CN111935646B - Method and system for estimating common address of mobile equipment user - Google Patents

Method and system for estimating common address of mobile equipment user Download PDF

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CN111935646B
CN111935646B CN202010709723.4A CN202010709723A CN111935646B CN 111935646 B CN111935646 B CN 111935646B CN 202010709723 A CN202010709723 A CN 202010709723A CN 111935646 B CN111935646 B CN 111935646B
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city
equipment
address
frequency
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CN111935646A (en
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张维邦
田丹丹
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Beijing Minglue Zhaohui Technology Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
    • H04L63/1425—Traffic logging, e.g. anomaly detection
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L2101/00—Indexing scheme associated with group H04L61/00
    • H04L2101/60—Types of network addresses
    • H04L2101/69—Types of network addresses using geographic information, e.g. room number
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/08—Network architectures or network communication protocols for network security for authentication of entities
    • H04L63/0876—Network architectures or network communication protocols for network security for authentication of entities based on the identity of the terminal or configuration, e.g. MAC address, hardware or software configuration or device fingerprint
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/10—Network architectures or network communication protocols for network security for controlling access to devices or network resources
    • H04L63/107—Network architectures or network communication protocols for network security for controlling access to devices or network resources wherein the security policies are location-dependent, e.g. entities privileges depend on current location or allowing specific operations only from locally connected terminals

Abstract

The invention relates to the technical field of abnormal flow identification, in particular to a method and a system for estimating a common address of a mobile equipment user. The method comprises the following steps: s1: resolving the address of the equipment from the IP address to obtain the corresponding frequency relation between the equipment and each city, and obtaining a correlation coefficient between the equipment i and the city j through calculation; s2: and judging whether the city j is the common address of the equipment i or not according to the correlation coefficient between the equipment i and the city j. The invention has the beneficial effects that: because the user behavior attribute is described, the individual difference of each user is considered, and the misjudgment is avoided to a certain extent; because the common address is used as the characteristic for identifying the abnormal flow, the data of the address where the equipment is located can be judged to be easy to obtain from the exposure or the click record of each user behavior, and the method is convenient to popularize to different scenes.

Description

Method and system for estimating common address of mobile equipment user
Technical Field
The invention relates to the technical field of abnormal flow identification, in particular to a method and a system for estimating a common address of a mobile equipment user.
Background
The identification of abnormal traffic usually depends on the advertisement exposure click frequency, the IP address correlation quantity and the like in a certain time period, the traffic credibility score is calculated through a rule, and then the abnormal traffic is judged according to a threshold value. The device IP corresponding to the traffic is an important characteristic for measuring the traffic effectiveness, and the geographic position of the device activity can be analyzed through the IP, so that whether the device appears at the geographic position where the device activity is impossible is judged.
In the prior art, the flow judgment rule based on the IP address is generally adopted, the threshold value of the time window is manually selected according to the geographical positions of the equipment login for a few times, and then judgment is carried out.
The invention provides a method and a system for estimating a common address of a mobile equipment user according to the problems. The common address is judged by describing the behavior attribute of the user, and the abnormal flow can be judged by taking the common address as a characteristic.
Disclosure of Invention
The present invention provides a method and a system for estimating a common address of a mobile device user, aiming at the above existing technical problems.
In order to achieve the purpose, the invention provides the following technical scheme:
a method for estimating a common address of a user of a mobile device, comprising the steps of:
s1: resolving the address of the equipment from the IP address to obtain the corresponding frequency relation between the equipment and each city, and obtaining a correlation coefficient between the equipment i and the city j through calculation;
s2: and judging whether the city j is the common address of the equipment i or not according to the correlation coefficient between the equipment i and the city j.
Preferably, S1 specifically includes the following steps:
s11: resolving the address of the equipment according to the IP address in the advertisement exposure click record of the equipment, establishing a frequency matrix A with initialization dimensionality of (N, K) according to the corresponding frequency relation between the equipment i and the city j in a certain time period,
Figure GDA0003627360480000021
wherein each row of matrix A represents the frequency of device i in each city, each column represents the frequency of device j, A ij Representing the frequency of occurrence of device i in city j within a certain time period;
s12: obtaining a stability coefficient of each device according to the frequency matrix A in S11;
s13: and determining a correlation coefficient matrix between each device and each city according to the frequency matrix A in S11 and the stability coefficient in S12 to obtain a correlation coefficient between the device i and the city j.
Preferably, S12 specifically includes:
and constructing an urban matrix B according to the frequency matrix A, and then calculating the inverse urbanization frequency of each device to serve as a stability coefficient of the device.
Preferably, S12 includes the steps of:
s121: according to the frequency matrix A, an indication function I () is utilized to obtain a city matrix B,
Figure GDA0003627360480000022
s122: adding and taking reciprocal of each row in the matrix B to obtain the inverse urbanization frequency of each device, namely the stability coefficient S i ，
Figure GDA0003627360480000023
Wherein the denominator
Figure GDA0003627360480000024
Is the sum of all elements in row i of matrix B.
Preferably, when a (i, j) >0, B (i, j) ═ 1, otherwise, B (i, j) is zero,
preferably, S13 includes the following steps:
s131: normalizing the frequency matrix A in S11 to obtain a probability matrix,
Figure GDA0003627360480000031
wherein the content of the first and second substances,
Figure GDA0003627360480000032
Figure GDA0003627360480000033
representing the probability of device i appearing in city j;
s132: multiplying each row of the probability matrix obtained in S131 by the stability coefficient S of the corresponding device obtained in S122 in sequence i To obtain a matrix M of correlation coefficients,
Figure GDA0003627360480000034
wherein M is ij Represents the correlation coefficient between device i and city j, which represents the correlation between device and city j.
Preferably, the larger the corresponding correlation coefficient in the correlation coefficient matrix M is, the greater the correlation between the corresponding device and the city is, and the more likely it is to determine that the device is the common address.
Preferably, the step of S2 includes: combining the correlation coefficient matrix obtained in S132, and converting M ij When compared with a threshold value k, when M ij >And k, judging the city j as the common address of the equipment i.
The invention also provides a system for estimating the common address of the mobile equipment user, and the method for estimating the common address based on the mobile equipment user comprises the following steps:
the data acquisition module is used for receiving the acquired IP address and outputting the analyzed address of the equipment;
the data processing module is used for receiving the address of the equipment output by the data acquisition module, processing the address and outputting a corresponding correlation coefficient matrix between each equipment and each city;
and the judging module is used for judging whether the city j is a common address for the equipment i or not according to the correlation coefficient matrix output by the data processing module.
Preferably, the data processing module specifically includes:
the first data processing module is used for receiving the address of the equipment output by the data acquisition module and outputting a frequency matrix A;
a second data processing module for receiving the frequency matrix A of the first data processing module and outputting a probability matrix
Figure GDA0003627360480000041
The third data processing module is used for receiving the frequency matrix A of the first data processing module, and outputting the stability coefficient of each device after constructing the city matrix B;
a fourth data processing module for receiving the probability matrix output by the second data processing module
Figure GDA0003627360480000042
And the stability coefficient of each device output by the third data processing module, and outputting a correlation coefficient matrix M.
Compared with the prior art, the invention has the advantages and positive effects that:
the method and the system for estimating the common address of the mobile equipment user can be applied to the field of abnormal flow identification, and have the advantage of more accurately judging whether the common address is used or not.
1. Because the association between the equipment i and the city j is used as a label to depict the behavior attribute of the user, the individual difference of each user is considered, and the misjudgment is avoided to a certain extent.
2. Because the common address is used as the characteristic for identifying the abnormal flow, the data of the address where the equipment is located can be judged to be easy to obtain from the exposure or the click record of each user behavior, so that the method is convenient to popularize.
Drawings
FIG. 1 is a flow chart illustrating a method for estimating a common address of a mobile UE;
fig. 2 is a schematic flowchart of step S1 in the common address estimation method of the mobile user equipment;
fig. 3 is a flowchart illustrating a step S12 in the method for estimating a general address of a mobile user equipment;
fig. 4 is a flowchart illustrating the step S13 in the method for estimating the general address of the mobile user equipment;
fig. 5 is a schematic diagram of a conventional address estimation system for a mobile user equipment.
Detailed Description
Hereinafter, embodiments of the present invention will be further described with reference to the accompanying drawings.
The invention provides a method for estimating a common address of a mobile equipment user, which more reasonably and effectively utilizes an IP address, obtains the common address of the equipment user according to the IP address, and describes user attributes by taking the common address as a label, wherein the user attributes can be used as the characteristics of abnormal flow identification. The specific flow of the common address estimation method for the mobile device user is shown in fig. 1, and includes the following steps:
s1: resolving the address of the equipment from the IP address to obtain the corresponding frequency relation between the equipment and each city, and obtaining a correlation coefficient between the equipment i and the city j through calculation;
s2: and adjusting a threshold value according to a correlation coefficient between the equipment i and the city j, and judging whether the city j is a common address of the equipment i.
And describing user attributes for the labels by judging whether the city j is the common address of the equipment i or not as a characteristic for identifying abnormal flow. The individual difference of the equipment is considered, and the misjudgment is avoided to a certain extent. The frequency is the number of advertisement exposure clicks.
As shown in fig. 2, S1 specifically includes the following steps:
s11: resolving the address of the equipment according to the IP address in the advertisement exposure click record of the equipment, establishing a frequency matrix A with initialization dimensionality of (N, K) according to the corresponding frequency relation between the equipment i and the city j in a certain time period,
Figure GDA0003627360480000051
wherein each row of matrix A represents the frequency of device i in each city, each column represents the frequency of device j, A ij Represents the frequency of occurrence of device i in city j over a period of time;
when the more places the device appears, the more active the device i is, that is, if the device i appears in many places, the possibility that the common address of the user of the device i is a certain specific city is reduced, and this situation can be understood as that the device i often has cheating behaviors such as remote login and the like, or the user of the device i travels and travels throughout the year and the like.
In the method, a certain time period can be set to be one year or several years, the time period is prolonged, the attribute of the equipment is more conveniently depicted, and the misjudgment behavior caused by too short time period is avoided.
S12: obtaining a stability coefficient of each device according to the frequency matrix A in the S11;
s13: and determining corresponding correlation coefficients between each device and each city according to the frequency matrix A in S11 and the stability coefficients in S12, so as to obtain the correlation coefficients between the device i and the city j.
As shown in fig. 3, S12 includes constructing a city matrix B from the frequency matrix a, and then calculating an inverse-urbanization frequency for each device as a stability factor for the device.
S12 specifically includes:
s121: according to the frequency matrix A, an indication function I () is utilized to obtain a city matrix B,
Figure GDA0003627360480000061
s122: adding and taking reciprocal of each row in the matrix B to obtain the inverse urbanization frequency of each device, namely the stability coefficient S i ，
Figure GDA0003627360480000062
Wherein the denominator
Figure GDA0003627360480000063
Is the sum of all elements in row i of matrix B.
The individual difference of each device can be seen through the setting of the stability coefficient, the larger the stability coefficient is, the more stable the device is, and whether the address is commonly used or not is easier to judge.
Wherein the mapping rule of I () is that if the condition in parentheses is true, then the indication function is equal to 1, otherwise it is equal to 0. In the method, when A (i, j) >0, B (i, j) ═ 1, otherwise, B (i, j) is zero.
As shown in fig. 4, S13 includes the following steps:
s131: normalizing the frequency matrix A in S11 to obtain a probability matrix,
Figure GDA0003627360480000064
wherein the content of the first and second substances,
Figure GDA0003627360480000065
Figure GDA0003627360480000066
representing the probability of device i appearing in city j;
s132: multiplying each row of the probability matrix obtained in S131 by the stability factor S of the corresponding device obtained in S122 in turn i To obtain a matrix M of correlation coefficients,
Figure GDA0003627360480000071
wherein M is ij Represents the correlation coefficient between device i and city j, which represents the correlation between device and city j.
The larger the corresponding correlation coefficient in the correlation coefficient matrix M is, the larger the correlation between the corresponding device and the city is, and the more likely it is that the device is determined to be a common address.
The step S2 specifically includes combining the correlation coefficient matrix M obtained in S132 with M ij When compared with a threshold value k, when M ij >And k, judging the city j as the common address of the equipment i.
The invention also provides a system for estimating the common address of the mobile equipment user, and a method for estimating the common address based on the mobile equipment user comprises the following steps:
the data acquisition module is used for receiving the acquired IP address and outputting the analyzed address of the equipment;
the data processing module is used for receiving the address of the equipment output by the data acquisition module, processing the address and outputting corresponding correlation coefficients between each equipment and each city;
and the judging module is used for judging whether the city j is a common address for the equipment i or not according to the correlation coefficient output by the data processing module.
The data processing module specifically includes:
the first data processing module is used for receiving the address of the equipment output by the data acquisition module and outputting a frequency matrix A;
a second data processing module for receiving the frequency matrix A of the first data processing module and outputting a probability matrix
Figure GDA0003627360480000072
The third data processing module is used for receiving the frequency matrix A of the first data processing module, and outputting the stability coefficient of each device after constructing the city matrix B;
a fourth data processing module for receiving the probability matrix output by the second data processing module
Figure GDA0003627360480000073
And the stability coefficient of each device output by the third data processing module, and outputting a correlation coefficient matrix M.
At present, identifying a common address of a device is generally applied to an application scenario of judging abnormal login, for example, a game manufacturer judges whether an account is stolen by using the device login address, and when login occurs in a different place, verification is started, so that the security of the game account can be protected to a certain extent.
The method can obtain the corresponding frequency matrix of the account and each login place according to the login address of the account in one year or several years, obtain the probability matrix, the city matrix and the stability coefficient matrix according to the corresponding frequency matrix, finally obtain the correlation coefficient matrix, judge the constant-live address of the account, and compare the constant-live address with the current login address, thereby judging whether the embezzlement behavior is performed.
The following is a specific description of the above method by taking an example in an actual application scenario: assume that there are four devices, five cities.
First, a frequency matrix A of (4,5) is established based on the frequencies of occurrence of five cities for a period of time by three devices,
Figure GDA0003627360480000081
in the above matrix, A 31 With 5 stands for 5 occurrences of the device 3 in city 1, a 21 The 9 represents that the device 2 appears 9 times in the city 1.
Secondly, according to the obtained frequency matrix A, constructing an urban matrix B according to a method of marking 1 at a position which is not zero in the matrix A,
Figure GDA0003627360480000082
thirdly, adding and taking reciprocal of each row in the city matrix B to obtain the stability coefficient S of the corresponding equipment i ，
Coefficient of stability
Figure GDA0003627360480000083
From the above matrix, it can be seen that the stability factor of device 1 is 1, the stability factor of device 2 is 0.5, the stability factor of device 3 is 0.33, and the stability factor of device 4 is 0.2.
Then, each row of the frequency matrix A is normalized to obtain a probability matrix, the probability of each device appearing in different cities is shown,
Figure GDA0003627360480000091
in the above matrix, it can be seen that the probability of device 1 appearing in city 2 is 1, the probability of device 2 appearing in cities 1 and 2 is 0.9 and 0.1, the probability of device 3 appearing in cities 1, 2 and 3 is 0.5, 0.4 and 0.1, and the probability of device 4 appearing in 5 cities is 0.2. Finally, according to the obtained probability matrix and the stability coefficient of each device, multiplying each row of the probability matrix by the corresponding stability coefficient to obtain a correlation coefficient matrix,
Figure GDA0003627360480000092
in the above-mentioned correlation coefficient matrix, M 11 1 represents a correlation coefficient of 1 between the device 1 and the city 1, M 31 0.17 represents a correlation coefficient of 0.17 between the device 3 and the city 1.
The invention judges whether the address is a common address according to the threshold value, the selection of the threshold value is suitable for a specific scene, and the severity of the relation of the residential addresses is determined by the height of the threshold value. In general, it is reasonable to select the threshold value in the range of 0.1 to 0.3.
In the above embodiment, the correlation coefficient between the device 1 and the device 2 and the city 1 is large, specifically, the correlation coefficient between the device 1 and the city 1 is 1, and the correlation coefficient between the device 2 and the city 1 is 0.45, and within a reasonable threshold range, the permanent addresses of both the device 1 and the device 2 are determined as the city 1, and the device 1 and the device 2 belong to relatively normal devices;
the device 3 is relatively active, the selection of the threshold value greatly affects the determination of the common address of the device 3, if the threshold value is set to 0.15, only the city 1 is determined as the common address of the device 3, if the threshold value is set to 0.1, the cities 1 and 2 are both determined as the common address of the device 3, the correlation coefficient between the device 3 and the city 3 is 0.03, and the correlation coefficient is small, generally speaking, when the correlation coefficient between the device and a certain city is low, the probability that abnormal traffic data occurs is increased for a monitor, so that the registration of the device 3 in the city 3 may be determined as abnormal registration, and the generated traffic data may be determined as abnormal traffic.
Device 4 is abnormally active and therefore device 4 should not have a commonly used address. This behavior is determined to be typical of the behavior of changing the Ip brush amount, based on the frequency of occurrence of the device 4 in 5 cities and the correlation coefficient.
The above description is only a preferred embodiment of the present invention, and not intended to limit the present invention in other forms, and any person skilled in the art may apply the above modifications or changes to the equivalent embodiments with equivalent changes, without departing from the technical spirit of the present invention, and any simple modification, equivalent change and change made to the above embodiments according to the technical spirit of the present invention still belong to the protection scope of the technical spirit of the present invention.

Claims (9)

1. A method for estimating a common address of a user of a mobile device, comprising the steps of:
s1: resolving the address of the equipment from the IP address to obtain the corresponding frequency relation between the equipment and each city, and obtaining a correlation coefficient between the equipment i and the city j through calculation;
s1 specifically includes the following steps:
s11: resolving the address of the equipment according to the IP address in the advertisement exposure click record of the equipment, establishing a frequency matrix A with initialization dimensionality of (N, K) according to the corresponding frequency relation between the equipment i and the city j in a certain time period,
Figure FDA0003627360470000011
wherein each row of matrix A represents the frequency of device i in each city, each column represents the frequency of device j, A ij Represents the frequency of occurrence of device i in city j over a period of time;
s12: obtaining a stability coefficient of each device according to the frequency matrix A in S11;
s13: determining a correlation coefficient matrix between each device and each city according to the frequency matrix A in S11 and the stability coefficient in S12 to obtain a correlation coefficient between the device i and the city j;
s2: and judging whether the city j is the common address of the equipment i or not according to the correlation coefficient between the equipment i and the city j.
2. The method of claim 1, wherein the step S12 specifically comprises:
and constructing an urban matrix B according to the frequency matrix A, and then calculating the inverse urbanization frequency of each device to serve as a stability coefficient of the device.
3. The method of any one of claims 1 or 2, wherein the step S12 comprises the steps of:
s121: according to the frequency matrix A, an indication function I () is utilized to obtain a city matrix B,
Figure FDA0003627360470000021
s122: adding and taking reciprocal of each row in the matrix B to obtain the inverse urbanization frequency of each device, namely the stability coefficient S i ，
Figure FDA0003627360470000022
Wherein the denominator
Figure FDA0003627360470000023
Is the sum of all elements in row i of matrix B.
4. The method of claim 3, wherein when A (i, j) >0, then B (i, j) ═ 1, otherwise, B (i, j) is zero.
5. The method of claim 4, wherein the step of S13 comprises the steps of:
s131: normalizing the frequency matrix A in S11 to obtain a probability matrix,
Figure FDA0003627360470000024
wherein the content of the first and second substances,
Figure FDA0003627360470000025
Figure FDA0003627360470000026
representing the probability of device i appearing in city j;
s132: multiplying each row of the probability matrix obtained in S131 by the stability coefficient S of the corresponding device obtained in S122 in sequence i To obtain a matrix M of correlation coefficients,
Figure FDA0003627360470000027
wherein M is ij Represents the correlation coefficient between device i and city j, which represents the correlation between device and city j.
6. The method of claim 5, wherein the larger the correlation coefficient in the correlation coefficient matrix M, the more relevant the corresponding device is to the city, and the more likely it is to determine that the device is the popular address.
7. The method of claim 6, wherein the step of S2 comprises: combining the correlation coefficient matrix obtained in S132, and dividing M ij When compared with a threshold value k, when M ij >And k, judging the city j as the common address of the equipment i.
8. The system for estimating the common address of the mobile device user, based on the method for estimating the common address of the mobile device user as claimed in any one of claims 1 to 7, comprises:
the data acquisition module is used for receiving the acquired IP address and outputting the address of the analyzed equipment;
the data processing module is used for receiving the address of the equipment output by the data acquisition module, establishing a frequency matrix A with initialization dimensionality of (N, K) according to the corresponding frequency relation between the equipment i and the city j in a certain time period, obtaining a stability coefficient of each equipment according to the frequency matrix A, and determining a correlation coefficient matrix between each equipment and each city according to the frequency matrix A and the stability coefficient;
and the judging module is used for judging whether the city j is a common address for the equipment i or not according to the correlation coefficient matrix output by the data processing module.
9. The system of claim 8, wherein the data processing module comprises:
the first data processing module is used for receiving the address of the equipment output by the data acquisition module and outputting a frequency matrix A;
a second data processing module for receiving the frequency matrix A of the first data processing module and outputting a probability matrix
Figure FDA0003627360470000031
The third data processing module is used for receiving the frequency matrix A of the first data processing module, and outputting the stability coefficient of each device after constructing the city matrix B;
a fourth data processing module for receiving the probability matrix output by the second data processing module
Figure FDA0003627360470000032
And outputting a correlation coefficient matrix M according to the stability coefficient of each device output by the third data processing module.
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