CN110362981A - The method and system of abnormal behaviour are judged based on credible equipment fingerprint - Google Patents
The method and system of abnormal behaviour are judged based on credible equipment fingerprint Download PDFInfo
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Abstract
The invention discloses a kind of method and system that abnormal behaviour is judged based on credible equipment fingerprint.Method includes obtaining user's operation behavior property data；Based on the user's operation behavior property data, judgment models operation is belonged to by user equipment, obtains the credible equipment fingerprint for corresponding to the user；If find that user equipment carries out specific operation by other users by user's credible equipment fingerprint, the prompt of abnormal behaviour is issued.The present invention belongs to model according to user's operation behavior property data by user equipment, obtain corresponding to the credible equipment fingerprint of the user, so as to the behavior of noting abnormalities, if using obtained user equipment ownership result as variable be input to it is other instead cheat in models, can auxiliary judgment client risk of fraud.
Description
Technical field
The present invention relates to computer fields, especially design it is a kind of based on credible equipment fingerprint judge abnormal behaviour method and
System.
Background technique
With flourishing for the internet consumer finance, people more and more use line upper mounting plate to carry out business application,
Efficiently operation and simple process so that the mobile devices such as cell phone/PAD as main application tool become user with
Important medium between platform, user and user.Risk of fraud is one of the principal risk that internet financial industry faces, and is letter
Borrow the important link of risk management.
These mobile devices and the one-to-one relationship of user are always the difficult point of anti-fraud analysis, and some users are by making
With other people equipment, reduce the acquisition of information, increase risk of fraud, it is therefore necessary to according to user's operation behavior property, really
The credible equipment of user is made, and according to the anti-application cheated of the credible equipment result.
Above- mentioned information are only used for reinforcing the understanding to the background of the disclosure, therefore it disclosed in the background technology part
It may include the information not constituted to the prior art known to persons of ordinary skill in the art.
Summary of the invention
In view of the above problems, it proposes description of the invention and overcomes the above problem or at least partly in order to provide one kind
It solves the above problems.
Other characteristics and advantages disclosed in description of the invention will be apparent from by the following detailed description, or partly
Pass through the practice acquistion of the disclosure.
2, in a first aspect, present specification discloses the methods for judging abnormal behaviour based on credible equipment fingerprint, including,
Obtain user's operation behavior property data；
Based on the user's operation behavior property data, judgment models operation is belonged to by user equipment, obtains corresponding be somebody's turn to do
The credible equipment fingerprint of user；
If find that user equipment carries out specific operation by other users by user's credible equipment fingerprint, abnormal row is issued
For prompt.
It is described to be based on the user's operation behavior property data in a kind of exemplary embodiment of the disclosure, pass through use
Family equipment belongs to judgment models operation, and the credible equipment fingerprint for obtaining corresponding to the user further comprises,
The credible equipment fingerprint is stored in the network of personal connections information of the corresponding user.
It is described to be based on the user's operation behavior property data in a kind of exemplary embodiment of the disclosure, pass through use
Family equipment belongs to judgment models operation, and the credible equipment fingerprint for obtaining corresponding to the user further comprises,
Use user information as the input variable of the anti-fraud model of debt-credit in real time in the credible equipment fingerprint and equipment.
In a kind of exemplary embodiment of the disclosure, if described find user equipment quilt by user's credible equipment fingerprint
When other users carry out specific operation, the prompt for issuing abnormal behaviour further comprises,
If the specific operation is related to debt-credit, the identity information of the user using the equipment is further verified.
In a kind of exemplary embodiment of the disclosure, the credible equipment is defined as the commonly used equipment of user, and this sets
It is standby not occur abnormal behaviour.
In a kind of exemplary embodiment of the disclosure, the user's operation behavior property data are based on, are set by user
Standby ownership judgment models operation, the method for obtaining the credible equipment fingerprint for corresponding to the user include,
Based on user's operation behavior property data, constructs user equipment and belong to judgment models；
Obtain the user equipment ownership judgment models required input variable；The input variable includes user's operation row
For；
Operation is carried out using user equipment ownership judgment models, obtains user equipment ownership result.
In a kind of exemplary embodiment of the disclosure, the building user equipment belongs to judgment models and includes,
According to user's operation behavior property data, training is used in such a way that supervised learning and semi-supervised learning combine
Family equipment belongs to judgment models.
In a kind of exemplary embodiment of the disclosure, the building user equipment belongs to judgment models and further comprises,
Obtain user's operation sample data；
The user's operation sample data, which is divided into first, exemplar and unlabeled exemplars；
There is exemplar to do supervised learning to described first, obtains the first judgment models based on user's operation behavior；
Semi-supervised learning is carried out to unlabeled exemplars using the first judgment models, obtaining second has exemplar；
There is exemplar and described second to there is exemplar to carry out supervised learning to described first, obtains grasping based on user
Make the user equipment ownership judgment models of behavior.
In a kind of exemplary embodiment of the disclosure, the acquisition user's operation sample data further comprises,
The user's operation sample data includes that operation behavior, equipment attribute itself and equipment of the user in equipment are closed
Join information.
It is described the user's operation sample data is divided into first to have label in a kind of exemplary embodiment of the disclosure
Sample and unlabeled exemplars further comprise,
The artificial mark of user's operation and equipment ownership judgement is carried out to a part of the user's operation sample data of acquisition
There is exemplar as first, remainder is unlabeled exemplars.
It is described to carry out artificial mark and include in a kind of exemplary embodiment of the disclosure,
According to user's operation behavior property, the simultaneously mark of attaching relation one by one of artificial judgment equipment and user.
It is described to there is exemplar to do supervised learning to described first in a kind of exemplary embodiment of the disclosure, it obtains
Further comprise to the first judgment models based on user's operation behavior,
Have carrying out described the first of artificial mark based on attaching relation of the user's operation behavior property to equipment and user
Exemplar is equally divided into two parts, and a copy of it is modeled to obtain and be grasped based on user for doing supervised learning by cross validation
Make the first judgment models of behavior.
It is described to there is exemplar and described second to have label to described first in a kind of exemplary embodiment of the disclosure
Sample carries out supervised learning, obtains the user equipment ownership judgment models based on user's operation behavior and further comprises,
There is exemplar remaining a for described first and described second has exemplar progress supervised learning.
It is described that half prison is carried out to unlabeled exemplars using the first judgment models in a kind of exemplary embodiment of the disclosure
Educational inspector practises, and obtaining second has the exemplar to further comprise,
Unlabeled exemplars are judged using the first judgment models based on user's operation behavior；
Using judging result unlabeled exemplars are carried out with the mark of user's operation and equipment ownership, obtaining second has label sample
This.
It is described that user's operation is carried out to unlabeled exemplars using judging result in a kind of exemplary embodiment of the disclosure
With the mark of equipment ownership, obtaining second has the exemplar to further comprise,
Given threshold；
The label that judging result is greater than threshold value is set as 1, it is on the contrary then label is set as 0.
It is described to obtain the user equipment ownership judgment models required input in a kind of exemplary embodiment of the disclosure
Variable further comprises,
Operation behavior attribute, equipment attribute itself and equipment related information of the user in equipment.
It is described to obtain the user equipment ownership prediction model required input in a kind of exemplary embodiment of the disclosure
Variable further includes,
Variable is derived, the derivative includes that automatic variable is derivative and manual variable is derivative.
Second aspect, present specification discloses the systems that abnormal behaviour is judged based on credible equipment fingerprint, including,
User's registration information obtains module, for obtaining user's operation behavior property data；
User equipment belongs to judgment module, for being based on the user's operation behavior property data, is returned by user equipment
Belong to judgment models operation, obtains the credible equipment fingerprint for corresponding to the user；
Analysis and processing module, if specific for being carried out by user's credible equipment fingerprint discovery user equipment by other users
When operation, the prompt of abnormal behaviour is issued.
In a kind of exemplary embodiment of the disclosure, the user equipment belongs to judgment module and further comprises,
Memory module, for the credible equipment fingerprint to be stored in the network of personal connections information of the corresponding user.
In a kind of exemplary embodiment of the disclosure, the user equipment belongs to judgment module and further comprises,
Variable input module, for using user information to take advantage of as debt-credit is counter in real time in the credible equipment fingerprint and equipment
Cheat the input variable of model.
In a kind of exemplary embodiment of the disclosure, the analysis and processing module further comprises,
Identity information validating module is further verified and is set using this if be related to debt-credit for the specific operation
The identity information of standby user.
In a kind of exemplary embodiment of the disclosure, it includes definition module that user equipment, which belongs to judgment models module, is used
In device definition that is user is common and not occurring abnormal behaviour be credible equipment.
In a kind of exemplary embodiment of the disclosure, equipment belongs to judgment models module, is based on user's operation behavior category
Property data, for construct user equipment ownership judgment models；
Input variable obtains module, for obtaining the user equipment ownership judgment models required input variable；It is described defeated
Entering variable includes user's operation behavior；
Computing module obtains user equipment ownership and sentences for carrying out operation using user equipment ownership judgment models
Disconnected result.
In a kind of exemplary embodiment of the disclosure, the equipment belongs to judgment models module and includes,
Unit, for mutually being tied with semi-supervised learning using supervised learning according to user's operation behavior property data
Mode training user's equipment of conjunction belongs to judgment models.
In a kind of exemplary embodiment of the disclosure, the user equipment belongs to judgment models module and further comprises,
User's operation sample data acquiring unit, for obtaining user's operation sample data；
Taxon has exemplar and unlabeled exemplars for the user's operation sample data to be divided into first；
First judgment models unit obtains grasping based on user for having exemplar to do supervised learning to described first
Make the first judgment models of behavior；
Semi-supervised learning unit obtains for carrying out semi-supervised learning to unlabeled exemplars using the first judgment models
Two have exemplar；
Supervised learning unit, for thering is exemplar and described second to there is exemplar to carry out supervision to described first
Study obtains the user equipment ownership judgment models based on user's operation behavior.
In a kind of exemplary embodiment of the disclosure, the user's operation sample data acquiring unit further comprises,
Operation behavior subelement, for obtaining operation behavior of the user in equipment；
Device attribute subelement, for obtaining equipment attribute itself；
Equipment related information subelement, for obtaining equipment related information.
In a kind of exemplary embodiment of the disclosure, the taxon further comprises,
Artificial mark subelement, a part for the user's operation sample data to acquisition carry out user's operation and equipment
The artificial mark of ownership judgement has exemplar as first, and remainder is unlabeled exemplars.
In a kind of exemplary embodiment of the disclosure, the artificial mark subelement includes,
According to user's operation behavior property, the simultaneously mark of attaching relation one by one of artificial judgment equipment and user.
In a kind of exemplary embodiment of the disclosure, the first judgment models unit further comprises,
The equal molecular cell of exemplar, for will based on user's operation behavior property to the attaching relation of equipment and user into
Described the first of pedestrian's work mark has exemplar to be equally divided into two parts, and a copy of it passes through intersection for doing supervised learning
Verifying modeling obtains the first judgment models based on user's operation behavior.
In a kind of exemplary embodiment of the disclosure, the user equipment belongs to judgment models and further comprises,
There is exemplar remaining a for described first and described second has exemplar progress supervised learning.
In a kind of exemplary embodiment of the disclosure, the semi-supervised learning unit further comprises,
Judgment sub-unit, for using the first judgment models based on user's operation behavior to sentence unlabeled exemplars
It is disconnected；
Mark subelement, for using judging result that unlabeled exemplars are carried out with the mark of user's operation and equipment ownership,
Obtaining second has exemplar.
In a kind of exemplary embodiment of the disclosure, the mark subelement further comprises,
Given threshold；
The label that judging result is greater than threshold value is set as 1, it is on the contrary then label is set as 0.
In a kind of exemplary embodiment of the disclosure, the input variable obtains module and further comprises,
Operation behavior attribute, equipment attribute itself and equipment related information of the user in equipment.
In a kind of exemplary embodiment of the disclosure,
The input variable obtains module,
Variable derived units, for deriving to variable, the derivative includes that automatic variable is derivative and manual variable is spread out
It is raw.
The third aspect, description of the invention provide a kind of server, including processor and memory: the memory is used for
Store the program of any of the above-described the method；The processor is configured to for executing the program stored in the memory
The step of realizing any of the above-described the method.
Fourth aspect, description of the invention embodiment provide a kind of computer readable storage medium, are stored thereon with calculating
Machine program, when which is executed by processor the step of realization any of the above-described the method.
The present invention has the effect of positive: the present invention belongs to model by user equipment and judges credible equipment, will obtain
User equipment ownership result as variable be input to it is other instead cheat in models, can auxiliary judgment client risk of fraud.
The result of judgement can also be used to improve the network of personal connections information of user；Or it is used for the user when borrowing money and does not use my equipment
When, reinforce core body strategy；And borrow money the stage anti-fraud model in, input client whether be the loaning bill equipment owner change
Amount, can aided assessment user risk of fraud.
It should be understood that the above general description and the following detailed description are merely exemplary, this can not be limited
It is open.
Detailed description of the invention
To describe the technical solutions in the embodiments of the present invention more clearly, make required in being described below to embodiment
Attached drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for
For those of ordinary skill in the art, without creative efforts, it can also be obtained according to these attached drawings other
Attached drawing.
Fig. 1 is the process of the method shown according to an exemplary embodiment that abnormal behaviour is judged based on credible equipment fingerprint
Figure.
Fig. 2 is the schematic diagram of supervised learning in the training of user equipment judgment models in method shown in Fig. 1.
Fig. 3 is the frame of model in method shown in Fig. 1.
Fig. 4 is a kind of device that abnormal behaviour is judged based on credible equipment fingerprint shown according to another exemplary embodiment
Block diagram.
Fig. 5 is a kind of block diagram of server shown according to an exemplary embodiment.
Specific embodiment
The above description is only an overview of the technical scheme of the present invention, in order to better understand the technical means of the present invention,
And it can be implemented in accordance with the contents of the specification, and in order to allow above and other objects of the present invention, feature and advantage can
It is clearer and more comprehensible, the followings are specific embodiments of the present invention.
However, following example embodiments can be implemented in a variety of forms, and it is not understood as limited to set forth herein
Embodiment；On the contrary, thesing embodiments are provided so that the disclosure will be full and complete, and the design of example embodiment is comprehensively passed
Up to those skilled in the art.Identical appended drawing reference indicates same or similar part in figure, thus will omit to it
Repeated description.
In addition, described feature, structure or characteristic can be incorporated in one or more implementations in any suitable manner
In example.In the following description, many details are provided to provide and fully understand to embodiment of the disclosure.However,
It will be appreciated by persons skilled in the art that can with technical solution of the disclosure without one or more in specific detail,
Or it can be using other methods, constituent element, device, step etc..In other cases, it is not shown in detail or describes known side
Method, device, realization or operation are to avoid fuzzy all aspects of this disclosure.
Block diagram shown in the drawings is only functional entity, not necessarily must be corresponding with physically separate entity.
I.e., it is possible to realize these functional entitys using software form, or realized in one or more hardware modules or integrated circuit
These functional entitys, or these functional entitys are realized in heterogeneous networks and/or processor device and/or microcontroller device.
Flow chart shown in the drawings is merely illustrative, it is not necessary to including all content and operation/step,
It is not required to execute by described sequence.For example, some operation/steps can also decompose, and some operation/steps can close
And or part merge, therefore the sequence actually executed is possible to change according to the actual situation.
It should be understood that although herein various assemblies may be described using term first, second, third, etc., these groups
Part should not be limited by these terms.These terms are to distinguish a component and another component.Therefore, first group be discussed herein below
Part can be described as the second component without departing from the teaching of disclosure concept.As used herein, term " and/or " include associated
All combinations for listing any of project and one or more.
It will be understood by those skilled in the art that attached drawing is the schematic diagram of example embodiment, module or process in attached drawing
Necessary to not necessarily implementing the disclosure, therefore it cannot be used for the protection scope of the limitation disclosure.
The present invention provides a kind of methods that abnormal behaviour is judged based on credible equipment fingerprint, for solving in the prior art
It can not judge credible equipment and the problem of according to judging result antifraud, to solve the above-mentioned problems, general thought of the invention
It is as follows:
The method of abnormal behaviour is judged based on credible equipment fingerprint, including,
Obtain user's operation behavior property data；
Based on the user's operation behavior property data, judgment models operation is belonged to by user equipment, obtains corresponding be somebody's turn to do
The credible equipment fingerprint of user；
If find that user equipment carries out specific operation by other users by user's credible equipment fingerprint, abnormal row is issued
For prompt.
The present invention belongs to model according to user's operation behavior property data by user equipment, and obtain corresponding to the user can
Device-fingerprint is believed, so as to the behavior of noting abnormalities, if being input to other using obtained user equipment ownership result as variable
Anti- fraud model in, can auxiliary judgment client risk of fraud.
Firstly the need of specification, in each embodiment of the present invention, related term are as follows:
The terms "and/or", only a kind of incidence relation for describing affiliated partner, indicates that there may be three kinds of passes
System, for example, A and/or B, can indicate: individualism A exists simultaneously A and B, these three situations of individualism B.In addition, herein
Middle character "/" typicallys represent the relationship that forward-backward correlation object is a kind of "or".
In the following, technical solution of the present invention is described in detail and is illustrated by several specific embodiments.
See Fig. 1, the method for abnormal behaviour is judged based on credible equipment fingerprint, including,
S101 obtains user's operation behavior property data；The ownership judgement of user's operation behavior property data history user equipment
The input variable of model uses behavior of distinct device and the attribute of equipment itself etc. including user.
S102 is based on the user's operation behavior property data, belongs to judgment models operation by user equipment, obtains pair Should user credible equipment fingerprint；Wherein, the credible equipment is defined as the commonly used equipment of user, and the equipment did not occurred
Abnormal behaviour.
One of application about obtained credible equipment fingerprint are as follows: the credible equipment fingerprint is stored in the corresponding user
Network of personal connections information in, for improving the network of personal connections information of user, so as to have apparent and comprehensive understanding to user.
About obtained credible equipment fingerprint using it two are as follows: by the credible equipment fingerprint and equipment in real time using using
Input variable of the family information as the anti-fraud model of debt-credit, it is possible thereby to the risk of fraud of aided assessment user.
In this step, the building of user equipment ownership judgment models is particularly important.The building of model is illustrated in detail below
Thinking:
Firstly, being based on user's operation behavior property data, building user equipment belongs to judgment models；
In this step, it according to user's operation behavior property data, is combined using supervised learning and semi-supervised learning
Mode training user's equipment ownership judges mould.It specifically can be by the way of following:
(a) user's operation sample data is obtained；The selection of user's operation sample data mainly determines according to user's operation,
Operation behavior, equipment attribute itself and equipment related information including user in equipment.
It is that it leads equipment to be used which equipment is the difference of operation behavior of the user in equipment, which can reflect out, i.e., I
The user attaching to be found equipment.Such as loaning bill behavior occurs for the first time for the operation order of user on different devices, user
Equipment be more likely its commonly used equipment；The number of operations of user on different devices is also more important variable, such as
The number that user logs on different devices, borrows money, if be the most equipment etc. of number of operations.
Equipment attribute itself refers to: user is using may set device name to oneself name or close when equipment
Claim, also can be used as the feature for judge equipment ownership, the similarity of device name and name is higher, is more likely to be the ownership user
Equipment.
Equipment related information can reflect out the relationship of association user, if such as two equipment network of personal connections association very phase
Seemingly, then this two equipment are possible to own for same user.
(b) the user's operation sample data is divided into first has exemplar and unlabeled exemplars；Specific way is:
The artificial mark of user's operation and equipment ownership judgement is carried out as first to a part of the user's operation sample data of acquisition
There is exemplar, remainder is unlabeled exemplars.Wherein, the mode of artificial mark are as follows: according to user's operation behavior property, people
Work judges the attaching relation one by one of equipment and user and mark.
(c) there is exemplar to do supervised learning to described first, obtain first based on user's operation behavior and judge mould
Type；Specifically, exemplar is equally divided into two parts by described the first of front artificial mark, a copy of it is for doing
Supervised learning models to obtain the first judgment models based on user's operation behavior by cross validation.
(d) semi-supervised learning is carried out to unlabeled exemplars using the first judgment models, obtaining second has exemplar；Specifically
It include: that unlabeled exemplars are judged using the first judgment models based on user's operation behavior；Using judging result to nothing
Exemplar carries out the mark of user's operation and equipment ownership, beats calibration method are as follows: given threshold；Judging result is greater than threshold value
Label be set as 1, it is on the contrary then label is set as 0, so that obtaining second has exemplar.
(e) there is exemplar remaining a for described first and described second have exemplar progress supervised learning,
Obtain the user equipment ownership judgment models based on user's operation behavior.
Secondly, obtaining the user equipment ownership judgment models required input variable；The input variable is grasped comprising user
Make behavior, refers specifically to operation behavior attribute, equipment attribute itself and equipment related information of the user in equipment.In addition to obtaining
Except these basic underlying variables, variable derivative is the important link of modeling, and deriving more more useful variables facilitates Lifting Modules
The effect of type.Deriving method mainly includes automatic derivatization method and two kinds of manual deriving method.Automatic variable deriving method includes
One discrete variable can be split as the process of multiple two classified variables by unique code；Also using some automatic variables
Derivative tool (such as Featuretools, Polynomialfeatures etc.) generates the new variables etc. that cross term is multiplied.Hand
Method derived from dynamic variable mainly excavates new useful variable according to business experience, for example, in equipment the guest operation time row
Whether sequence variable uses register etc. when equipment for the first time.
Finally, carrying out operation using user equipment ownership judgment models, user equipment ownership result is obtained.
The model algorithm used combines for supervised learning with semi-supervised learning, is changed by the stacking of several layer models
In generation, can make the accuracy of analysis constantly be promoted.
If S103 is issued find that user equipment carries out specific operation by other users by user's credible equipment fingerprint The prompt of abnormal behaviour.If the specific operation is related to debt-credit, the identity of the user using the equipment is further verified
Information.
As shown in Fig. 2, illustrating the mistake of supervised learning training pattern in user equipment ownership judgment models building process
Journey.User equipment ownership judgment models mainly use the method that supervised learning and semi-supervised learning combine, and collect first
The sample of a part of artificial mark does supervised learning, i.e., the attaching relation one by one of artificial judgment equipment and user is (according to various letters
Breath, whom the user of this equipment of artificial judgment is) and mark as dependent variable, first do supervised learning with this part of sample.
5 foldings of the sample of the artificial mark in this part point are done cross validation to model to form Model1, then carries out prediction with Model1 and judges, and
The result of Model1 judgement is inputted into Model2, and so on, generate supervised learning part.
The frame of user equipment ownership judgment models is constructed as shown in figure 3, utilizing the sample A of the artificial mark of a part
It trains supervised learning model (referred to as Model I), trained supervised learning model M odel I prediction does not have for utilization
There is the sample B of artificial mark, sets a threshold value (such as 0.8), the label by prediction probability greater than 0.8 is set as 1, otherwise is 0,
It is that unlabeled exemplars B labels with there is monitor model.Then new with remaining a part of sample A and sample B training one
Supervised learning model (according to the method described above, referred to as Model II), forms final prediction model, that is, has new user/set
When standby relationship, by variable input prediction model prediction.
For a kind of application scenarios (borrowing money safely, lightning to account) of the result of the present embodiment, user first use cell-phone number into
Row registration, in this registration process, system will obtain the operation behavior attribute of user, can carry out the school of credible equipment
It tests, the amount application of next step is entered if it can pass through verifying, if abnormality warnings cannot be issued by verifying, request
Further verify identity.Subsequent amount application then includes recognition of face, real-name authentication, bank card authentication, cell-phone number owner's body
Part verification and other increasing letter certifications.Then enter to borrow money after passing through and submit step, includes payment cipher verification, fund side
Match, anti-fraudulent trading detecting, account will be arrived by making loans for rear fund side.Subsequent refund processing is also very convenient, and client is also
Money --- fund side withholds --- fund side keeps accounts --- amount real-time recovery.
Second embodiment of the invention also discloses the system that abnormal behaviour is judged based on credible equipment fingerprint, as shown in figure 4,
Including,
User's registration information obtains module 401, for obtaining user's operation behavior property data；
User equipment belongs to judgment module 402, for being based on the user's operation behavior property data, passes through user equipment
Belong to judgment models operation, obtains the credible equipment fingerprint for corresponding to the user；
Analysis and processing module 403, if for finding that user equipment is carried out by other users by user's credible equipment fingerprint
When specific operation, the prompt of abnormal behaviour is issued.
Wherein, user equipment ownership judgment module further comprises,
Memory module, for the credible equipment fingerprint to be stored in the network of personal connections information of the corresponding user.
Variable input module, for using user information to take advantage of as debt-credit is counter in real time in the credible equipment fingerprint and equipment
Cheat the input variable of model.
Definition module was credible equipment for device definition that is user is common and not occurring abnormal behaviour.
Wherein, the analysis and processing module further comprises identity information validating module, if being for the specific operation
When related to debt-credit, the identity information of the user using the equipment is further verified.
It is specifically described the user equipment ownership judgment module based on user's operation behavior below, including；
Equipment belongs to judgment models module, is based on user's operation behavior property data, sentences for constructing user equipment ownership
Disconnected model；It specifically includes unit that equipment, which belongs to judgment models module, for using according to user's operation behavior property data
Mode training user's equipment that supervised learning and semi-supervised learning combine belongs to judgment models.
Furthermore, further includes: user's operation sample data acquiring unit, for obtaining user's operation sample data；According to
Family operation behavior attribute, user's sample data acquiring unit have comprising operation behavior subelement, for obtaining user in equipment
On operation behavior；Device attribute subelement, for obtaining equipment attribute itself；Equipment related information subelement, for obtaining
Equipment related information.
Taxon has exemplar and unlabeled exemplars for the user's operation sample data to be divided into first；Point
Class unit specifically includes artificial mark subelement, and a part for the user's operation sample data to acquisition carries out user's operation
Artificial mark with equipment ownership judgement has exemplar as first, and remainder is unlabeled exemplars.Artificial mark is single
Member includes, according to user's operation behavior property, the simultaneously mark of attaching relation one by one of artificial judgment equipment and user.
First judgment models unit obtains grasping based on user for having exemplar to do supervised learning to described first
Make the first judgment models of behavior；First judgment models unit further comprises the equal molecular cell of exemplar, for that will be based on
User's operation behavior property has exemplar average mark to the attaching relation of equipment and user carries out artificial mark described first
It is two parts, a copy of it models to obtain first sentencing based on user's operation behavior for doing supervised learning, by cross validation
Disconnected model.
Semi-supervised learning unit obtains for carrying out semi-supervised learning to unlabeled exemplars using the first judgment models
Two have exemplar；Semi-supervised learning unit further comprises judgment sub-unit, for using based on user's operation behavior
One judgment models judge unlabeled exemplars；Mark subelement, for being used using judging result unlabeled exemplars
Mark (the given threshold of family operation and equipment ownership；The label that judging result is greater than threshold value is set as 1, it is on the contrary then set label
There is exemplar 0), to obtain second.
Supervised learning unit, for thering is exemplar and described second to there is exemplar to carry out supervision to described first
Study obtains the user equipment ownership judgment models based on user's operation behavior.Specifically, being to have label sample for described first
This is remaining a and described second has exemplar to carry out supervised learning.
Input variable obtains module, for obtaining the user equipment ownership judgment models required input variable；It is described defeated
Entering variable includes user's operation behavior；Input variable obtains module, operation behavior attribute of the user in equipment,
Equipment attribute itself and equipment related information.In addition, it further includes variable derived units that input variable, which obtains module, for change
Amount is derived, and the derivative includes that automatic variable is derivative and manual variable is derivative
Computing module obtains user equipment ownership and sentences for carrying out operation using user equipment ownership judgment models
Disconnected result.
In the present embodiment, user equipment ownership judgment models mainly use supervised learning and mutually tie with semi-supervised learning
The method of conjunction, the sample for collecting the artificial mark of a part first do supervised learning, i.e. artificial judgment equipment and user is returned one by one
Simultaneously mark is as dependent variable for category relationship (according to various information, whom user of this equipment of artificial judgment is), first with this part
Sample do supervised learning.5 foldings of the sample of the artificial mark in this part point are done cross validation to model to form the first judgment models, then
Prediction is carried out with the first judgment models to judge, and the result of the first judgment models unit judges input user equipment ownership is judged
Model, and so on, generate supervised learning part.That is it is trained using the sample A of the artificial mark of a part
Supervised learning model (the first judgment models), trained first judgment models predict the sample of no artificial mark for utilization
This B labels for unlabeled exemplars B.Then with remaining a part of sample A and sample B one new supervised learning of training
Model forms final user equipment ownership judgment models.
This specification 3rd embodiment additionally provides a kind of server, including memory, processor and is stored in memory
Computer program that is upper and can running on a processor, the processor realize the step of method described above when executing described program
Suddenly.For ease of description, part relevant to this specification embodiment is illustrated only, it is disclosed by specific technical details, it please join
According to this specification embodiment method part.The server can be the server apparatus formed including various electronic equipments, PC electricity
(Personal Digital Assistant, individual digital help by brain, network Cloud Server or even mobile phone, tablet computer, PDA
Reason), POS (Point of Sales, point-of-sale terminal), vehicle-mounted computer, the service being arranged on any electronic equipment such as desktop computer
Device function.
Specifically, the server composed structure frame relevant to the technical solution of this specification embodiment offer shown in Fig. 5
Figure, it will include one represented by processor 501 or more that bus 500, which may include the bus and bridge of any number of interconnection,
The various circuits for the memory that a processor and memory 502 represent link together.Bus 500 can also such as will be set periphery
Various other circuits of standby, voltage-stablizer and management circuit or the like link together, and these are all it is known in the art,
Therefore, it will not be further described herein.Bus interface 503 bus 500 and receiver and/or transmitter 504 it
Between interface is provided, receiver and/or transmitter 504 can be separately independent receiver or transmitter and be also possible to the same member
Part such as transceiver, provides the unit for communicating over a transmission medium with various other devices.Processor 501 is responsible for management bus
500 and common processing, and memory 502 can be used for the used data when executing operation of storage processor 501.
Through the above description of the embodiments, those skilled in the art is it can be readily appreciated that example described herein is implemented
Mode can also be realized by software realization in such a way that software is in conjunction with necessary hardware.Therefore, according to the disclosure
The technical solution of embodiment can be embodied in the form of software products, which can store in a computer
In readable storage medium storing program for executing (can be CD-ROM, USB flash disk, mobile hard disk etc.) or on network, including some instructions are so that a meter
Equipment (can be personal computer, server or network equipment etc.) execution is calculated according to the above-mentioned side of disclosure embodiment
Method.
The computer readable storage medium may include in a base band or the data as the propagation of carrier wave a part are believed
Number, wherein carrying readable program code.The data-signal of this propagation can take various forms, including but not limited to electromagnetism
Signal, optical signal or above-mentioned any appropriate combination.Readable storage medium storing program for executing can also be any other than readable storage medium storing program for executing
Readable medium, the readable medium can send, propagate or transmit for by instruction execution system, device or device use or
Person's program in connection.The program code for including on readable storage medium storing program for executing can transmit with any suitable medium, packet
Include but be not limited to wireless, wired, optical cable, RF etc. or above-mentioned any appropriate combination.
Can with any combination of one or more programming languages come write for execute the disclosure operation program
Code, described program design language include object oriented program language-Java, C++ etc., further include conventional
Procedural programming language-such as " C " language or similar programming language.Program code can be fully in user
It calculates and executes in equipment, partly executes on a user device, being executed as an independent software package, partially in user's calculating
Upper side point is executed on a remote computing or is executed in remote computing device or server completely.It is being related to far
Journey calculates in the situation of equipment, and remote computing device can pass through the network of any kind, including local area network (LAN) or wide area network
(WAN), it is connected to user calculating equipment, or, it may be connected to external computing device (such as utilize ISP
To be connected by internet).
Above-mentioned computer-readable medium carries one or more program, when said one or multiple programs are by one
When the equipment executes, so that the computer-readable medium implements function such as: obtaining history credit data set, the history credit
Data set includes the multidimensional attribute information and overdue information of user；Based on the multidimensional attribute information to the history credit
Data set carries out automatic stepping processing to generate multiple Sub Data Sets；The multiple Sub Data Set is calculated according to the overdue information
In the corresponding overdue rate of each Sub Data Set；And credit risk is generated according to Sub Data Set and its corresponding overdue rate
Control rule.
It will be appreciated by those skilled in the art that above-mentioned each module can be distributed in device according to the description of embodiment, it can also
Uniquely it is different from one or more devices of the present embodiment with carrying out corresponding change.The module of above-described embodiment can be merged into
One module, can also be further split into multiple submodule.
By the description of above embodiment, those skilled in the art is it can be readily appreciated that example embodiment described herein
It can also be realized in such a way that software is in conjunction with necessary hardware by software realization.Therefore, implemented according to the disclosure
The technical solution of example can be embodied in the form of software products, which can store in a non-volatile memories
In medium (can be CD-ROM, USB flash disk, mobile hard disk etc.) or on network, including some instructions are so that a calculating equipment (can
To be personal computer, server, mobile terminal or network equipment etc.) it executes according to the method for the embodiment of the present disclosure.
Although the preferred embodiment of this specification has been described, once a person skilled in the art knows basic wounds
The property made concept, then additional changes and modifications may be made to these embodiments.So the following claims are intended to be interpreted as includes
Preferred embodiment and all change and modification for falling into this specification range.
It is particularly shown and described the exemplary embodiment of the disclosure above.It should be appreciated that the present disclosure is not limited to
Detailed construction, set-up mode or implementation method described herein；On the contrary, disclosure intention covers included in appended claims
Various modifications and equivalence setting in spirit and scope.
In addition, structure shown by this specification Figure of description, ratio, size etc., only to cooperate specification institute
Disclosure, for skilled in the art realises that be not limited to the enforceable qualifications of the disclosure with reading, therefore
Do not have technical essential meaning, the modification of any structure, the change of proportionate relationship or the adjustment of size are not influencing the disclosure
Under the technical effect and achieved purpose that can be generated, it should all still fall in technology contents disclosed in the disclosure and obtain and can cover
In the range of.Meanwhile cited such as "upper" in this specification, " first ", " second " and " one " term, be also only and be convenient for
Narration is illustrated, rather than to limit the enforceable range of the disclosure, relativeness is altered or modified, without substantive change
Under technology contents, when being also considered as the enforceable scope of the disclosure.
Claims (10)
1. the method for abnormal behaviour is judged based on credible equipment fingerprint, including,
Obtain user's operation behavior property data；
Based on the user's operation behavior property data, judgment models operation is belonged to by user equipment, obtains corresponding to the user
Credible equipment fingerprint；
If find that user equipment carries out specific operation by other users by user's credible equipment fingerprint, abnormal behaviour is issued
Prompt.
2. the method according to claim 1 for judging abnormal behaviour based on credible equipment fingerprint, wherein
It is described to be based on the user's operation behavior property data, judgment models operation is belonged to by user equipment, obtains corresponding be somebody's turn to do
The credible equipment fingerprint of user further comprises,
The credible equipment fingerprint is stored in the network of personal connections information of the corresponding user.
3. -2 described in any item methods for judging abnormal behaviour based on credible equipment fingerprint according to claim 1, wherein
It is described to be based on the user's operation behavior property data, judgment models operation is belonged to by user equipment, obtains corresponding be somebody's turn to do
The credible equipment fingerprint of user further comprises,
Use user information as the input variable of the anti-fraud model of debt-credit in real time in the credible equipment fingerprint and equipment.
4. the method according to claim 1-3 for judging abnormal behaviour based on credible equipment fingerprint, wherein
If described when finding that user equipment carries out specific operation by other users by user's credible equipment fingerprint, abnormal row is issued
For prompt further comprise,
If the specific operation is related to debt-credit, the identity information of the user using the equipment is further verified.
5. the method according to claim 1-4 for judging abnormal behaviour based on credible equipment fingerprint, wherein
The credible equipment is defined as the commonly used equipment of user, and the equipment did not occurred abnormal behaviour.
6. the method according to claim 1-5 for judging abnormal behaviour based on credible equipment fingerprint, wherein
Based on the user's operation behavior property data, judgment models operation is belonged to by user equipment, obtains corresponding to the user
The method of credible equipment fingerprint include,
Based on user's operation behavior property data, constructs user equipment and belong to judgment models；
Obtain the user equipment ownership judgment models required input variable；The input variable includes user's operation behavior；
Operation is carried out using user equipment ownership judgment models, obtains user equipment ownership result.
7. the method according to claim 1-6 for judging abnormal behaviour based on credible equipment fingerprint, wherein
The building user equipment belongs to judgment models,
According to user's operation behavior property data, training user is set in such a way that supervised learning and semi-supervised learning combine
Standby ownership judgment models.
8. the system of abnormal behaviour is judged based on credible equipment fingerprint, including,
User's registration information obtains module, for obtaining user's operation behavior property data；
User equipment belongs to judgment module, for being based on the user's operation behavior property data, is sentenced by user equipment ownership
Disconnected model calculation, obtains the credible equipment fingerprint for corresponding to the user；
Analysis and processing module, if for finding that user equipment carries out specific operation by other users by user's credible equipment fingerprint
When, issue the prompt of abnormal behaviour.
9. a kind of server, including processor and memory:
The memory is used to store the program that perform claim requires any one of 1 to 7 the method；
The processor is configured to for executing the program stored in the memory.
10. a kind of computer readable storage medium, is stored with computer program, which is characterized in that the program is held by processor
The step of any one of claim 1 to 7 the method is realized when row.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113989859A (en) * | 2021-12-28 | 2022-01-28 | 江苏苏宁银行股份有限公司 | Fingerprint similarity identification method and device for anti-flashing equipment |
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104318138A (en) * | 2014-09-30 | 2015-01-28 | 杭州同盾科技有限公司 | Method and device for verifying identity of user |
| CN104580091A (en) * | 2013-10-21 | 2015-04-29 | 深圳市腾讯计算机系统有限公司 | Identity verification method, device and system |
| CN105844126A (en) * | 2016-03-16 | 2016-08-10 | 成都信息工程大学 | Automatic identification method of intelligent electronic device user |
| CN106548342A (en) * | 2015-09-22 | 2017-03-29 | 阿里巴巴集团控股有限公司 | A kind of credible equipment determines method and device |
| CN106559213A (en) * | 2015-09-24 | 2017-04-05 | 腾讯科技（深圳）有限公司 | Device management method, equipment and system |
| US20170147803A1 (en) * | 2015-02-04 | 2017-05-25 | Aerendir Mobile Inc. | Local user authentication with neuro and neuro-mechanical fingerprints |
| CN106909545A (en) * | 2015-12-22 | 2017-06-30 | 阿里巴巴集团控股有限公司 | A kind of method and apparatus of the attaching information for determining user |
| CN107066616A (en) * | 2017-05-09 | 2017-08-18 | 北京京东金融科技控股有限公司 | Method, device and electronic equipment for account processing |
| CN107465642A (en) * | 2016-06-02 | 2017-12-12 | 百度在线网络技术（北京）有限公司 | A kind of method and device for judging account abnormal login |
| CN107481019A (en) * | 2017-07-28 | 2017-12-15 | 上海携程商务有限公司 | Order fraud recognition methods, system, storage medium and electronic equipment |
| CN107579956A (en) * | 2017-08-07 | 2018-01-12 | 北京奇安信科技有限公司 | The detection method and device of a kind of user behavior |
| CN107742068A (en) * | 2017-09-01 | 2018-02-27 | 中国科学院信息工程研究所 | A kind of implicit identity authorization system of the multi-source of smart machine and method |
| CN109165940A (en) * | 2018-06-28 | 2019-01-08 | 阿里巴巴集团控股有限公司 | A kind of theft preventing method, device and electronic equipment |
| CN109241711A (en) * | 2018-08-22 | 2019-01-18 | 平安科技（深圳）有限公司 | User behavior recognition method and device based on prediction model |
| CN109300028A (en) * | 2018-09-11 | 2019-02-01 | 上海天旦网络科技发展有限公司 | Real-time anti-fraud method and system and storage medium based on network data |
| CN109922032A (en) * | 2017-12-13 | 2019-06-21 | 百度在线网络技术（北京）有限公司 | Method and apparatus for determining the risk of logon account |
| CN110348190A (en) * | 2019-06-29 | 2019-10-18 | 上海淇毓信息科技有限公司 | User equipment ownership judgment method and device based on user's operation behavior |
-
2019
- 2019-06-29 CN CN201910581340.0A patent/CN110362981B/en active Active
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104580091A (en) * | 2013-10-21 | 2015-04-29 | 深圳市腾讯计算机系统有限公司 | Identity verification method, device and system |
| CN104318138A (en) * | 2014-09-30 | 2015-01-28 | 杭州同盾科技有限公司 | Method and device for verifying identity of user |
| US20170147803A1 (en) * | 2015-02-04 | 2017-05-25 | Aerendir Mobile Inc. | Local user authentication with neuro and neuro-mechanical fingerprints |
| CN106548342A (en) * | 2015-09-22 | 2017-03-29 | 阿里巴巴集团控股有限公司 | A kind of credible equipment determines method and device |
| CN106559213A (en) * | 2015-09-24 | 2017-04-05 | 腾讯科技（深圳）有限公司 | Device management method, equipment and system |
| CN106909545A (en) * | 2015-12-22 | 2017-06-30 | 阿里巴巴集团控股有限公司 | A kind of method and apparatus of the attaching information for determining user |
| CN105844126A (en) * | 2016-03-16 | 2016-08-10 | 成都信息工程大学 | Automatic identification method of intelligent electronic device user |
| CN107465642A (en) * | 2016-06-02 | 2017-12-12 | 百度在线网络技术（北京）有限公司 | A kind of method and device for judging account abnormal login |
| CN107066616A (en) * | 2017-05-09 | 2017-08-18 | 北京京东金融科技控股有限公司 | Method, device and electronic equipment for account processing |
| CN107481019A (en) * | 2017-07-28 | 2017-12-15 | 上海携程商务有限公司 | Order fraud recognition methods, system, storage medium and electronic equipment |
| CN107579956A (en) * | 2017-08-07 | 2018-01-12 | 北京奇安信科技有限公司 | The detection method and device of a kind of user behavior |
| CN107742068A (en) * | 2017-09-01 | 2018-02-27 | 中国科学院信息工程研究所 | A kind of implicit identity authorization system of the multi-source of smart machine and method |
| CN109922032A (en) * | 2017-12-13 | 2019-06-21 | 百度在线网络技术（北京）有限公司 | Method and apparatus for determining the risk of logon account |
| CN109165940A (en) * | 2018-06-28 | 2019-01-08 | 阿里巴巴集团控股有限公司 | A kind of theft preventing method, device and electronic equipment |
| CN109241711A (en) * | 2018-08-22 | 2019-01-18 | 平安科技（深圳）有限公司 | User behavior recognition method and device based on prediction model |
| CN109300028A (en) * | 2018-09-11 | 2019-02-01 | 上海天旦网络科技发展有限公司 | Real-time anti-fraud method and system and storage medium based on network data |
| CN110348190A (en) * | 2019-06-29 | 2019-10-18 | 上海淇毓信息科技有限公司 | User equipment ownership judgment method and device based on user's operation behavior |
Non-Patent Citations (2)
| Title |
|---|
| 吴雷等: "基于多层架构的信用卡反欺诈系统研究", 《金融科技时代》 * |
| 杨从安: "数字联盟移动设备可信ID概述", 《网络空间安全》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113989859A (en) * | 2021-12-28 | 2022-01-28 | 江苏苏宁银行股份有限公司 | Fingerprint similarity identification method and device for anti-flashing equipment |
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| Publication number | Publication date |
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