CN110335144B - Personal electronic bank account security detection method and device - Google Patents
Personal electronic bank account security detection method and device Download PDFInfo
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Abstract
The application provides a method and a device for detecting the safety of a personal electronic bank account, wherein the method comprises the following steps: acquiring data of a target personal electronic bank account and preprocessing the acquired data to obtain a characteristic value corresponding to the data; and inputting the characteristic value into a preset safety evaluation model, and taking the output of the safety evaluation model as a safety detection result of the target personal electronic bank account, wherein the safety evaluation model is a prediction model obtained by applying personal information, bank card medium information, account setting information, channel information and login equipment information training. According to the method and the system, the safety of the electronic bank account of the customer is effectively evaluated, the safety risk is prompted to the customer, the customer is guided to adopt a safer using mode, and then the safety of the personal electronic bank account is improved.
Description
Technical Field
The invention relates to the technical field of personal electronic banks, in particular to a method and a device for detecting the safety of a personal electronic bank account.
Background
With the development of the internet, many financial institutions such as commercial banks provide electronic banking services to customers using the internet. After the customer signs up with the bank, the electronic bank account can be used for managing own funds. Electronic banking accounts face more complex use environments and security risks than physical accounts.
The safety of electronic bank accounts is a basic requirement for customers to use electronic banking services, although many measures are taken to ensure the safety of electronic banking services, such as: the identity authentication of the client is strengthened, and the identity of the client is prevented from being stolen; a multi-dimensional authority switch is provided for a client, so that the client can conveniently and safely set according to the self condition; and performing environmental security check and the like before the customer logs in the electronic bank. While the bank provides a plurality of safety measures, the events of theft of the electronic bank account of the customer, theft of funds and information leakage still occur at times. Through event analysis, the security of the electronic bank account is not only related to the security of a bank system, but also inseparable from the way that a customer uses the electronic bank.
There is therefore a need for a way to detect the security of a personal electronic banking account from the way a customer uses electronic banking.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method and a device for detecting the safety of a personal electronic bank account, which are beneficial to a client to visually know the safety of the electronic bank account and guide the client to use a safe mode, thereby improving the safety of the personal electronic bank account.
In order to solve the technical problems, the invention provides the following technical scheme:
in a first aspect, the present invention provides a method for detecting security of a personal electronic bank account, including:
acquiring data of a target personal electronic bank account and preprocessing the acquired data to obtain a characteristic value corresponding to the data;
and inputting the characteristic value into a preset safety evaluation model, and taking the output of the safety evaluation model as a safety detection result of the target personal electronic bank account, wherein the safety evaluation model is a prediction model obtained by applying personal information, bank card medium information, account setting information, channel information and login equipment information training.
The preprocessing the acquired data to obtain a characteristic value corresponding to the data includes:
screening the acquired data to obtain screened data;
and carrying out normalization processing on the screening data to obtain a characteristic value corresponding to the screening data.
The screening processing of the acquired data to obtain screened data includes:
and deleting part of the acquired data which meets the preset conditions to obtain screening data.
Further, the method also comprises the following steps:
acquiring historical data of a plurality of personal electronic bank accounts, wherein the historical data comprises the personal information, bank card medium information, account setting information, channel information and login equipment information;
and based on the XGboost algorithm, the historical data is applied to train a security evaluation model.
Further, before the XGBoost algorithm is used to train a security assessment model by applying the historical data, the method further includes:
performing data cleaning and data annotation on the historical data;
performing feature extraction on the historical data subjected to data cleaning and data labeling to obtain corresponding historical feature data;
correspondingly, the training of the safety assessment model by applying the historical data comprises:
and training the safety assessment model by applying the historical characteristic data.
Further, before the applying the historical feature data to train the security assessment model, the method further includes:
dividing the historical feature data into a training set and a test set;
correspondingly, the training of the safety assessment model by applying the historical feature data comprises:
and applying the training set to train the safety assessment model.
Further, after the applying the training set to train the security assessment model, the method further includes:
and testing the safety evaluation model obtained by current training by applying the test set, and adjusting the safety evaluation model according to the test result.
Wherein the data of the target personal electronic banking account comprises: personal information, bank card information, account setting information, channel information, and login device information.
In a second aspect, the present invention provides a personal electronic bank account security detection apparatus, including:
the characteristic unit is used for acquiring data of a target personal electronic bank account and preprocessing the acquired data to obtain a characteristic value corresponding to the data;
and the detection unit is used for inputting the characteristic value into a preset safety evaluation model and taking the output of the safety evaluation model as a safety detection result of the target personal electronic bank account, wherein the safety evaluation model is a prediction model obtained by applying personal information, bank card medium information, account setting information, channel information and login equipment information training.
Wherein the feature unit includes:
the screening subunit is used for screening the acquired data to obtain screened data;
and the processing subunit is used for carrying out normalization processing on the screening data to obtain a characteristic value corresponding to the screening data.
Wherein the screening subunit comprises:
and the deleting module is used for deleting part of the acquired data which meets the preset conditions to obtain the screening data.
Further, the method also comprises the following steps:
the system comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is used for acquiring historical data of a plurality of personal electronic bank accounts, and the historical data comprises the personal information, the bank card medium information, the account setting information, the channel information and the login equipment information;
and the training unit is used for applying the historical data to train a security evaluation model based on the XGboost algorithm.
Further, the method also comprises the following steps:
the marking unit is used for carrying out data cleaning and data marking on the historical data;
the extraction unit is used for extracting the characteristics of the historical data subjected to data cleaning and data labeling to obtain corresponding historical characteristic data;
correspondingly, the training unit comprises:
and the training subunit is used for applying the historical characteristic data to train the safety assessment model.
Further, the method also comprises the following steps:
the dividing subunit is used for dividing the historical characteristic data into a training set and a test set;
correspondingly, the training subunit includes:
and the training module is used for applying the training set to train the safety assessment model.
Further, the method also comprises the following steps:
and the test module is used for testing the safety evaluation model obtained by current training by applying the test set and adjusting the safety evaluation model according to the test result.
Wherein the data of the target personal electronic banking account comprises: personal information, bank card information, account setting information, channel information, and login device information.
In a third aspect, the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the steps of the method for detecting security of an electronic personal bank account.
In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the method for detecting the security of a personal electronic banking account.
According to the technical scheme, the invention provides the personal electronic bank account security detection method and the device, and the characteristic value corresponding to the data is obtained by acquiring the data of the target personal electronic bank account and preprocessing the acquired data; inputting the characteristic value into a preset safety evaluation model, and taking the output of the safety evaluation model as a safety detection result of a target personal electronic bank account, wherein the safety evaluation model is a prediction model obtained by applying personal information, bank card medium information, account setting information, channel information and login equipment information training, so that the safety of the customer electronic bank account is effectively evaluated, safety risks are prompted to the customer, the customer is guided to adopt a safer use mode, account and fund safety is guaranteed, and further the safety of the personal electronic bank account is improved. And for the customers with lower account security, the security risk of the customer account of the electronic bank is analyzed and monitored through the bank, so that the accounts are prevented from being utilized by attackers.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for detecting security of a personal electronic bank account according to an embodiment of the present invention.
Fig. 2 is another schematic flow chart of the method for detecting security of an electronic personal bank account according to the embodiment of the present invention.
Fig. 3 is another schematic flow chart of a method for detecting security of a personal electronic banking account according to an embodiment of the present invention.
Fig. 4 is a flowchart of the security assessment model training and tuning stage in the method for detecting security of an electronic bank account according to the embodiment of the present invention.
Fig. 5 is a schematic structural diagram of a personal electronic bank account security detection apparatus according to an embodiment of the present invention.
Fig. 6 is a schematic structural diagram of a second structure of a personal electronic bank account security detection apparatus according to an embodiment of the present invention.
Fig. 7 is a schematic structural diagram of an electronic device in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
The invention provides a personal electronic bank account security detection method, referring to fig. 1, the personal electronic bank account security detection method specifically comprises the following contents:
s101: acquiring data of a target personal electronic bank account and preprocessing the acquired data to obtain a characteristic value corresponding to the data;
in this step, the data of the target individual electronic banking account is information about the individual and the account, which is handled or retained by the target individual in the electronic banking, and includes: personal information, bank card information, account setting information, channel information, and login device information.
The data sources of the data of the target personal electronic bank account are a bank data warehouse, a historical information storage system and databases of all related application systems.
It is understood that the personal information includes: customer basic information, certificate information and contact information. The bank card information includes: the information of the type of the bank card, the password intensity information of the bank card, the latest using time information of the bank card and the temporary loss reporting information. The account setting information includes: the system comprises external transfer authority setting information, electronic commerce authority setting information, payment and payment authority setting information, financing transaction authentication authority setting information, balance change reminding setting information, login reminding setting information, secret-free transaction setting information, transaction limit setting information, online off-line cardless payment authority setting information, online in-line transaction region authority setting information, offline off-line transaction country/region authority setting information, offline transaction time authority setting information, electronic bank safety medium type information and electronic bank password intensity information. The channel information includes: the system comprises electronic bank channel use information, counter and artificial channel use information, self-service channel use information, partner channel use information and third-party quick payment channel use information. The login device information includes: the system comprises equipment number information, equipment hardware information, bank application version information, running program information, network connection information, operating system permission information, operating system version information, key path file information and browser plug-in information.
Further, preprocessing the acquired data to obtain a characteristic value corresponding to the data, including:
and screening the acquired data to obtain screening data, and normalizing the screening data to obtain a characteristic value corresponding to the screening data.
The acquired data is screened to obtain screened data, and specifically, part of the acquired data which meets a preset condition is deleted to obtain the screened data.
The preset condition can be set by the user according to the screening requirement, and in the embodiment of the city, the preset condition is that the data value in the acquired data of the target personal electronic bank account is null or data which does not accord with the business logic.
Specifically, the data value in the acquired data of the target personal electronic bank account is null or the data which does not accord with the business logic is deleted.
The method comprises the steps of obtaining a characteristic value corresponding to screening data by normalizing the screening data, and specifically, normalizing the obtained personal information, bank card information, account setting information, channel information and login equipment information.
Referring to the detailed index table shown in table 1, normalization processing is performed on the acquired personal information, bank card information, account setting information, channel information, and login device information to obtain a feature value corresponding to the screening data.
TABLE 1 detailed index Table
And processing the personal information to obtain the basic information missing condition of the client, the expiration of the client certificate and the mobile phone number index verified by the bank. Wherein, the client basic information missing condition = number of missing client basic information items/total number of client basic information.
And processing the information of the bank card to obtain the indexes of the condition of the non-chip card of the bank card, the condition that the password of the bank card is weak, the condition that the bank card is not moved for a long time and the condition that the bank card is temporarily lost. Wherein, the situation of the bank card non-chip cards = the number of the client bank card non-chip cards/the total number of the client bank cards; the condition that the bank card password is weak password = the customer bank card password is weak password quantity/customer bank card total quantity; long-term standing bank card condition = number of long-term standing bank cards/total number of customer bank cards; the temporary loss report bank card condition = the number of the temporary loss report bank cards of the client/the total number of the bank cards of the client; the bank card password is judged to be a weak password according to any one of the following rules: the length is less than 6, 6 bits of same number, 6 bits of increasing number and 6 bits of decreasing number; the long-term immobile account bank card refers to a bank card which has no payment and receipt activities and is not owed to the bank debt of the account opening.
Processing the electronic bank account setting information to obtain whether to activate an external transfer function, whether to activate an electronic commerce function, whether to activate a payment function, whether to activate a financing transaction authentication function, whether to activate a balance change reminding function, whether to activate a secret-free transaction function, whether to set a transaction limit, whether to set an online off-line cardless payment authority, whether to set an online in-line transaction area authority, whether to set an offline off-line transaction country/area authority, whether to set an offline transaction time authority, whether an electronic bank password is a weak password, whether to activate an electronic bank login reminding, and whether to use a new intelligent password key index.
The channel information is processed to obtain the day of the last use of the electronic bank channel, the day of the last use of the counter and the manual channel, the day of the last use of the self-service channel, the day of the last use of the partner channel, and the day of the last use of the third-party quick payment channel. The method for calculating the days of the last-time use of electronic banks/counters and manual/self-help/cooperation/third-party quick payment channels comprises the following steps: day date-the number of days of the natural day that the customer last used the channel service date.
Processing the information of the login device to obtain whether the user has bound the device, whether the client application is updated to the latest version, whether the security software is operated, whether the debugging software is operated, whether the network agent is operated, whether the operation system is connected to a risk network, whether the operation system has super administrator authority, whether the operation system has a major security hole, whether a simulator is used, and whether an anti-phishing control index is installed.
S102: and inputting the characteristic value into a preset safety evaluation model, and taking the output of the safety evaluation model as a safety detection result of the target personal electronic bank account, wherein the safety evaluation model is a prediction model obtained by applying personal information, bank card medium information, account setting information, channel information and login equipment information training.
In this step, the characteristic value obtained in step S101 is used as an input of a security evaluation model, and the security of the user account is evaluated according to an output result of the security evaluation model (i.e., the probability that the customer is classified into risk categories), so as to provide a security detection service for the customer, provide a batch statistic function for the bank, and provide data support for the wind control system.
As can be seen from the above description, in the method for detecting the security of the personal electronic bank account provided in the embodiment of the present invention, the characteristic value corresponding to the data is obtained by acquiring the data of the target personal electronic bank account and preprocessing the acquired data; and inputting the characteristic value into a preset security evaluation model, and taking the output of the security evaluation model as a security detection result of a target personal electronic bank account, wherein the security evaluation model is a prediction model obtained by training application personal information, bank card medium information, account setting information, channel information and login equipment information, so that the security of the electronic bank account of a customer is effectively evaluated, security risks are prompted to the customer, the customer is guided to adopt a safer use mode, the security of the account and funds is guaranteed, and the security of the personal electronic bank account is further improved. And for the customers with lower account security, the security risk of the customer account of the electronic bank is analyzed and monitored through the bank, so that the account is prevented from being utilized by an attacker.
On the basis of the above embodiment, referring to fig. 2, the embodiment of the method for detecting the security of the personal electronic banking account further includes:
s10: acquiring historical data of a plurality of personal electronic bank accounts, wherein the historical data comprises the personal information, bank card medium information, account setting information, channel information and login equipment information;
s30: and based on the XGboost algorithm, the historical data is applied to train a security evaluation model.
In the embodiment, historical data of a plurality of personal electronic bank accounts are obtained, training is carried out based on an XGboost algorithm and application historical data to obtain a security evaluation model, the XGboost algorithm can utilize multiple threads of a processor to realize parallel processing, hadoop realization is supported, and the method is suitable for large-data-volume analysis. The safety of the user account is evaluated according to the safety evaluation model output result (namely the probability that the customer is classified into the risk classification), the defect that the bank generally does not pay attention to the participation of the customer to jointly improve the safety of the electronic bank account is overcome, the customer can visually know the safety of the electronic bank account, the customer is guided to change an unsafe use mode, and the bank can analyze and monitor the safety risk of the customer account of the full-scale electronic bank.
Further, referring to fig. 3, in this embodiment, the method further includes:
s20: performing data cleaning and data annotation on the historical data;
s40: performing feature extraction on the historical data subjected to data cleaning and data labeling to obtain corresponding historical feature data;
correspondingly, the training of the safety assessment model by applying the historical data comprises:
s50: and training the safety evaluation model by applying the historical characteristic data.
In the embodiment, the effectiveness of the historical data can be improved by performing data cleaning and data labeling on the historical data of the obtained multiple personal electronic bank accounts, and the historical characteristic data is obtained by performing characteristic extraction after the data cleaning and the data labeling. The historical characteristic data is used for training the safety assessment model, so that the performance of the safety assessment model can be improved, and the precision of the safety assessment model is further improved.
It should be noted that, in the data cleansing, data with a data value of null or not in accordance with business logic in the acquired historical data of the multiple personal electronic bank accounts is deleted. Data annotation is the labeling of historical data for model training and validation. Marking the clients who have the risk events as the clients with the safety risk in the account as 0; and marking the customers without risk events as the customers with safer accounts, as 1.
Further, before the historical feature data is applied to train the safety assessment model, the experienced historical feature data is divided into a training set and a test set; correspondingly, when the historical characteristic data is used for training a safety assessment model, the training set is used for training the safety assessment model. And verifying the safety evaluation model by applying the test set, testing the currently trained safety evaluation model by applying the test set, and adjusting the safety evaluation model according to the test result.
As can be seen from the above description, the embodiment of the present invention includes a method for generating a security evaluation model and a method for detecting security of an account of an electronic bank using the security evaluation model. The security evaluation model is trained and the weight value is adjusted through the XGboost algorithm, meanwhile, the security of the account is evaluated by utilizing the prejudgment result of the model, the security of the electronic bank account is jointly improved by a bank and a client, the client can visually know the security of the electronic bank account, the client is guided to change the unsafe use mode, the security of the electronic bank account is further improved, and the analysis and monitoring of the security risk of the client account of the full-scale electronic bank by the bank are facilitated.
To describe the method for training the security assessment model by using the XGBoost algorithm in more detail, this embodiment provides a specific training method, referring to fig. 4, which specifically includes:
step S301: and dividing a training set and a testing set. And dividing the marked data set into a training set and a testing set through a random division function, wherein the number ratio of samples in the training set to the testing set is selected as 7. If the data set is small, an N-fold cross-validation method can be adopted.
Step S302: and setting a hyper-parameter. The hyper-parameter is an initial value set according to an empirical value, and is not a parameter obtained through training;
step S303: the "learning rate (eta)" is determined from the empirical value.
Step S304: fixing learning rate, adjusting the value of the number of 'best trees' (nround), and determining nround according to the value of AUC (Area under the future of ROC) under different nround selections; and adjusting the obtained optimal learning rate according to the CV function, and determining the depth of the optimal tree according to cross validation.
It should be noted that XGBoost has a very useful function called "CV" that performs cross validation at each enhancement iteration to return the optimal number of trees required.
Step S305: and performing grid search (grid search), further determining a parameter combination of the maximum depth (max _ depth) and the minimum node weight (min _ child _ weight) of each tree, and traversing the combination to obtain an optimal parameter combination.
Step S306: and determining a Gamma value, wherein the Gamma represents a minimum loss function reduction value required by node splitting, and the algorithm is more conservative when the value is larger, so that overfitting is avoided.
Step S307: the grid search determines the sizes of a sample subset (subsample) and a feature subset (colsample _ byte) of each tree training;
step S308: determining an L1 regularization term alpha and an L2 regularization term lambda of each tree by grid search;
step S309: and judging whether the value of the AUC reaches the expected target.
Step S310: and if the expected target is achieved, taking the adjusted parameters as the final optimal parameter combination.
Step S311: if the expected target is not met, a smaller learning rate is reset, and the optimal parameter combination is further obtained.
The embodiment of the present invention provides a specific implementation manner of a personal electronic bank account security detection device capable of implementing all contents in the personal electronic bank account security detection method, and referring to fig. 5, the personal electronic bank account security detection device specifically includes the following contents:
the characteristic unit 10 is used for acquiring data of a target personal electronic bank account and preprocessing the acquired data to obtain a characteristic value corresponding to the data;
the detection unit 20 is configured to input the feature value into a preset security assessment model, and use an output of the security assessment model as a security detection result of the target personal electronic bank account, where the security assessment model is a prediction model obtained by applying personal information, bank card media information, account setting information, channel information, and login device information training.
Wherein the feature unit 10 includes:
a screening subunit 101, configured to perform screening processing on the acquired data to obtain screened data;
and the processing subunit 102 is configured to perform normalization processing on the screening data to obtain a feature value corresponding to the screening data.
Wherein the screening subunit comprises:
and the deleting module is used for deleting part of the acquired data which meet the preset conditions to obtain the screening data.
Further, referring to fig. 6, the personal electronic bank account security detection apparatus specifically further includes:
the acquiring unit 30 is configured to acquire historical data of a plurality of personal electronic bank accounts, where the historical data includes the personal information, the bank card media information, the account setting information, the channel information, and the login device information;
and the training unit 60 is configured to apply the historical data to train a security assessment model based on the XGBoost algorithm.
Further, the method also comprises the following steps:
the labeling unit 40 is used for performing data cleaning and data labeling on the historical data;
the extraction unit 50 is used for performing feature extraction on the historical data subjected to data cleaning and data labeling to obtain corresponding historical feature data;
correspondingly, the training unit comprises:
and the training subunit is used for applying the historical characteristic data to train the safety assessment model.
Further, the method also comprises the following steps:
the dividing subunit is used for dividing the historical characteristic data into a training set and a test set;
correspondingly, the training subunit comprises:
and the training module is used for applying the training set to train the safety assessment model.
Further, the method also comprises the following steps:
and the test module is used for testing the safety evaluation model obtained by current training by applying the test set and adjusting the safety evaluation model according to the test result.
Wherein the data of the target personal electronic banking account comprises: personal information, bank card information, account setting information, channel information, and login device information.
The embodiment of the personal electronic bank account security detection apparatus provided by the present invention may be specifically used for executing the processing flow of the embodiment of the personal electronic bank account security detection method in the above embodiment, and the functions thereof are not described herein again, and reference may be made to the detailed description of the embodiment of the method.
As can be seen from the above description, in the device for detecting the security of the personal electronic bank account provided in the embodiment of the present invention, the characteristic value corresponding to the data is obtained by acquiring the data of the target personal electronic bank account and preprocessing the acquired data; inputting the characteristic value into a preset safety evaluation model, and taking the output of the safety evaluation model as a safety detection result of a target personal electronic bank account, wherein the safety evaluation model is a prediction model obtained by applying personal information, bank card medium information, account setting information, channel information and login equipment information training, so that the safety of the customer electronic bank account is effectively evaluated, safety risks are prompted to the customer, the customer is guided to adopt a safer use mode, account and fund safety is guaranteed, and further the safety of the personal electronic bank account is improved. And for the customers with lower account security, the security risk of the customer account of the electronic bank is analyzed and monitored through the bank, so that the accounts are prevented from being utilized by attackers.
The embodiment of the present invention further provides a specific implementation manner of an electronic device, which is capable of implementing all steps in the method for detecting security of an account of a personal electronic bank in the foregoing embodiment, and referring to fig. 7, the electronic device specifically includes the following contents:
a processor (processor) 601, a memory (memory) 602, a communication Interface (Communications Interface) 603, and a bus 604;
the processor 601, the memory 602 and the communication interface 603 complete mutual communication through the bus 604; the processor 601 is configured to call a computer program in the memory 602, and the processor executes the computer program to implement all the steps in the method for detecting security of a personal electronic banking account in the foregoing embodiments, for example, when executing the computer program, the processor implements the following steps: acquiring data of a target personal electronic bank account and preprocessing the acquired data to obtain a characteristic value corresponding to the data; and inputting the characteristic value into a preset security evaluation model, and taking the output of the security evaluation model as a security detection result of the target personal electronic bank account, wherein the security evaluation model is a prediction model obtained by applying personal information, bank card medium information, account setting information, channel information and login equipment information training.
An embodiment of the present invention further provides a computer-readable storage medium capable of implementing all the steps in the personal electronic bank account security detection method in the foregoing embodiment, where the computer-readable storage medium stores a computer program, and the computer program, when executed by a processor, implements all the steps of the personal electronic bank account security detection method in the foregoing embodiment, for example, when the processor executes the computer program, implements the following steps: acquiring data of a target personal electronic bank account and preprocessing the acquired data to obtain a characteristic value corresponding to the data; and inputting the characteristic value into a preset safety evaluation model, and taking the output of the safety evaluation model as a safety detection result of the target personal electronic bank account, wherein the safety evaluation model is a prediction model obtained by applying personal information, bank card medium information, account setting information, channel information and login equipment information training.
Although the present invention provides method steps as described in the examples or flowcharts, additional or fewer steps may be included based on routine or non-inventive practice. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or client product executes, it may execute sequentially or in parallel (e.g., in the context of parallel processors or multi-threaded processing) according to the embodiments or methods shown in the figures.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the embodiments described herein may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. The terms "upper", "lower", and the like, indicate orientations or positional relationships that are based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and to simplify the description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention. Unless expressly stated or limited otherwise, the terms "mounted," "connected," and "connected" are intended to be inclusive and mean, for example, that they may be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the description of the present invention, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description. Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention. It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention is not limited to any single aspect or embodiment, nor is it limited to any single embodiment, nor to any combination and/or permutation of such aspects and/or embodiments. Moreover, each aspect and/or embodiment of the present invention may be utilized alone or in combination with one or more other aspects and/or embodiments thereof.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (16)
1. A personal electronic bank account security detection method is characterized by comprising the following steps:
acquiring data of a target personal electronic bank account and preprocessing the acquired data to obtain a characteristic value corresponding to the data; the data comprises personal information, bank card information, account setting information, channel information and login equipment information which are retained by a target individual in the business transacted by an electronic bank; the personal information comprises client basic information, certificate information and contact information; the bank card information comprises bank card type information, bank card password intensity information, bank card recent use time information and temporary loss reporting information; the account setting information comprises external transfer account authority setting information, electronic commerce authority setting information, payment authority setting information, financial transaction authentication authority setting information, balance change reminding setting information, login reminding setting information, password-free transaction setting information, transaction limit setting information, online and offline cardless payment authority setting information, online and offline transaction region authority setting information, offline and offline transaction country/region authority setting information, offline transaction time authority setting information, electronic bank security medium type information and electronic bank password intensity information; the channel information includes: the system comprises electronic bank channel use information, counter and artificial channel use information, self-service channel use information, partner channel use information and third-party quick payment channel use information; the login device information includes: equipment number information, equipment hardware information, bank application version information, running program information, network connection information, operating system permission information, operating system version information, key path file information and browser plug-in information;
inputting the characteristic value into a preset safety evaluation model, and taking the output of the safety evaluation model as a safety detection result of a target personal electronic bank account, wherein the safety evaluation model is a prediction model obtained by applying personal information, bank card medium information, account setting information, channel information and login equipment information training;
wherein the step of training the security assessment model comprises:
acquiring historical data of a plurality of personal electronic bank accounts, wherein the historical data comprises the personal information, bank card medium information, account setting information, channel information and login equipment information;
and based on the XGboost algorithm, the historical data is applied to train a security evaluation model.
2. The method for detecting the security of the personal electronic banking account according to claim 1, wherein the preprocessing the acquired data to obtain the feature value corresponding to the data includes:
screening the acquired data to obtain screened data;
and carrying out normalization processing on the screening data to obtain a characteristic value corresponding to the screening data.
3. The method for detecting the security of the personal electronic banking account according to claim 2, wherein the screening the acquired data to obtain screened data includes:
and deleting part of the acquired data which meets the preset conditions to obtain screening data.
4. The method for detecting the security of the personal electronic banking account according to claim 1, wherein before the XGBoost algorithm is applied to train a security assessment model, the method further comprises:
performing data cleaning and data annotation on the historical data;
performing feature extraction on the historical data subjected to data cleaning and data labeling to obtain corresponding historical feature data;
correspondingly, the training of the safety assessment model by applying the historical data comprises:
and training the safety evaluation model by applying the historical characteristic data.
5. The method for detecting the security of the personal electronic banking account according to the claim 4, further comprising, before the applying the historical feature data to train the security evaluation model:
dividing the historical feature data into a training set and a test set;
correspondingly, the training of the safety assessment model by applying the historical feature data comprises:
and applying the training set to train the safety assessment model.
6. The method for detecting the security of the personal electronic banking account according to the claim 5, further comprising, after the applying the training set to train the security evaluation model:
and testing the safety evaluation model obtained by current training by applying the test set, and adjusting the safety evaluation model according to the test result.
7. The method for detecting the security of the personal electronic bank account according to claim 1, wherein the data of the target personal electronic bank account comprises: personal information, bank card information, account setting information, channel information, and login device information.
8. A personal electronic bank account security detection device is characterized by comprising:
the characteristic unit is used for acquiring data of a target personal electronic bank account and preprocessing the acquired data to obtain a characteristic value corresponding to the data; the data comprises personal information, bank card information, account setting information, channel information and login equipment information which are kept by a target person when transacting business in an electronic bank; the personal information comprises client basic information, certificate information and contact information; the information of the bank card comprises information of the type of the bank card, the password intensity information of the bank card, the latest using time information of the bank card and temporary loss reporting information; the account setting information comprises external transfer authority setting information, electronic commerce authority setting information, payment authority setting information, financing transaction authentication authority setting information, balance change reminding setting information, login reminding setting information, secret-free transaction setting information, transaction limit setting information, offline card-free payment authority setting information, offline internal transaction regional authority setting information, offline external transaction country/regional authority setting information, offline transaction time authority setting information, electronic bank safety medium type information and electronic bank password intensity information; the channel information includes: the method comprises the following steps of (1) using information of an electronic bank channel, using information of a counter and a manual channel, using information of a self-service channel, using information of a partner channel and using information of a third-party quick payment channel; the login device information includes: equipment number information, equipment hardware information, bank application version information, running program information, network connection information, operating system permission information, operating system version information, key path file information and browser plug-in information;
the detection unit is used for inputting the characteristic value into a preset safety evaluation model and taking the output of the safety evaluation model as a safety detection result of a target personal electronic bank account, wherein the safety evaluation model is a prediction model obtained by applying personal information, bank card medium information, account setting information, channel information and login equipment information training;
the system comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is used for acquiring historical data of a plurality of personal electronic bank accounts, and the historical data comprises the personal information, the bank card medium information, the account setting information, the channel information and the login equipment information;
and the training unit is used for applying the historical data to train a security evaluation model based on the XGboost algorithm.
9. The personal electronic banking account security detecting device according to claim 8, wherein the characteristic unit includes:
the screening subunit is used for screening the acquired data to obtain screened data;
and the processing subunit is used for carrying out normalization processing on the screening data to obtain a characteristic value corresponding to the screening data.
10. The apparatus for detecting security of a personal electronic banking account as claimed in claim 9, wherein the screening subunit comprises:
and the deleting module is used for deleting part of the acquired data which meets the preset conditions to obtain the screening data.
11. The personal electronic banking account security detecting device according to claim 8, further comprising:
the marking unit is used for carrying out data cleaning and data marking on the historical data;
the extraction unit is used for extracting the characteristics of the historical data subjected to data cleaning and data labeling to obtain corresponding historical characteristic data;
correspondingly, the training unit comprises:
and the training subunit is used for applying the historical characteristic data to train the safety assessment model.
12. The personal electronic banking account security detecting device according to claim 11, further comprising:
the dividing subunit is used for dividing the historical characteristic data into a training set and a test set;
correspondingly, the training subunit includes:
and the training module is used for applying the training set to train the safety assessment model.
13. The personal electronic banking account security detection device of claim 12, further comprising:
and the test module is used for testing the safety evaluation model obtained by current training by applying the test set and adjusting the safety evaluation model according to the test result.
14. The apparatus for detecting security of personal electronic banking account according to claim 8, wherein the data of the target personal electronic banking account includes: personal information, bank card information, account setting information, channel information, and login device information.
15. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method for detecting the security of a personal electronic banking account according to any one of claims 1 to 7 when executing the program.
16. A computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method for detecting security of a personal electronic banking account as claimed in one of the claims 1 to 7.
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