CN108810230B - Method, device and equipment for acquiring incoming call prompt information - Google Patents
Method, device and equipment for acquiring incoming call prompt information Download PDFInfo
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- CN108810230B CN108810230B CN201710282748.9A CN201710282748A CN108810230B CN 108810230 B CN108810230 B CN 108810230B CN 201710282748 A CN201710282748 A CN 201710282748A CN 108810230 B CN108810230 B CN 108810230B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/57—Arrangements for indicating or recording the number of the calling subscriber at the called subscriber's set
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/57—Arrangements for indicating or recording the number of the calling subscriber at the called subscriber's set
- H04M1/575—Means for retrieving and displaying personal data about calling party
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
- H04M1/724—User interfaces specially adapted for cordless or mobile telephones
- H04M1/72484—User interfaces specially adapted for cordless or mobile telephones wherein functions are triggered by incoming communication events
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Abstract
The invention discloses a method for acquiring incoming call prompt information, which comprises the following steps: acquiring data associated with a target telephone number of an incoming call on a terminal, wherein the data comprises internet data and telecommunication data associated with the target telephone number; when the target telephone number is determined not to be in the telephone contact list of the terminal, extracting the characteristics representing all dimensions from the data; inputting the characteristics of each dimension into a telephone evaluation model, determining the malicious index of the target telephone number, and reflecting the degree that the target telephone number is a malicious number; and when the malicious index is larger than the malicious threshold, determining that the target telephone number is a malicious number, and sending alarm prompt information aiming at the incoming call to the terminal. The method for acquiring the incoming call prompt information can determine the malicious degree of the target telephone number of the incoming call on the terminal, and carries out alarm prompt according to the malicious degree, so that harassing calls and fraud calls are effectively prompted.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method, a device and equipment for acquiring incoming call prompt information.
Background
With the maturity of telecommunication networks and mobile communication networks, basically, everyone has communication equipment such as a mobile phone, and can find familiar people or strangers by making a call, which brings great convenience to the majority of users.
Although the maturity of the communication network brings great convenience to the majority of users, the users often hear promotional calls or fraud calls due to serious telephone number leakage, and the users also have a lot of troubles, not only because the calls are harassed, but also many users are successfully cheated, and property loss and mental injury are caused.
How to effectively prompt harassing calls and fraudulent calls becomes a problem to be solved urgently.
Disclosure of Invention
In order to better prompt crank calls and fraud calls, the embodiment of the application provides a method for acquiring incoming call prompt information, which can determine the malicious degree of a target telephone number through analyzing internet data and telecommunication data associated with the target telephone number of an incoming call on a terminal, and perform alarm prompt according to the malicious degree, so that crank calls and fraud calls are effectively prompted. The embodiment of the application also provides a corresponding device and equipment.
A first aspect of the present application provides a method for obtaining incoming call prompt information, including:
acquiring data associated with a target telephone number of an incoming call on a terminal, wherein the data comprises internet data and telecommunication data associated with the target telephone number;
when the target telephone number is determined not to be in the telephone contact list of the terminal, extracting features representing all dimensions from the data;
inputting the characteristics of all dimensions into a telephone evaluation model, and determining a malicious index of the target telephone number, wherein the malicious index reflects the degree of the target telephone number being a malicious number;
and when the malicious index is larger than a malicious threshold value, determining that the target telephone number is a malicious number, and sending alarm prompt information aiming at the incoming call to the terminal.
A second aspect of the present application provides a method for establishing a phone assessment model, including:
obtaining a first type of data and a second type of data selected as samples, wherein the first type of data is malicious object data, the second type of data is non-malicious object data, and each of the first type of data and the second type of data comprises internet data and telecommunication data associated with a specified telephone number;
and training an initial evaluation model according to the first class data and the second class data to obtain the telephone evaluation model.
A third aspect of the present application provides an apparatus for obtaining incoming call prompt information, including:
an acquisition program module for acquiring data associated with a target telephone number of an incoming call on a terminal, the data including internet data and telecommunications data associated with the target telephone number;
the first determining program module is used for extracting the characteristics representing all dimensions from the data when the target telephone number acquired by the acquiring program module is determined not to be in the telephone contact list of the terminal;
the second determining program module is used for inputting the features of the dimensions extracted by the first determining program module into a telephone evaluation model, and determining a malicious index of the target telephone number, wherein the malicious index reflects the degree of the target telephone number as a malicious number;
and a third determining program module, configured to determine that the target phone number is a malicious number when the malicious index determined by the second determining program module is greater than a malicious threshold, and send an alert notification message for an incoming call to the terminal.
A fourth aspect of the present application provides an apparatus for establishing a phone assessment model, including:
an acquisition program module for acquiring a first type of data and a second type of data selected as samples, the first type of data being malicious object data, the second type of data being non-malicious object data, each of the first type of data and the second type of data including internet data and telecommunications data associated with a specified telephone number;
and the model training program module is used for training an initial evaluation model according to the first type data and the second type data acquired by the acquisition program module so as to obtain the telephone evaluation model.
A fifth aspect of the present application provides a computer device comprising: an input/output (I/O) interface, a processor, and a memory, where the instruction for obtaining the incoming call prompt information according to the first aspect is stored in the memory;
the processor is configured to execute the instructions for obtaining the incoming call prompt information stored in the memory, and execute the steps of the method for obtaining the incoming call prompt information according to the first aspect.
A sixth aspect of the present application provides a computer device comprising: an input/output (I/O) interface, a processor, and a memory, the memory having stored therein instructions for the telephone evaluation model establishment of the second aspect;
the processor is configured to execute instructions of the phone assessment model building stored in the memory to perform the steps of the method of phone assessment model building as described in the second aspect.
Yet another aspect of the present application provides a computer-readable storage medium having stored therein instructions, which when executed on a computer, cause the computer to perform the method of the above-described aspects.
Yet another aspect of the present application provides a computer program product containing instructions which, when run on a computer, cause the computer to perform the method of the above-described aspects.
Compared with the prior art that the harassing call and the fraud call cannot be well prompted, the method for acquiring the incoming call prompt information provided by the embodiment of the application can determine the malicious degree of the target telephone number by analyzing the internet data and the telecommunication data associated with the target telephone number of the incoming call on the terminal, and performs alarm prompt according to the malicious degree, so that the harassing call and the fraud call are effectively prompted.
Drawings
FIG. 1 is a schematic diagram of a network architecture of a distributed system according to an embodiment of the present application;
FIG. 2 is a schematic structural diagram of a distributed system in a simulation scenario in the embodiment of the present application;
FIG. 3 is a schematic diagram of an embodiment of a method for establishing a telephone evaluation model in an embodiment of the present application;
FIG. 4 is a schematic diagram of another embodiment of a method for establishing a telephone evaluation model provided by an embodiment of the present application;
fig. 5 is a schematic diagram of an embodiment of a method for obtaining an incoming call prompt message in an embodiment of the present application;
fig. 6 is a schematic diagram illustrating an example of a scenario of a method for obtaining incoming call prompt information in an embodiment of the present application;
FIG. 7 is a schematic diagram of another embodiment of a method for obtaining incoming call prompt information in an embodiment of the present application;
FIG. 8 is a schematic diagram of an embodiment of an apparatus for obtaining incoming call prompt information in the embodiment of the present application;
FIG. 9 is a schematic diagram of another embodiment of an apparatus for obtaining incoming call prompt information in the embodiment of the present application;
FIG. 10 is a schematic diagram of an embodiment of a device for telephone evaluation modeling in an embodiment of the present application;
FIG. 11 is a schematic diagram of an embodiment of a computer device in an embodiment of the present application.
Detailed Description
Embodiments of the present invention will be described below with reference to the accompanying drawings, and it is to be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. As can be appreciated by those skilled in the art, as technology advances, the technical solutions provided by the embodiments of the present invention are also applicable to similar technical problems.
The embodiment of the invention provides a method for acquiring incoming call prompt information, which can determine the malicious degree of a target telephone number by analyzing internet data and telecommunication data associated with the target telephone number of an incoming call on a terminal, and carry out alarm prompt according to the malicious degree, thereby effectively prompting harassing calls and fraud calls. The embodiment of the application also provides a corresponding device and equipment. The following are detailed below.
In daily life, users often hear various product sales promotion calls or fraud calls, and if the telephone number of the incoming call is not in a telephone contact list, and the incoming call of a stranger is displayed during the incoming call, a plurality of important calls are likely to be missed if the incoming call is refused, so that great trouble is caused to the daily life of the users. Although some software applications can give some prompts to the incoming call telephone number and can identify whether the incoming call telephone number belongs to a harassing call or not according to the number of times that the incoming call telephone number is marked by the user, the prompting mode only judges the marked condition, and the telephone number which is not marked cannot be identified, so that the accuracy of prompting for the incoming call is not high.
The method and the device for processing the incoming call number combine the telecommunication data with the internet data by relying on the telecommunication data and the internet data, make a call evaluation model for the evaluation of the call number through a machine learning algorithm, evaluate the incoming call number through the call evaluation model, determine that the incoming call number is a malicious index, and make an alarm prompt according to the malicious index.
The malicious number in the embodiment of the present application refers to a telephone number for the purpose of promotion or fraud, that is, a so-called promotion telephone number or fraud telephone number.
The telecommunications data in the embodiments of the present application typically includes a contact list for each telephone number, and a record of calls made or made by each telephone number. Internet data typically includes indicia of telephone numbers on a network, such as the marking of promotional or fraudulent calls by users on the network. The internet data also includes associations between the phone number and some applications, especially some social applications such as WeChat or QQ. Data associated with these applications, such as a contact list for WeChat and a contact list for QQ, etc., of course, there may be more Internet data, and these are only examples described here, and other Internet data that can be managed with telephone numbers may be suitable.
The embodiment of the application relates to establishment and use of a telephone evaluation model, wherein the telephone evaluation model refers to a model or algorithm capable of calculating a malicious index, namely a malicious degree, of a telephone number.
Whether the phone assessment model is established or used, the method needs to be implemented based on background computing devices, which usually exist in a cluster form and can be independent computing devices. The following description will be made by taking the form of a cluster as an example.
As shown in fig. 1, fig. 1 is a schematic diagram of a network architecture of a distributed system according to an embodiment of the present application;
the distributed system provided by the embodiment of the application comprises a Master control node (Master)10, a network 20 and a plurality of working nodes (Worker)30, wherein the Master control node 10 and the working nodes 30 can communicate through the network 20, the working nodes in the distributed system are responsible for storing various telecommunication data and Internet data, the Master control node can be established for a device for acquiring incoming call prompt information or a telephone evaluation model in the embodiment of the application and is responsible for establishing the telephone evaluation model or determining the malicious index of a target telephone number of an incoming call on a terminal according to the telecommunication data and the Internet data recorded by the working nodes, and the malicious index reflects the degree that the target telephone number is a malicious number. And when the malicious number is determined, sending alarm prompt information to the terminal. In the embodiment of the present application, there may be one or more master nodes 10. For example, in order to ensure the reliability of the system, a standby main control node may be deployed to share a part of the load when the load of the currently operating main control node is too high, or to take over the work of the main control node when the currently operating main control node fails. Both the master node 10 and the worker node 30 may be physical hosts.
The distributed system may also be a virtualized system, which may take the form shown in fig. 2 in a virtualization scenario in which the distributed system includes a hardware layer and a Virtual Machine Monitor (VMM)1001 running above the hardware layer, and a plurality of virtual machines 1002. One or more virtual machines may be selected as master nodes and a plurality of virtual machines as worker nodes.
Specifically, virtual machine 1002: one or more virtual computers are simulated on common hardware resources through virtual machine software, the virtual machines work like real computers, an operating system and an application program can be installed on the virtual machines, and the virtual machines can also access network resources. For applications running in a virtual machine, the virtual machine operates as if it were a real computer.
Hardware layer: the hardware platform on which the virtualized environment operates may be abstracted from the hardware resources of one or more physical hosts. The hardware layer may include various hardware, including, for example, a processor 1004 (e.g., CPU) and a memory 1005, and may also include a network card 1003 (e.g., RDMA network card), high-speed/low-speed Input/Output (I/O) devices, and other devices with specific processing functions.
In addition, the distributed system under the virtualization scenario may further include a Host (Host): as management layer, it is used to complete the management and allocation of hardware resources; presenting a virtual hardware platform for a virtual machine; and the scheduling and isolation of the virtual machine are realized. Wherein, the Host may be a Virtual Machine Monitor (VMM); in addition, sometimes the VMM and 1 privileged virtual machine cooperate, the combination of which constitutes the Host. The virtual hardware platform provides various hardware resources for each virtual machine running thereon, such as a virtual processor (e.g., VCPU), a virtual memory, a virtual disk, a virtual network card, and the like. The virtual disk may correspond to a file of the Host or a logical block device. The virtual machine runs on a virtual hardware platform prepared for the Host, and one or more virtual machines run on the Host.
Privileged virtual machines: a special virtual machine, also called a driver domain, for example, is called Dom0 on the Xen Hypervisor platform, and a driver of a real physical device, such as a network card or a SCSI disk, is installed in the virtual machine, and can detect and directly access the real physical device. Other virtual machines access the real physical device through the privileged virtual machine using the corresponding mechanisms provided by Hypervisor.
The above is a description of the network architecture of the present application, and the method for establishing the phone evaluation model in the embodiment of the present application is described first with reference to the network architecture.
As shown in fig. 3, an embodiment of the method for establishing a phone evaluation model provided in the embodiment of the present application includes:
101. obtaining a first type of data and a second type of data selected as samples, wherein the first type of data is malicious object data, the second type of data is non-malicious object data, and each of the first type of data and the second type of data comprises internet data and telecommunication data associated with a specified telephone number.
The first type of data is data that has been marked as belonging to a malicious user, i.e., data of a user conducting a telemarketing or data of a user conducting a telefraud. The second category is normal user data, where no promotional and fraudulent activities have taken place.
The first type data and the second type data are multiple, wherein each data corresponds to a telephone number, and the telephone number corresponding to the data is called as a designated telephone number.
102. And training an initial evaluation model according to the first class data and the second class data to obtain the telephone evaluation model.
Compared with the prior art, the method for establishing the telephone evaluation model can establish the telephone evaluation model for evaluating the telephone number of the incoming call, so that accurate prompts can be given to users aiming at crank calls and fraud calls.
Wherein training an initial assessment model based on the first class of data and the second class of data to obtain the phone assessment model may include:
extracting features representing all dimensions from each data of the first class of data and the second class of data;
training an initial evaluation model by using the characteristics representing all dimensions in each datum;
the initial evaluation model is:
hθ(x) And g (theta)Tx) is a maliciousness index;
x is a matrix, and the x comprises the characteristics which characterize each dimension in each datum;
theta is a weight matrix; the number of theta corresponds to the number of x, and theta isTA transposed matrix representing θ;
and training through the characteristic of each data representing each dimension to obtain the value of the theta so as to obtain the telephone evaluation model.
Extracting features representing each dimension from each of the first type of data and the second type of data may include:
extracting at least one characteristic of a network mark attribute associated with a specified telephone number in each data and a contact attribute in an application associated with the specified telephone number from the internet data of each data;
extracting the characteristics of the contact person attribute associated with the appointed telephone number from the telecommunication data of each data.
The features of the dimensions in the embodiments of the present application may include, for example:
the network tag attribute for a given telephone number may be a fraud call or a nuisance call, which may comprise a series of nuisance type calls such as loan promotions, real estate agents and insurance promotions.
The applications associated with the specified telephone number may include WeChat or QQ or other social applications associated with the telephone number, such as: an application registered using the telephone number, or an application registered in the terminal where the telephone number is located.
In-application contact attributes associated with the specified phone number, such as: the contact list of the WeChat contains one or more buddies associated with promotional or fraud phones, or chat content with certain WeChat buddies is related to promotion or fraud.
The contact attribute associated with the specified telephone number in the telecommunications data may be whether a telephone marked as harassing or fraudulent is contained in the contact list for the specified telephone number.
Regarding the value of the feature of each dimension, if each contact contains a fraud attribute or a harassment attribute, the value of the dimension may be 1, and if not, the value of the dimension may be 0.
The value of each dimension is the value of each x in the x matrix, and because the sample data is used in the model training process, hθ(x) The value of theta is known, and the value of theta can be trained through a plurality of samples, so that a telephone evaluation model is obtained.
In order to more visually express the training process of the phone evaluation model in the embodiment of the present application, the following describes the establishment process of the phone evaluation model in the embodiment of the present application with reference to fig. 4.
As shown in fig. 4, in the process of model training, the training process is divided into two parts, one part is a background part, and the other part is a training part.
The background part stores telecommunication data and internet data, and the telecommunication data and the internet data are combined to obtain the user full data. In this embodiment, each piece of data in the user full volume data includes telecommunication data and internet data for a specific telephone number, and the data content can be understood by referring to the description in the above section, which is not repeated herein.
In the training part, the method comprises A, B, C, D and E, wherein, the steps are respectively:
a: and dividing malicious user data from the user full data.
The malicious user data is marked data belonging to a malicious user, namely data of a user who carries out telemarketing or data of a user who carries out telefraud.
B: and dividing normal user data from the user full data.
The normal user data is user data in which no promotional and fraudulent activities have occurred.
C: and respectively extracting the features of each dimension from the malicious user data and the normal user data.
The characteristic of each dimension is the value of each x in the x matrix described above.
D: and modeling by a classification algorithm.
The process of modeling by the classification algorithm is to initially evaluate the model
And training to obtain a weight matrix theta.
Since all sample data is used in the model training process, hθ(x) The value of (a) is known, and the value of theta can be trained through a plurality of samples, so that the telephone evaluation is obtainedAnd (6) estimating the model.
E: a phone assessment model.
The above is a description of a process of establishing a telephone evaluation model, and a process of evaluating a target telephone number using the telephone evaluation model in the embodiment of the present application is described below with reference to the drawings.
Referring to fig. 5, an embodiment of the method for acquiring an incoming call prompt message according to the embodiment of the present application includes:
201. data associated with a target telephone number of an incoming call on a terminal is obtained, the data comprising internet data and telecommunications data associated with the target telephone number.
In the embodiment of the present application, an application having a phone number analysis function may be installed on a terminal, and when a call comes from the terminal, a target phone number of the call is reported to a background device, such as the main control node in fig. 1, through the application. And the background equipment acquires the internet data and the telecommunication data associated with the target telephone number from the storage equipment according to the target telephone number.
202. And when the target telephone number is determined not to be in the telephone contact list of the terminal, extracting the characteristics representing all dimensions from the data.
Whether a target telephone number is in a telephone contact list of the incoming call terminal can be determined according to the telecommunication data, if not, the incoming call can be determined to be a stranger, and features representing all dimensions are extracted from the telecommunication data and internet data associated with the target telephone number.
Extracting features representing each dimension from the data may include:
extracting at least one feature of a network tag attribute and an in-application contact attribute associated with the target telephone number from internet data associated with the target telephone number;
extracting characteristics of contact attributes associated with the target telephone from telecommunications data associated with the target telephone number.
That is, the features characterizing each dimension may include at least one of attributes of the web tag and attributes of contacts in the application associated with the target telephone number, as well as features of attributes of contacts associated with the target telephone.
The features of the dimensions in the embodiments of the present application may include, for example:
the network tag attribute for the target telephone number may be a fraud call or a nuisance call which may comprise a series of nuisance type calls such as loan promotions, real estate agents and insurance promotions.
The applications associated with the target telephone number may include WeChat or QQ or other social applications associated with the telephone number, such as: an application registered using the telephone number, or an application registered in the terminal where the telephone number is located.
In-application contact attributes associated with the target telephone number, such as: the contact list of the WeChat contains one or more buddies associated with promotional or fraud phones, or chat content with certain WeChat buddies is related to promotion or fraud.
The contact attribute associated with the target telephone number in the telecommunications data may be whether a telephone marked as harassing or fraudulent is contained in the contact list for the target telephone number.
203. Inputting the characteristics of all dimensions into a telephone evaluation model, and determining a malicious index of the target telephone number, wherein the malicious index reflects the degree of the target telephone number being a malicious number.
Optionally, the step may comprise:
determining a malicious index for the target phone number according to a phone assessment model;
hθ(x) And g (theta)Tx) is a maliciousness index;
x is a matrix, wherein x comprises at least one of the network tag attribute and the contact attribute in the application associated with the target telephone number, and the characteristic of the contact attribute associated with the target telephone;
theta is a weight matrix, the value of each weight in the weight matrix is obtained in the process of training the telephone evaluation model, the number of theta corresponds to the number of x, and theta isTRepresenting a transposed matrix of theta.
In this calculation, since the values of x and θ are known, h can be calculatedθ(x) The value of (c) is, for example: h isθ(x)＝0.7。
In fact, there may be a terminal to determine whether the target telephone number is in the telephone contact list of the terminal, and if the target telephone number is not in the telephone contact list, the target telephone number is sent to a background device, for example, a main control node.
204. And when the malicious index is larger than a malicious threshold value, determining that the target telephone number is a malicious number, and sending alarm prompt information aiming at the incoming call to the terminal.
If the maliciousness threshold is 0.5, then h may be determinedθ(x) If the number is more than 0.5, the target telephone number is a malicious number, and if h is greater than 0.5θ(x) And if the number is less than 0.5, the target telephone number is a non-malicious number. And sending alarm prompt information to the terminal aiming at the malicious number.
As shown in fig. 6, the process of performing the alert prompt on the incoming call on the terminal may be: after the terminal has the incoming call, the terminal reports the target telephone number 134 and 5513 and 4126 of the incoming call to the background equipment, and the background equipment determines that the target telephone number 134 and 5513 and 4126 are malicious indexes h according to the process of the figure 5θ(x) If the malicious index is 0.7 and is greater than the malicious threshold value 0.5, it may be determined that the target phone number 134 and 5513 and 4126 are malicious numbers, and an alarm prompt message is sent to the terminal.
The two terminals shown in fig. 6 are actually the same terminal, and are only represented by schematic diagrams of the two terminals for distinguishing the difference between no warning indication and the difference between warning indications before and after.
Compared with the prior art that the harassing call and the fraud call cannot be well prompted, the method for acquiring the incoming call prompt information provided by the embodiment of the application can determine the malicious degree of the target telephone number by analyzing the internet data and the telecommunication data associated with the target telephone number of the incoming call on the terminal, and performs alarm prompt according to the malicious degree, so that the harassing call and the fraud call are effectively prompted.
In conjunction with the process of establishing the telephone evaluation model shown in fig. 4, the method for obtaining the incoming call prompt information provided in the embodiment of the present application can be further understood with reference to the process shown in fig. 7. In practice, the model training process may be an off-line training process or an on-line training process.
The background part stores telecommunication data and internet data, and the telecommunication data and the internet data are combined to obtain the user full data. In this embodiment, each piece of data in the user full volume data includes telecommunication data and internet data for a specific telephone number, and the data content can be understood by referring to the description in the above section, which is not repeated herein.
In the training part, the method comprises A, B, C, D and E, wherein, the steps are respectively:
a: and dividing malicious user data from the user full data.
The malicious user data is marked data belonging to a malicious user, namely data of a user who carries out telemarketing or data of a user who carries out telefraud.
B: and dividing normal user data from the user full data.
The normal user data is user data in which no promotional and fraudulent activities have occurred.
C: and respectively extracting the features of each dimension from the malicious user data and the normal user data.
The characteristic of each dimension is the value of each x in the x matrix described above.
D: and modeling by a classification algorithm.
The process of modeling by the classification algorithm is to initially evaluate the model
And training to obtain a weight matrix theta.
Since all sample data is used in the model training process, hθ(x) The value of theta is known, and the value of theta can be trained through a plurality of samples, so that a telephone evaluation model is obtained.
E: a phone assessment model.
F: when a stranger calls, data associated with the target telephone number is acquired.
Then, step C is executed: malicious features are extracted from data associated with the target telephone number.
And inputting the malicious features into a telephone evaluation model to obtain a prediction result of the step G.
And if the prediction result comprises normal and malicious results, sending alarm prompt information to the terminal.
In the above description of the process of establishing a telephone evaluation model and obtaining incoming call prompt information by using the telephone evaluation model, the following describes an apparatus for obtaining incoming call prompt information and an apparatus for establishing a telephone evaluation model in the embodiments of the present application with reference to the drawings.
Referring to fig. 8, an embodiment of the apparatus 40 for obtaining the incoming call prompt information provided in the embodiment of the present application includes:
an obtaining program module 401, configured to obtain data associated with a target telephone number of an incoming call on a terminal, where the data includes internet data and telecommunication data associated with the target telephone number;
a first determining program module 402, configured to extract, when it is determined that the target phone number acquired by the acquiring program module 401 is not in the phone contact list of the terminal, features representing the dimensions from the data;
a second determining program module 403, configured to input the feature of each dimension extracted by the first determining program module 402 into a phone evaluation model, and determine a malicious index of the target phone number, where the malicious index reflects a degree to which the target phone number is a malicious number;
a third determining program module 404, configured to determine that the target phone number is a malicious number when the malicious index determined by the second determining program module 403 is greater than a malicious threshold, and send an alert notification message for an incoming call to the terminal.
Compared with the prior art that the harassing call and the fraud call cannot be well prompted, the device for acquiring the incoming call prompt information provided by the embodiment of the application can determine the malicious degree of the target telephone number by analyzing the internet data and the telecommunication data associated with the target telephone number of the incoming call on the terminal, and performs alarm prompt according to the malicious degree, so that the harassing call and the fraud call are effectively prompted.
Optionally, in another embodiment of the apparatus for acquiring the incoming call prompt information provided in the embodiment of the present application,
the first determination program module 402 is for:
extracting at least one feature of a network tag attribute and an in-application contact attribute associated with the target telephone number from internet data associated with the target telephone number;
extracting characteristics of contact attributes associated with the target telephone from telecommunications data associated with the target telephone number.
Optionally, in another embodiment of the apparatus for acquiring the incoming call prompt information provided in the embodiment of the present application,
the second determination program module 403 is configured to:
determining a malicious index for the target phone number according to a phone assessment model;
hθ(x) And g (theta)Tx) is a maliciousness index;
x is a matrix, wherein x comprises at least one of the network tag attribute and the contact attribute in the application associated with the target telephone number, and the characteristic of the contact attribute associated with the target telephone;
theta is a weight matrix, the value of each weight in the weight matrix is obtained in the process of training the telephone evaluation model, the number of theta corresponds to the number of x, and theta isTRepresenting a transposed matrix of theta.
Optionally, referring to fig. 9, in another embodiment of the apparatus for obtaining an incoming call prompt message provided in this application, the apparatus 40 further includes a model training program module 405,
the get program module 401 is further configured to: obtaining a first type of data and a second type of data selected as samples, wherein the first type of data is malicious object data, the second type of data is non-malicious object data, and each of the first type of data and the second type of data comprises internet data and telecommunication data associated with a specified telephone number;
the model training program module 405 is configured to train an initial evaluation model according to the first class data and the second class data to obtain the phone evaluation model.
Optionally, in another embodiment of the apparatus for acquiring the incoming call prompt information provided in the embodiment of the present application,
the model training program module 405 is used to:
extracting features representing all dimensions from each data of the first class of data and the second class of data;
training an initial evaluation model by using the characteristics representing all dimensions in each datum;
the initial evaluation model is:
hθ(x) And g (theta)Tx) is a maliciousness index;
x is a matrix, and the x comprises the characteristics which characterize each dimension in each datum;
theta isA weight matrix; the number of theta corresponds to the number of x, and theta isTA transposed matrix representing θ;
and training through the characteristic of each data representing each dimension to obtain the value of the theta so as to obtain the telephone evaluation model.
Optionally, in another embodiment of the apparatus for acquiring the incoming call prompt information provided in the embodiment of the present application,
the model training program module 405 is used to:
extracting at least one characteristic of a network mark attribute associated with a specified telephone number in each data and a contact attribute in an application associated with the specified telephone number from the internet data of each data;
extracting the characteristics of the contact person attribute associated with the appointed telephone number from the telecommunication data of each data.
The above description of the apparatus for obtaining the incoming call prompt information can be understood by referring to the related methods in fig. 1 to fig. 7, and will not be repeated herein.
Referring to fig. 10, an embodiment of the device 50 for establishing a phone evaluation model provided by the embodiment of the present application includes:
an obtaining program module 501, configured to obtain a first type of data and a second type of data selected as samples, where the first type of data is malicious object data, the second type of data is non-malicious object data, and each of the first type of data and the second type of data includes internet data and telecommunication data associated with a specified telephone number;
a model training program module 502, configured to train an initial evaluation model according to the first type of data and the second type of data acquired by the acquisition program module 501, so as to obtain the phone evaluation model.
Compared with the prior art, the method for establishing the telephone evaluation model can establish the telephone evaluation model for evaluating the telephone number of the incoming call, so that accurate prompts can be given to users aiming at crank calls and fraud calls.
Alternatively, in another embodiment of the device 50 for establishing a phone evaluation model provided in the embodiment of the present application,
the model training program module 502 is configured to:
extracting features representing all dimensions from each data of the first class of data and the second class of data;
training an initial evaluation model by using the characteristics representing all dimensions in each datum;
the initial evaluation model is:
hθ(x) And g (theta)Tx) is a maliciousness index;
x is a matrix, and the x comprises the characteristics which characterize each dimension in each datum;
theta is a weight matrix; the number of theta corresponds to the number of x, and theta isTA transposed matrix representing θ;
and training through the characteristic of each data representing each dimension to obtain the value of the theta so as to obtain the telephone evaluation model.
Alternatively, in another embodiment of the device 50 for establishing a phone evaluation model provided in the embodiment of the present application,
the model training program module 502 is configured to:
extracting at least one characteristic of a network mark attribute associated with a specified telephone number in each data and a contact attribute in an application associated with the specified telephone number from the internet data of each data;
extracting the characteristics of the contact person attribute associated with the appointed telephone number from the telecommunication data of each data.
The device 50 for establishing a phone evaluation model according to the embodiment of the present application can be understood by referring to the corresponding descriptions in fig. 1 to fig. 7, and repeated descriptions are not repeated here.
Fig. 11 is a schematic structural diagram of a computer device 60 according to an embodiment of the present invention. The computer device 60 includes a processor 610, a memory 650, and a transceiver 630, and the memory 650 may include a read-only memory and a random access memory, and provides operating instructions and data to the processor 610. A portion of the memory 650 may also include non-volatile random access memory (NVRAM).
In some embodiments, memory 650 stores the following elements, executable modules or data structures, or a subset thereof, or an expanded set thereof:
in an embodiment of the present invention, by calling the operation instructions stored in the memory 650 (which may be stored in the operating system),
acquiring data associated with a target telephone number of an incoming call on a terminal, wherein the data comprises internet data and telecommunication data associated with the target telephone number;
when the target telephone number is determined not to be in the telephone contact list of the terminal, extracting features representing all dimensions from the data;
inputting the characteristics of all dimensions into a telephone evaluation model, and determining a malicious index of the target telephone number, wherein the malicious index reflects the degree of the target telephone number being a malicious number;
and when the malicious index is larger than a malicious threshold value, determining that the target telephone number is a malicious number, and sending alarm prompt information aiming at the incoming call to the terminal.
Compared with the prior art that the harassing call and the fraud call cannot be well prompted, the device for acquiring the incoming call prompt information provided by the embodiment of the application can determine the malicious degree of the target telephone number by analyzing the internet data and the telecommunication data associated with the target telephone number of the incoming call on the terminal, and performs alarm prompt according to the malicious degree, so that the harassing call and the fraud call are effectively prompted.
The method disclosed in the above embodiments of the present invention may be applied to the processor 610, or implemented by the processor 610. The processor 610 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 610. The processor 610 may be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 650, and the processor 610 reads the information in the memory 650 and performs the steps of the above method in combination with the hardware thereof.
Optionally, the processor 610 is configured to:
extracting at least one feature of a network tag attribute and an in-application contact attribute associated with the target telephone number from internet data associated with the target telephone number;
extracting characteristics of contact attributes associated with the target telephone from telecommunications data associated with the target telephone number.
Optionally, the processor 610 is configured to:
determining a malicious index for the target phone number according to a phone assessment model;
hθ(x) And g (theta)Tx) is a maliciousness index;
x is a matrix, wherein x comprises at least one of the network tag attribute and the contact attribute in the application associated with the target telephone number, and the characteristic of the contact attribute associated with the target telephone;
theta is a weight matrix, the value of each weight in the weight matrix is obtained in the process of training the telephone evaluation model, the number of theta corresponds to the number of x, and theta isTRepresenting a transposed matrix of theta.
Optionally, the processor 610 is further configured to:
obtaining a first type of data and a second type of data selected as samples, wherein the first type of data is malicious object data, the second type of data is non-malicious object data, and each of the first type of data and the second type of data comprises internet data and telecommunication data associated with a specified telephone number;
and training an initial evaluation model according to the first class data and the second class data to obtain the telephone evaluation model.
Optionally, the processor 610 is configured to:
extracting features representing all dimensions from each data of the first class of data and the second class of data;
training an initial evaluation model by using the characteristics representing all dimensions in each datum;
the initial evaluation model is:
hθ(x) And g (theta)Tx) is a maliciousness index;
x is a matrix, and the x comprises the characteristics which characterize each dimension in each datum;
theta is a weight matrix; the number of theta corresponds to the number of x, and theta isTA transposed matrix representing θ;
and training through the characteristic of each data representing each dimension to obtain the value of the theta so as to obtain the telephone evaluation model.
Optionally, the processor 610 is configured to:
extracting at least one characteristic of a network mark attribute associated with a specified telephone number in each data and a contact attribute in an application associated with the specified telephone number from the internet data of each data;
extracting the characteristics of the contact person attribute associated with the appointed telephone number from the telecommunication data of each data.
The above description of the computer device 60 can be understood with reference to the description of fig. 1 to 7, and will not be repeated herein.
In addition, the actual physical form of the device established by the telephone evaluation model can also be understood by referring to the hardware structure of fig. 11, and the corresponding hardware functions are used for completing the following steps:
the processor 610 is configured to:
obtaining a first type of data and a second type of data selected as samples, wherein the first type of data is malicious object data, the second type of data is non-malicious object data, and each of the first type of data and the second type of data comprises internet data and telecommunication data associated with a specified telephone number;
and training an initial evaluation model according to the first class data and the second class data to obtain the telephone evaluation model.
Optionally, the processor 610 is configured to:
extracting features representing all dimensions from each data of the first class of data and the second class of data;
training an initial evaluation model by using the characteristics representing all dimensions in each datum;
the initial evaluation model is:
hθ(x) And g (theta)Tx) is a maliciousness index;
x is a matrix, and the x comprises the characteristics which characterize each dimension in each datum;
theta is a weight matrix; the number of theta corresponds to the number of x, and theta isTA transposed matrix representing θ;
and training through the characteristic of each data representing each dimension to obtain the value of the theta so as to obtain the telephone evaluation model.
Optionally, the processor 610 is configured to:
extracting at least one characteristic of a network mark attribute associated with a specified telephone number in each data and a contact attribute in an application associated with the specified telephone number from the internet data of each data;
extracting the characteristics of the contact person attribute associated with the appointed telephone number from the telecommunication data of each data.
The above description of the corresponding functions in the computer device can be understood with reference to the corresponding descriptions in fig. 1 to fig. 7, and will not be repeated herein.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product.
The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can store or a data storage device, such as a server, a data center, etc., that is integrated with one or more available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: ROM, RAM, magnetic or optical disks, and the like.
The method for acquiring the incoming call prompt information, the method for establishing the telephone evaluation model, the device for establishing the telephone evaluation model and the equipment provided by the embodiment of the invention are described in detail, a specific example is applied in the text to explain the principle and the implementation mode of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (11)
1. A method for obtaining incoming call prompt information is characterized by comprising the following steps:
acquiring data associated with a target telephone number of an incoming call on a terminal, wherein the data comprises internet data and telecommunication data associated with the target telephone number; the internet data at least comprises data associated with an application, and the application has an association relation with the target telephone number;
when the target telephone number is determined not to be in the telephone contact list of the terminal, extracting features representing all dimensions from the data;
inputting the characteristics of all dimensions into a telephone evaluation model, and determining a malicious index of the target telephone number, wherein the malicious index reflects the degree of the target telephone number being a malicious number;
when the malicious index is larger than a malicious threshold value, determining that the target telephone number is a malicious number, and sending alarm prompt information aiming at the incoming call to the terminal;
wherein the extracting features characterizing each dimension from the data includes:
extracting at least one feature of a network tag attribute and a contact attribute in the application from internet data associated with the target telephone number;
extracting characteristics of contact attributes associated with the target telephone from telecommunications data associated with the target telephone number.
2. The method of claim 1, wherein inputting the features of the dimensions into a phone assessment model, determining a malicious index for the target phone number, comprises:
determining a malicious index for the target phone number according to a phone assessment model;
x is a matrix, wherein x comprises at least one of the network tag attribute and the contact attribute in the application associated with the target telephone number, and the characteristic of the contact attribute associated with the target telephone;
3. A method for telephone rating model building, comprising:
obtaining a first type of data and a second type of data selected as samples, wherein the first type of data is malicious object data, the second type of data is non-malicious object data, and each of the first type of data and the second type of data comprises internet data and telecommunication data associated with a specified telephone number; the internet data at least comprises data associated with an application, and the application has an association relation with the specified telephone number;
extracting features representing all dimensions from each data of the first class of data and the second class of data;
training an initial evaluation model according to the characteristics representing all dimensions in each datum to obtain a telephone evaluation model;
extracting features representing the dimensions from each of the first class of data and the second class of data comprises:
extracting at least one feature of a network tag attribute associated with a specified telephone number in each data and a contact attribute in the application from internet data of each data;
extracting the characteristics of the contact person attribute associated with the appointed telephone number from the telecommunication data of each data.
4. The method of claim 3, wherein training the initial assessment model according to the features characterizing the dimensions in each data to obtain the phone assessment model comprises:
training an initial evaluation model by using the characteristics representing all dimensions in each datum;
the initial evaluation model is:
x is a matrix, and the x comprises the characteristics which characterize each dimension in each datum;
training through the characteristic of each dimension in each data to obtain the data
To obtain the phone assessment model.
5. An apparatus for obtaining incoming call prompt information, comprising:
an acquisition program module for acquiring data associated with a target telephone number of an incoming call on a terminal, the data including internet data and telecommunications data associated with the target telephone number; the internet data at least comprises data associated with an application, and the application has an association relation with the target telephone number;
the first determining program module is used for extracting the characteristics representing all dimensions from the data when the target telephone number acquired by the acquiring program module is determined not to be in the telephone contact list of the terminal;
the second determining program module is used for inputting the features of the dimensions extracted by the first determining program module into a telephone evaluation model, and determining a malicious index of the target telephone number, wherein the malicious index reflects the degree of the target telephone number as a malicious number;
a third determining program module, configured to determine that the target phone number is a malicious number when the malicious index determined by the second determining program module is greater than a malicious threshold, and send an alert notification message for an incoming call to the terminal;
wherein the first determining program module is specifically configured to:
extracting at least one feature of a network tag attribute and a contact attribute in the application from internet data associated with the target telephone number;
extracting characteristics of contact attributes associated with the target telephone from telecommunications data associated with the target telephone number.
6. The apparatus of claim 5,
the second determining program module is to:
determining a malicious index for the target phone number according to a phone assessment model;
x is a matrix, wherein x comprises at least one of the network tag attribute and the contact attribute in the application associated with the target telephone number, and the characteristic of the contact attribute associated with the target telephone;
7. An apparatus for telephone rating model building, comprising:
an acquisition program module for acquiring a first type of data and a second type of data selected as samples, the first type of data being malicious object data, the second type of data being non-malicious object data, each of the first type of data and the second type of data including internet data and telecommunications data associated with a specified telephone number; the internet data at least comprises data associated with an application, and the application has an association relation with the specified telephone number;
the model training program module is used for extracting features representing all dimensions from each data of the first class of data and the second class of data; training an initial evaluation model according to the characteristics representing all dimensions in each datum to obtain a telephone evaluation model;
the model training program module extracts features representing dimensions from each of the first type of data and the second type of data, and specifically includes:
extracting at least one feature of a network tag attribute associated with a specified telephone number in each data and a contact attribute in the application from internet data of each data;
extracting the characteristics of the contact person attribute associated with the appointed telephone number from the telecommunication data of each data.
8. The apparatus of claim 7,
the model training program module is used for:
training an initial evaluation model by using the characteristics representing all dimensions in each datum;
the initial evaluation model is:
x is a matrix, and the x comprises the characteristics which characterize each dimension in each datum;
training through the characteristic of each dimension in each data to obtain the data
To obtain the phone assessment model.
9. A computer device, comprising: an input/output (I/O) interface, a processor, and a memory having instructions stored therein;
the processor is used for executing the instructions to execute the method for acquiring the incoming call prompt message according to any one of claims 1-2.
10. A computer device, comprising: an input/output (I/O) interface, a processor, and a memory having instructions stored therein;
the processor is configured to execute the instructions to perform a method of telephone rating model building as claimed in any of claims 3 to 4.
11. A computer-readable storage medium having stored thereon instructions which, when executed on a computer, cause the computer to perform the method of any of claims 1-4.
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| CN102447771A (en) * | 2010-10-08 | 2012-05-09 | Tcl集团股份有限公司 | Method of displaying the information of calling party and communication terminal thereof |
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