection associated item in the protection associated rule of the safety protection operating unit may be matched with the protection associated rule of the target safety protection operating unit to obtain a usage rate of the overall protection associated item in the protection associated rule of the target safety protection operating unit, a proportion of identical associated rules between the protection associated rule of the safety protection operating unit and the protection associated rule of the target safety protection operating unit is determined based on the usage rate of the overall protection associated item in the protection associated rule of the target safety protection operating unit, the protection associated rules of the target safety protection operating unit are sorted according to a priority order based on the proportion of identical associated rules between the protection associated rule of the safety protection operating unit and the protection associated rule of the target safety protection operating unit, and taking the protection association rules of the first M target safety protection operation units sequenced in the sequencing information as the protection association rules of the plurality of second safety protection operation units. Wherein M is a natural number.
For example, in an embodiment, before the protection association rule of the safety protection operation unit is fused with the protection association rules of the first safety protection operation units and the protection association rules of the second safety protection operation units, the embodiment may further sort the protection association rules of the first safety protection operation units and the protection association rules of the second safety protection operation units according to the priority order based on the matching degree between the protection association rules of the safety protection operation units and the protection association rules of the first safety protection operation units and the matching degree between the protection association rules of the safety protection operation units and the protection association rules of the second safety protection operation units, and use the protection association rules of the first safety protection operation units or the protection association rules of the second safety protection operation units sorted in the first L in the sorting information as the protection association rules of the optimized safety protection operation units for performing the business relationship matching And protecting the association rule. Wherein L is a natural number.
In an embodiment, based on the foregoing description, the embodiments of the present disclosure may further include the following steps.
Step A110, obtaining key payment safety data of a target payment scene searched based on the block chain safety big data generated by block chain online payment.
In this embodiment, the big blockchain security data is used to characterize a payment security verification event between blockchain payment services, where the blockchain payment services are payment services related to the target security protection operating program. The specific implementation will be described in detail later.
Step A120, obtaining paired payment behavior information in a paired payment scene associated with the target payment scene from the key payment safety data according to a pre-trained AI network corresponding to the target safety protection operation program, and obtaining a payment behavior knowledge graph corresponding to the paired payment behavior information.
In this embodiment, when the risk behavior coverage information of the target payment scenario needs to be obtained, the blockchain service system 100 may obtain relevant payment scenario data of the paired payment scenario, where the relevant payment scenario data includes at least one of dynamic payment scenario data and static payment scenario data. The block chain service system 100 may identify, according to a pre-trained AI network corresponding to the target security protection operating program, relevant payment scene data of the paired payment scene to obtain risk behavior information in the paired payment scene, where the risk behavior information of the paired payment scene includes paired payment behavior information in the paired payment scene, a payment behavior knowledge graph of the paired payment behavior information, and the like; paired payment behavior information herein may refer to any of the risk behavior tags in the paired payment scenario.
It should be noted that the pre-trained AI network may be obtained by collecting training data samples under a target security protection operation program and corresponding risk behavior preset attributes for training, so that the pre-trained AI network may obtain a plurality of paired payment behavior information based on the key payment security data, and then screen out the paired payment behavior information in the paired payment scenario associated with the target payment scenario, thereby obtaining a payment behavior knowledge graph corresponding to the paired payment behavior information.
It can be understood that the specific training process of the pre-trained AI network may be implemented in a conventional training manner in the prior art, and details of this training process are not described in this disclosure.
Step A130, determining the payment prediction flow direction characteristics of the paired payment behavior information in the paired payment scene according to the payment behavior knowledge graph.
In this embodiment, the payment prediction flow direction feature of the target risky behavior tag in the target payment scene is matched with the payment prediction flow direction feature of the paired payment behavior information in the paired payment scene, and the payment behavior knowledge graph (for example, the statistical number of the risky behavior tags) corresponding to the target risky behavior tag in the target payment scene is unknown, that is, it is difficult to obtain the payment prediction flow direction feature of the target risky behavior tag in the target payment scene. Therefore, the blockchain service system 100 may determine the payment prediction flow direction feature of the paired payment behavior information in the paired payment scenario according to the payment behavior knowledge map, which is beneficial to determine the payment prediction flow direction feature of the target risk behavior tag in the target payment scenario according to the payment prediction flow direction feature of the paired payment behavior information in the paired payment scenario; namely, the payment prediction flow characteristics of the paired payment behavior information in the paired payment scene are used for reflecting: matching behavior features between a payment behavior knowledge graph of paired payment behavior information in a paired payment scenario and risk behavior distribution information in the paired payment scenario.
Step A140, predicting the risk behavior probability of the risk behavior of the paired payment behavior information according to the paired payment behavior information and the payment behavior knowledge graph, as a prediction probability, and determining the risk tendency value information of the risk behavior of the paired payment behavior information in the paired payment scene according to the prediction probability.
In this embodiment, the behavior probability described above may be used to characterize the attention degree of the risk generating behavior group, and the higher the behavior probability is, the higher the attention degree of the risk subject behavior group with a wide surface is.
In this embodiment, the blockchain service system 100 may predict, according to the paired payment behavior information and the payment behavior knowledge graph in the paired payment scenario, a risk behavior probability of a risk behavior of the paired payment behavior information as a prediction probability; the predicted probability may include at least one of a risk behavior probability when the risk behavior of the pair of payment behavior information is not triggered and a risk behavior probability when the risk behavior of the pair of payment behavior information is triggered.
In an embodiment, the implementation manners of determining the risk tendency value information of the risk behavior of the pair payment behavior information in the pair payment scenario according to the prediction probability include the following three implementation manners.
In a first implementation manner, when the predicted probability includes a risk behavior probability when the risk behavior of the paired payment behavior information is not triggered, the block chain service system 100 may use the risk behavior probability when the risk behavior of the paired payment behavior information is not triggered as a first behavior probability; and generating a risk tendency reference value of the risk behavior of the paired payment behavior information when the risk behavior is not triggered according to the first behavior probability, wherein the risk tendency reference value is used as the first reference value. The first reference value is used for reflecting the ratio of the first behavior probability to the first total behavior probability, and the first total behavior probability is the total behavior probability of the risk behavior in the paired payment scene when the risk behavior is not triggered; and taking the first reference value as risk tendency value information of the risk behaviors of the pair payment behavior information in the pair payment scene.
If none of the risk behaviors in the paired payment scenario is triggered, the blockchain service system 100 may use the risk behavior probability when the risk behavior of the paired payment behavior information is not triggered as the first behavior probability; and accumulating and summing the first behavior probability to obtain a total behavior probability of the risk behavior when the risk behavior in the paired payment scene is not triggered, wherein the total behavior probability is used as the first total behavior probability. Further, the ratio of the first action probability to the first total action probability is used as a risk tendency reference value of the risk action of the paired payment action information when the risk action is not triggered; taking a risk tendency reference value of the risk behavior of the paired payment behavior information when the risk behavior is not triggered as a first reference value, namely that the risk tendency value information of the risk behavior of the paired payment behavior information in the paired payment scene comprises the first reference value; the risk tendency value information is used for reflecting the proportion of the risk behavior probability of the risk behavior of the paired payment behavior information when the risk behavior is not triggered.
In a second implementation manner, when the predicted probability includes the risk behavior probability when the risk behavior of the paired payment behavior information is triggered, the block chain service system 100 may use the risk behavior probability when the risk behavior of the paired payment behavior information is triggered as a second behavior probability; and generating a risk tendency reference value of the risk behavior of the paired payment behavior information when the risk behavior is triggered according to the second behavior probability, wherein the risk tendency reference value is used as a second reference value. The second reference value is used for reflecting the ratio of the first secondary behavior probability and the second total behavior probability, and the second total behavior probability is the total risk behavior probability of the risk behavior in the paired payment scene when the risk behavior is triggered; and taking the second reference value as risk tendency value information of the risk behaviors of the pair payment behavior information in the pair payment scene.
If the risk behaviors in the paired payment scenario are all triggered, the block chain service system 100 may use the risk behavior probability when the risk behaviors of the paired payment behavior information are triggered as a second behavior probability; and the second behavior probabilities are accumulated and summed to obtain the total behavior probability of the risk behaviors when the risk behaviors in the paired payment scene are triggered, and the total behavior probability is used as the second total behavior probability. Further, the ratio of the second behavior probability to the second total behavior probability is used as a risk tendency reference value of the risk behavior of the paired payment behavior information when triggered; taking a risk tendency reference value of the risk behavior of the paired payment behavior information when triggered as a second reference value, wherein the risk behavior of the paired payment behavior information in the paired payment scene comprises the risk tendency value information; namely, the risk tendency value information is used for reflecting the proportion of the risk behavior probability of the risk behavior of the paired payment behavior information when the risk behavior is not triggered.
In a third implementation manner, when the prediction probability may include the risk behavior probability when the risk behavior of the paired payment behavior information is not triggered and the risk behavior probability when the risk behavior of the paired payment behavior information is triggered; the blockchain service system 100 may use the risk behavior probability when the risk behavior of the paired payment behavior information is not triggered as the first behavior probability; and generating a risk tendency reference value of the risk behavior of the paired payment behavior information when the risk behavior is not triggered according to the first behavior probability, wherein the risk tendency reference value is used as the first reference value. Taking the risk behavior probability when the risk behavior of the paired payment behavior information is triggered as a second behavior probability; generating a risk tendency reference value of the risk behavior of the paired payment behavior information when the risk behavior is triggered according to the second behavior probability, and taking the risk tendency reference value as a second reference value; further, the first reference value and the second reference value may be used as risk tendency value information of the risk behavior of the pair payment behavior information in the pair payment scenario.
If the risk behavior in the paired payment scenario is triggered and not triggered, the blockchain service system 100 may refer to the above steps to obtain a first reference value and a second reference value, and use the first reference value and the second reference value as risk tendency value information of the risk behavior of the paired payment behavior information in the paired payment scenario; namely, the risk tendency value information of the risk behavior of the paired payment behavior information in the paired payment scenario is used for reflecting: the proportion of the risk behavior probability of the risk behavior of the paired payment behavior information when the risk behavior is not triggered is larger than the proportion of the risk behavior probability of the risk behavior of the paired payment behavior information when the risk behavior is triggered.
Step A150, obtaining the risk behavior probability of the risk behavior in the target payment scene as the target behavior probability, and predicting the risk behavior coverage information of the target payment scene according to the payment prediction flow direction characteristic, the risk tendency value information and the target behavior probability.
The blockchain service system 100 may obtain, from the traffic management device in the target payment scenario, a risk behavior probability of a risk behavior in the target payment scenario as a target behavior probability, where the target behavior probability is a total risk behavior probability of the risk behavior in the target payment scenario. In one embodiment, when none of the risky behaviors of the target payment scenario is triggered, the target behavior probability is the risky behavior probability of the risky behavior in the target payment scenario when the risky behavior is not triggered. In one embodiment, when all the risky behaviors in the target payment scenario are triggered, the target behavior probability is the risky behavior probability of the risky behaviors in the target payment scenario at the moment of triggering. In one embodiment, when there are triggered risky behaviors and non-triggered risky behaviors in the target payment scenario, the target behavior probability is the sum of the risky behavior probability of the triggered risky behaviors in the target payment scenario and the risky behavior probability of the non-triggered risky behaviors in the target payment scenario.
The payment prediction flow direction feature is used for reflecting the matching behavior feature between the payment behavior knowledge graph corresponding to the paired payment behavior information and the risk behavior distribution information in the paired payment scene, that is, the payment prediction flow direction feature can be used for reflecting the relationship between the payment behavior knowledge graph corresponding to the paired payment behavior information and the risk behavior distribution information in the paired payment scene. The risk tendency value information is used for reflecting the ratio between the risk behavior probability of the risk behavior of the paired payment behavior information and the risk behavior total behavior probability of the risk behavior in the paired payment scene, and the risk behavior probability of the risk behavior of the paired payment behavior information is determined according to the payment behavior knowledge graph corresponding to the paired payment behavior information, so that the risk tendency value information can be used for reflecting the relationship among the payment behavior knowledge graph, the risk behavior probability of the risk behavior of the paired payment behavior information and the risk behavior total behavior probability of the risk behavior in the paired payment scene. The target payment scene is associated with the paired payment scene, namely, the payment prediction flow direction characteristic and the risk tendency value information corresponding to the target payment scene are respectively matched with the payment prediction flow direction characteristic and the risk tendency value information corresponding to the paired payment scene, namely, the relationship between the payment behavior knowledge graph corresponding to the target risk behavior tag and the risk behavior distribution information in the target payment scene is matched with the relationship between the payment behavior knowledge graph corresponding to the paired payment behavior information and the risk behavior distribution information in the paired payment scene; and the relation among the payment behavior knowledge graph corresponding to the target risk behavior label, the risk behavior probability of the risk behavior of the paired payment behavior information and the risk behavior total behavior probability of the risk behavior in the paired payment scene is matched with the relation among the payment behavior knowledge graph corresponding to the paired payment behavior information, the risk behavior probability of the risk behavior of the paired payment behavior information and the risk behavior total behavior probability of the risk behavior in the paired payment scene.
The blockchain service system 100 may use the payment prediction flow direction feature corresponding to the paired payment scenario as the payment prediction flow direction feature corresponding to the target payment scenario, and use the risk tendency value information corresponding to the paired payment scenario as the risk tendency value information corresponding to the target payment scenario. Further, risk behavior coverage information of the target payment scene can be predicted according to the payment prediction flow direction characteristic, the risk tendency value information and the target behavior probability, wherein the risk behavior coverage information of the target payment scene is used for reflecting a risk behavior statistic value passing through the target payment scene in a unit time period.
Therefore, information pushing can be performed based on the risk behavior coverage information of the target payment scene, for example, information pushing corresponding to risk behaviors can be performed sequentially according to the size sequence of the risk behavior statistic.
In the embodiment of the application, the payment prediction flow direction feature is used for reflecting the matching behavior feature between the payment behavior knowledge graph corresponding to the paired payment behavior information and the risk behavior distribution information in the paired payment scene, that is, the payment prediction flow direction feature may be used for reflecting the relationship between the payment behavior knowledge graph corresponding to the paired payment behavior information and the risk behavior distribution information in the paired payment scene. The risk tendency value information is used for reflecting the ratio of the risk behavior probability of the risk behavior of the paired payment behavior information to the risk behavior total behavior probability of the risk behavior in the paired payment scene, and the risk behavior probability of the risk behavior of the paired payment behavior information is determined according to the payment behavior knowledge graph corresponding to the paired payment behavior information, so that the risk tendency value information can be used for reflecting the relationship among the payment behavior knowledge graph, the risk behavior probability of the risk behavior of the paired payment behavior information and the risk behavior total behavior probability of the risk behavior in the paired payment scene. The target payment scene is associated with the paired payment scene, namely, the payment prediction flow direction characteristic and the risk tendency value information corresponding to the target payment scene are respectively matched with the payment prediction flow direction characteristic and the risk tendency value information corresponding to the paired payment scene, namely, the relationship between the payment behavior knowledge graph corresponding to the target risk behavior tag and the risk behavior distribution information in the target payment scene is matched with the relationship between the payment behavior knowledge graph corresponding to the paired payment behavior information and the risk behavior distribution information in the paired payment scene; and the relation among the payment behavior knowledge graph corresponding to the target risk behavior label, the risk behavior probability of the risk behavior of the paired payment behavior information and the risk behavior total behavior probability of the risk behavior in the paired payment scene is matched with the relation among the payment behavior knowledge graph corresponding to the paired payment behavior information, the risk behavior probability of the risk behavior of the paired payment behavior information and the risk behavior total behavior probability of the risk behavior in the paired payment scene. Therefore, risk behavior coverage information of the target payment scene can be predicted according to the payment prediction flow direction feature, the risk tendency value information and the target behavior probability. The risk behavior probability of the risk behavior of the paired payment behavior information is determined according to the paired payment behavior information and the payment behavior knowledge graph, namely the risk behavior probability of the risk behavior of the paired payment behavior information considers that the risk behavior probability of the risk behavior is a multi-path condition; the problem that the risk behavior coverage information of the target payment scene is inaccurate due to the fact that the risk behavior probability of the risk behaviors of different risk behavior labels is multipath can be solved, accuracy of the risk behavior coverage information can be improved, and prediction accuracy of the risk behaviors can be improved.
Fig. 3 is a schematic functional block diagram of an information generating apparatus 300 based on blockchain security big data according to an embodiment of the present disclosure, and the functions of the functional blocks of the information generating apparatus 300 based on blockchain security big data are described in detail below.
An obtaining module 310, configured to obtain big blockchain security data generated by online payment of blockchains, where the big blockchain security data is used to characterize a payment security verification event between blockchain payment services, and the blockchain payment services are payment services related to a target security protection operating program.
A generating module 320, configured to generate a blockchain payment service sequence based on the blockchain security big data; the blockchain payment service sequence is a sequence consisting of a plurality of the blockchain payment services with payment security verification events in the blockchain security big data.
A determining module 330, configured to determine, according to the blockchain payment service sequence, payment risk behavior information corresponding to the blockchain payment service in the blockchain security big data, and determine, according to a risk behavior association degree between the payment risk behavior information corresponding to the blockchain payment service in the blockchain security big data and mapping information between the blockchain payment service and a security protection operating unit on the target security protection operating program, a first risk behavior association degree between the security protection operating units on the target security protection operating program.
A generating module 340, configured to update a protection association rule of a security protection operating unit on the target security protection operating program based on the first risk behavior association degree, perform key payment security data collection based on the updated protection association rule of the security protection operating unit, and perform security portrait generation on the key payment security data based on the security protection operating rule corresponding to the security protection operating program.
Fig. 4 is a schematic diagram illustrating a hardware structure of a blockchain service system 100 for implementing the above-described information generation method based on blockchain security big data according to an embodiment of the present disclosure, and as shown in fig. 4, the blockchain service system 100 may include a processor 110, a machine-readable storage medium 120, a bus 130, and a transceiver 140.
In a specific implementation process, at least one processor 110 executes computer-executable instructions stored in the machine-readable storage medium 120, so that the processor 110 may execute the information generation method based on the blockchain security big data according to the above method embodiment, the processor 110, the machine-readable storage medium 120, and the transceiver 140 are connected through the bus 130, and the processor 110 may be configured to control the transceiving action of the transceiver 140, so as to perform data transceiving with the aforementioned blockchain link point terminal 200.
For a specific implementation process of the processor 110, reference may be made to the various method embodiments executed by the block chain service system 100, which have similar implementation principles and technical effects, and further description of the embodiments is omitted here.
In addition, the embodiment of the disclosure also provides a readable storage medium, where the readable storage medium is preset with a computer execution instruction, and when a processor executes the computer execution instruction, the above information generation method based on the big block chain security data is implemented.
Finally, it should be understood that the examples in this specification are only intended to illustrate the principles of the examples in this specification. Other variations are also possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims (9)

1. An information generation method based on block chain security big data is applied to a block chain service system, wherein the block chain service system is in communication connection with a plurality of block chain node terminals, and the method comprises the following steps:
acquiring block chain security big data generated by the block chain link point terminal in a payment on a block chain line, wherein the block chain security big data is used for representing a payment security verification event between block chain payment services, and the block chain payment services are payment services related to a target security protection operation program;
generating a blockchain payment service sequence based on the blockchain security big data, wherein the blockchain payment service sequence is a sequence formed by a plurality of blockchain payment services with payment security verification events in the blockchain security big data;
determining payment risk behavior information corresponding to the blockchain payment service in the blockchain security big data according to the blockchain payment service sequence, and determining a first risk behavior association degree between safety protection operation units on the target safety protection operation program according to risk behavior association degree between the payment risk behavior information corresponding to the blockchain payment service in the blockchain security big data and mapping information between the blockchain payment service and the safety protection operation units on the target safety protection operation program;
updating protection association rules of a safety protection operation unit on the target safety protection operation program based on the first risk behavior association degree, collecting key payment safety data based on the updated protection association rules of the safety protection operation unit, and generating a safety portrait of the block link point terminal for the key payment safety data based on the safety protection operation rules corresponding to the safety protection operation program;
wherein, the step of collecting the key payment safety data based on the updated protection association rule of the safety protection operation unit comprises the following steps:
performing safety protection attribute feature extraction based on cross-attribute on the updated protection association rule of the safety protection operation unit to obtain a plurality of safety protection attribute features combined with protection attribute information;
based on any one of the safety protection attribute features, updating the protection association rules of the target safety protection operation units with different protection attribute information based on an anti-fraud feature to obtain protection association rules of a plurality of first safety protection operation units corresponding to the safety protection attribute features;
updating the protection association rules of the target safety protection operation unit based on the anti-phishing characteristics to obtain protection association rules of a plurality of second safety protection operation units corresponding to the protection association rules of the safety protection operation unit;
fusing the protection association rules of the safety protection operation units with the protection association rules of the first safety protection operation units and the protection association rules of the second safety protection operation units respectively to obtain the protection association rules of the matched target safety protection operation units;
and taking the key payment safety data collection characteristic corresponding to the protection association rule of the target safety protection operation unit as a key payment safety data collection characteristic used for responding to the protection association rule of the safety protection operation unit, and collecting key payment safety data based on the key payment safety data collection characteristic.
2. The method for generating information based on big block chain security data according to claim 1, wherein the method further comprises:
determining a second risk behavior association degree between safety protection operation units on the target safety protection operation program according to a protection association rule of the safety protection operation units running on the target safety protection operation program;
updating the protection association rule of the safety protection operation unit on the target safety protection operation program based on the first risk behavior association degree, including:
and updating the protection association rule of the safety protection operation unit on the target safety protection operation program based on the first risk behavior association degree and the second risk behavior association degree.
3. The method as claimed in claim 1, wherein the determining a first risk behavior association degree between security protection operating units on the target security protection operating program according to the risk behavior association degree between payment risk behavior information corresponding to the blockchain payment service in the blockchain security big data and mapping information between the blockchain payment service and the security protection operating units on the target security protection operating program includes:
determining first risk information corresponding to a first risk behavior association degree according to payment risk behavior information corresponding to each block chain payment service in the block chain security big data; each risk node in the first risk information corresponding to the first risk behavior association degree is used for representing the risk behavior association degree between the block chain payment service corresponding to the first risk dimension where the risk node is located and the block chain payment service corresponding to the second risk dimension where the risk node is located;
converting first risk information corresponding to the first risk behavior association degree into second risk information corresponding to a second risk behavior association degree according to mapping information between each block chain payment service and each safety protection operation unit on the target safety protection operation program; each risk node in the second risk information corresponding to the second risk behavior association degree is used for representing the risk behavior association degree between the safety protection operation unit corresponding to the first risk dimension where the risk node is located and the safety protection operation unit corresponding to the second risk dimension where the risk node is located;
updating the protection association rule of the safety protection operation unit on the target safety protection operation program based on the first risk behavior association degree, including:
determining risk information for updating the protection association rule of the safety protection operation unit according to the second risk information corresponding to the second risk behavior association degree and the risk information of the protection association rule of the starting safety protection operation unit; the risk information of the protection association rule of the starting safety protection operation unit is constructed according to the protection association rule of the safety protection operation unit which is running on the target safety protection operation program; each risk node in the risk information of the protection association rule of the starting safety protection operation unit and the risk information of the protection association rule of the updating safety protection operation unit is used for representing the influence weight of a risk subject behavior corresponding to a first risk dimension where the risk node is located on a safety protection operation unit corresponding to a second risk dimension where the risk node is located;
wherein the method further comprises:
training a target characteristic vector based on the risk information of the protection association rule of the starting safety protection operation unit, and taking the target characteristic vector as third risk information corresponding to a third risk behavior association degree; each risk node in the third risk information corresponding to the third risk behavior association degree is used for representing the risk behavior association degree between the safety protection operation unit corresponding to the first risk dimension where the risk node is located and the safety protection operation unit corresponding to the second risk dimension where the risk node is located;
determining the risk information of updating the protection association rule of the safety protection operation unit according to the second risk information corresponding to the second risk behavior association degree and the risk information of the protection association rule of the starting safety protection operation unit, including:
fusing second risk information corresponding to the second risk behavior association degree and third risk information corresponding to the third risk behavior association degree to obtain target risk information;
determining risk information of the protection association rule of the updated safety protection operation unit according to the target risk information and the risk information of the protection association rule of the initial safety protection operation unit;
judging whether a risk node in the dimension in the risk information of the protection association rule of the initial safety protection operation unit is larger than a first target value or not aiming at a risk node in the same dimension in the risk information of the protection association rule of the updated safety protection operation unit and the risk information of the protection association rule of the initial safety protection operation unit, and if so, setting the risk node in the dimension in the risk information of the protection association rule of the updated safety protection operation unit to be 0; and
and judging whether the risk node is smaller than or equal to a second target value or not aiming at each risk node in the risk information of the protection association rule of the updated safety protection operation unit, and if so, setting the risk node in the risk information of the protection association rule of the updated safety protection operation unit to be 0.
4. The method for generating information based on blockchain security big data according to claim 1, wherein the determining payment risk behavior information corresponding to the blockchain payment service in the blockchain security big data according to the sequence of blockchain payment services includes:
performing unique hot code feature extraction on each block chain payment service in the block chain security big data to obtain unique hot code feature information corresponding to each block chain payment service;
training a payment risk behavior extraction network based on the unique hot code characteristic information corresponding to the block chain payment service in the block chain payment service sequence, and adjusting the payment risk behavior prediction information of the block chain payment service in the training process;
and taking the payment risk behavior prediction information of the block chain payment service after the training of the payment risk behavior extraction network is finished as the payment risk behavior information corresponding to the block chain payment service.
5. The method for generating information based on blockchain security big data according to claim 1, wherein the generating a blockchain payment service sequence based on the blockchain security big data includes at least one of:
generating the block chain payment service sequence based on the block chain payment service subscribed with the payment security verification event in the block chain security big data through a random walking algorithm;
and generating the block chain payment service sequence based on the block chain payment service belonging to the same subscription attribute in the block chain security big data through the irregular walking algorithm.
6. The method for generating information based on blockchain security big data according to any one of claims 1 to 5, wherein obtaining the blockchain security big data generated by paying the blockchain link point terminal on a blockchain line comprises:
selecting a payment service meeting a preset condition from the initial payment service data as the blockchain payment service; the preset condition comprises at least one of the following conditions: the payment service type is a preset type, and the online rate of the payment service exceeds a preset online rate threshold value;
and determining the big blockchain security data according to a payment security verification event of the blockchain payment service in the initial payment service data, wherein the payment security verification event comprises a payment security verification event of completing switching or updating a payment verification process.
7. The method for generating big block chain security data-based information according to claim 1, wherein the artificial intelligence learning model for key payment security data gathering comprises a plurality of model branch structures;
the performing, based on the cross-attribute, security protection attribute feature extraction on the updated protection association rule of the security protection operation unit to obtain a plurality of security protection attribute features combined with protection attribute information, includes:
performing safety protection attribute feature extraction on the protection association rule of the safety protection operation unit through any one of the model branch structures based on the protection attribute different from the protection association rule of the safety protection operation unit to obtain the safety protection attribute feature corresponding to the model branch structure;
taking a plurality of safety protection attribute features corresponding to the model branch structure as the plurality of safety protection attribute features;
the artificial intelligence learning model for collecting the key payment safety data comprises a characteristic connection branch structure;
wherein the method further comprises:
performing safety protection attribute feature extraction on the protection association rule of a preset safety protection operation unit through any one of the model branch structures based on the protection attribute different from the protection association rule of the preset safety protection operation unit to obtain the safety protection attribute feature corresponding to the model branch structure;
performing characteristic connection on the safety protection attribute characteristics corresponding to the model branch structure through the characteristic connection branch structure to obtain cross-attribute safety protection characteristics corresponding to the protection association rule of the preset safety protection operation unit;
constructing a model evaluation parameter of the artificial intelligence learning model based on the cross-attribute safety protection feature and a cross-attribute safety protection feature label of a protection association rule of the preset safety protection operation unit, updating the parameter of the artificial intelligence learning model until the model evaluation parameter is converged, and taking the updated parameter of the artificial intelligence learning model when the model evaluation parameter is converged as the parameter of the trained artificial intelligence learning model;
performing attribute prediction on safety protection attribute features corresponding to the model branch structure to obtain prediction attributes of protection association rules corresponding to the preset safety protection operation unit, and constructing category model evaluation parameters of the artificial intelligence learning model based on the prediction attributes and the preset attributes of the protection association rules of the preset safety protection operation unit;
the constructing of the model evaluation parameters of the artificial intelligence learning model based on the cross-attribute safety protection feature and the cross-attribute safety protection feature label of the protection association rule of the preset safety protection operation unit includes:
constructing a cross-attribute model evaluation parameter of the artificial intelligence learning model based on the cross-attribute safety protection feature and a cross-attribute safety protection feature label of a protection association rule of the preset safety protection operation unit;
and carrying out weighted summation on the category model evaluation parameters and the cross-attribute model evaluation parameters to obtain model evaluation parameters of the artificial intelligence learning model.
8. The method for generating information based on big block chain security data according to claim 1, wherein the performing an anti-fraud feature-based update process on the protection association rules of the target security protection operating units with different protection attribute information to obtain the protection association rules of the plurality of first security protection operating units corresponding to the security attribute features includes:
performing the following processing for any one of the plurality of safeguard attribute features of the combined safeguard attribute information: determining anti-fraud characteristic parameters between the safety protection attribute characteristics and the safety protection attribute characteristics of the protection association rules of the target safety protection operation units with different protection attribute information respectively;
sequencing the protection association rules of the target safety protection operation unit according to a priority order based on an anti-fraud feature parameter between the safety protection attribute feature and the safety protection attribute feature of the protection association rule of the target safety protection operation unit, and taking the protection association rules of the first N target safety protection operation units sequenced in the sequencing information as the protection association rules of the plurality of first safety protection operation units; wherein N is a natural number;
the updating process based on the anti-phishing feature is performed on the protection association rule of the target safety protection operation unit to obtain the protection association rules of a plurality of second safety protection operation units corresponding to the protection association rules of the safety protection operation unit, and the updating process comprises the following steps: matching an overall protection association item in the protection association rules of the safety protection operation unit with the protection association rules of the target safety protection operation unit to obtain the utilization rate of the overall protection association item in the protection association rules of the target safety protection operation unit, determining the proportion of the same association rules between the protection association rules of the safety protection operation unit and the protection association rules of the target safety protection operation unit based on the utilization rate of the overall protection association item in the protection association rules of the target safety protection operation unit, and sequencing the protection association rules of the target safety protection operation unit according to the priority order based on the proportion of the same association rules between the protection association rules of the safety protection operation unit and the protection association rules of the target safety protection operation unit, taking the protection association rules of the top M target safety protection operation units sequenced in the sequencing information as the protection association rules of the plurality of second safety protection operation units; wherein M is a natural number.
9. A blockchain service system, comprising a processor, a machine-readable storage medium, and a network interface, wherein the machine-readable storage medium, the network interface, and the processor are connected through a bus system, the network interface is configured to be communicatively connected to at least one blockchain link point terminal, the machine-readable storage medium is configured to store a program, an instruction, or a code, and the processor is configured to execute the program, the instruction, or the code in the machine-readable storage medium to perform the information generation method based on blockchain security big data according to any one of claims 1 to 8.
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