CN110213341B - Method and device for detecting downloading of application program - Google Patents
Method and device for detecting downloading of application program Download PDFInfo
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- CN110213341B CN110213341B CN201910394078.9A CN201910394078A CN110213341B CN 110213341 B CN110213341 B CN 110213341B CN 201910394078 A CN201910394078 A CN 201910394078A CN 110213341 B CN110213341 B CN 110213341B
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Abstract
The invention provides a method and a device for detecting the downloading of an application program, wherein the method comprises the following steps: determining SDK data sent to a server by a terminal; the SDK data comprises attribute information of the terminal; and detecting whether the terminal downloads the target application program according to the SDK data. The method and the device for detecting the downloading of the application program can enable a developer of the target application program to detect whether the terminal downloads the target application program or not based on the SDK data which is reported by the terminal and is not easy to tamper and falsify through the attribute information of the terminal in the SDK data, so that whether the terminal downloads the application program or not can be detected more accurately.
Description
Technical Field
The present invention relates to network technologies, and in particular, to a method and apparatus for detecting downloading of an application program.
Background
With the continuous development of network technology and electronic technology, people can realize more and more functions through application programs on terminals, and developers of the application programs also grasp the requirement and continuously develop new application programs. Besides releasing the application program to the internet by itself, the developer of the application program usually entrusts a popularization channel to further popularize the application program, so that more terminals download and install the application program developed by the terminal.
In the prior art, a developer of an application program needs to pay and settle accounts to a provider of a popularization channel according to the number of terminals for downloading the application program by using the popularization channel. Under the driving of benefits, some inferior suppliers of popularization channels incorporate fake cheating terminals into terminals for actually downloading application programs through the action of fake terminals for downloading application programs. And report more terminal numbers to the development side of the application program to cheat more popularization rewards, thereby bringing economic loss to the development side of the application program. Therefore, after a developer obtains a terminal that downloads its application program through a popularization channel and that is reported by a provider of the popularization channel, the developer generally determines whether the terminal reported by the popularization channel actually downloads the application program based on a login behavior, a login address, or an active state of the terminal using the application program, which are recorded by the developer.
In the prior art, when a developer detects whether the terminal downloads an application program, the behavior of the terminal needs to be analyzed based on the service data of the application program used by the terminal in an application program server, so as to determine whether the terminal actually downloads the application program. However, since the behavior of the terminal is very easy to forge, especially, the terminal using the android system, once the terminal is maliciously obtained the operating system authority, the operating system parameters of the terminal can be arbitrarily modified, so that the terminal behavior is forged, and a developer can not accurately detect whether the application program is downloaded to the terminal. Therefore, how to detect whether the terminal downloads the application program more accurately is a technical problem to be solved in the field.
Disclosure of Invention
The invention provides a download detection method and a download detection device, which are based on SDK data reported by a terminal to a server, and acquire the SDK data uploaded to the server by the terminal when whether the terminal downloads a target application program or not is detected. And determining whether the terminal downloads the target application program according to the attribute information of the terminal in the SDK data. The method and the device for detecting the downloading of the application program can detect whether the terminal downloads the target application program or not based on the SDK data reported to the server by the terminal, the SDK data is directly uploaded to the server by the terminal, and DK data is not easy to tamper and forge by a popularization channel of a third party. The method and the device enable a developer of the application program to judge whether the terminal downloads the application program based on the SDK data more accurately.
The first aspect of the present invention provides a method for detecting downloading of an application program, including:
determining SDK data sent to a server by a terminal; the SDK data comprises attribute information of the terminal;
and detecting whether the terminal downloads the target application program according to the SDK data.
In a first embodiment of the first aspect of the present invention, the attribute information of the terminal includes one or more of the following: hardware information, software information, security environment information and network environment information of the terminal.
In an embodiment of the first aspect of the present invention, the detecting, according to the SDK data, whether the terminal downloads the target application includes:
and determining whether the terminal downloads the target application program according to the similarity degree of the attribute information of each terminal and the attribute information of the terminal which does not download the target application program through a machine learning algorithm.
In an embodiment of the first aspect of the present invention, before the determining the SDK data sent by the terminal to the server, the method further includes:
determining a plurality of terminals of the target application program, which are reported by a provider of a popularization channel and downloaded through the popularization channel;
the determining the SDK data sent to the server by the terminal includes:
determining SDK data sent to a server by the plurality of terminals;
the detecting whether the terminal downloads the target application program according to the SDK data comprises the following steps:
and determining whether the plurality of terminals download the target application program through the popularization channel according to the SDK data.
In an embodiment of the first aspect of the present invention, after determining whether the plurality of terminals download the target application according to the SDK data, the method includes:
and evaluating the popularization channel according to whether the target application program is downloaded by the terminals.
In an embodiment of the first aspect of the present invention, the evaluating the promotion channel includes:
determining the duty ratio of cheating information included in the attribute information of the plurality of terminals; the cheating information comprises attribute information of a terminal which does not download the target application program through the popularization channel;
and evaluating the popularization channel according to the occupancy ratio of the cheating information.
In an embodiment of the first aspect of the present invention, the determining a duty ratio of cheating information included in the attribute information of the at least one terminal includes:
sequentially calculating the duty ratio of cheating information included in each attribute information in the attribute information of the plurality of terminals;
and normalizing the duty ratio of the cheating information of each attribute information to obtain the duty ratio of the cheating information included in the attribute information of the plurality of terminals.
In an embodiment of the first aspect of the present invention, the calculating, in sequence, a duty ratio of cheating information included in each attribute information in the attribute information of the at least one terminal includes:
and determining whether the attribute information is the cheating information according to the similarity degree of each attribute information and the cheating information through a machine learning algorithm, so as to determine the duty ratio of the cheating information included in each attribute information.
In an embodiment of the first aspect of the present invention, the evaluating the promotion channel according to the occupancy ratio of the cheating information includes:
determining the range of the numerical value of the duty ratio of the cheating information and the quality evaluation grade of the corresponding popularization channel; wherein the quality evaluation grade comprises at least: the range of the numerical value of the duty ratio of the cheating information corresponds to the quality evaluation grade one by one;
and evaluating the popularization channel according to the quality evaluation grade.
A second aspect of the present invention provides an apparatus for detecting a download of an application program, for performing the method according to the first aspect of the present invention, including:
the determining module is used for determining SDK data sent to the server by the terminal; the SDK data comprises attribute information of the terminal;
and the processing module is used for detecting whether the terminal downloads the target application program according to the SDK data.
In an embodiment of the second aspect of the present invention, the attribute information of the terminal includes one or more of the following: hardware information, software information, security environment information and network environment information of the terminal.
In an embodiment of the second aspect of the present invention, the processing module is specifically configured to determine, according to attribute information of each terminal by using a machine learning algorithm, whether the terminal downloads the target application according to a similarity degree between attribute information of the terminal and attribute information of a terminal that does not download the target application.
In an embodiment of the second aspect of the present invention, the determining module is further configured to determine a plurality of terminals reported by a provider of a promotion channel, and download the target application program through the promotion channel;
the determining module is specifically configured to determine SDK data sent by the plurality of terminals to the server;
the processing module is specifically configured to determine, according to the SDK data, whether the plurality of terminals download the target application program through the promotion channel.
In an embodiment of the second aspect of the present invention, the processing module is further configured to evaluate the promotion channel according to whether the plurality of terminals download the target application.
In an embodiment of the second aspect of the present invention, the processing module is specifically configured to determine a duty ratio of cheating information included in attribute information of the plurality of terminals; the cheating information comprises attribute information of a terminal which does not download the target application program through the popularization channel; and evaluating the popularization channel according to the occupancy ratio of the cheating information.
In an embodiment of the second aspect of the present invention, the processing module is specifically configured to sequentially calculate a duty ratio of cheating information included in each of attribute information in attribute information of the plurality of terminals; and normalizing the duty ratio of the cheating information of each attribute information to obtain the duty ratio of the cheating information included in the attribute information of the plurality of terminals.
In an embodiment of the second aspect of the present invention, a machine learning algorithm determines whether the attribute information is cheating information according to a similarity degree between each attribute information and the cheating information, so as to determine a duty ratio of the cheating information included in each attribute information.
In an embodiment of the second aspect of the present invention, determining a range in which a numerical value of the duty ratio of the cheating information is located, and a quality evaluation level of a corresponding popularization channel; wherein the quality evaluation grade comprises at least: the range of the numerical value of the duty ratio of the cheating information corresponds to the quality evaluation grade one by one; and evaluating the popularization channel according to the quality evaluation grade.
A third aspect of the present invention provides an electronic apparatus, comprising: a processor, a memory and a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor, the computer program comprising instructions for performing the method according to any of the first aspects of the invention.
A fourth aspect of the invention provides a computer readable storage medium storing a computer program which, when executed, implements a method according to any one of the first aspects of the invention.
In summary, the present invention provides a method and an apparatus for detecting downloading of an application program, where the method includes: determining SDK data sent to a server by a terminal; the SDK data comprises attribute information of the terminal; and detecting whether the terminal downloads the target application program according to the SDK data. The method and the device for detecting the downloading of the application program can realize the detection of whether the terminal downloads the target application program or not based on the SDK data reported to the server by the terminal, wherein the SDK data is directly uploaded to the server by the terminal, and the DK data is not easy to tamper and forge by a popularization channel of a third party. The method and the device enable a developer of the application program to judge whether the terminal downloads the application program based on the SDK data more accurately.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a schematic view of a scenario of application promotion in the prior art;
fig. 2 is a schematic structural diagram of a system to which the method for detecting downloading of an application program according to the present invention is applied;
FIG. 3 is a flowchart illustrating an embodiment of a method for detecting a download of an application program according to the present invention;
FIG. 4 is a flowchart illustrating an embodiment of a method for detecting a download of an application program according to the present invention;
FIG. 5 is a flowchart illustrating an embodiment of a method for detecting a download of an application program according to the present invention;
FIG. 6 is a schematic structural diagram of an embodiment of an application program download detection apparatus according to the present invention;
fig. 7 is a schematic structural diagram of an embodiment of an electronic device according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented, for example, in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Before formally describing the method and the device for detecting the downloading of the application program, the scene applied by the invention and the problems existing in the prior art are described with reference to fig. 1.
Fig. 1 is a schematic view of a scenario of application promotion in the prior art, as shown in fig. 1, in the prior art, after a developer of a program develops an application 1 (APP), the developer may issue the application 1 through the internet 3, for example, by a server 2 of the developer. A user who needs to use the application 1 is enabled to download the application 1 from the developer's server 2 through the path (1) in fig. 1 using the terminal 4 connected to the internet 3, and use the application 1 on the terminal 4.
In order to enable more terminals 4 to install the application 1 developed by the developer of the application 1, the developer of the application 1 may request the popularization channel 5 to popularize the application 1. The promotion channel 5 described herein may be an advertiser that causes other terminals to install the application 1 by publishing links in the internet content; alternatively, the promotion channel 5 may be software installed in the terminal and specially used for managing application programs, such as mobile phone assistant software and the like. As shown in fig. 1, after receiving the promotion request of the application 1, the provider of the promotion channel 5 can issue the application 1 in the internet, so that the terminal 4 accessing the internet 3 can download the application from the promotion channel 5 and use the application 1 on the terminal 4 through the path (2) in fig. 1. Thus, the promotion channel 5 realizes promotion of the application 1, so that the application 1 can be downloaded and used by more terminals through more channels.
In the prior art, a developer of an application 1 needs to pay and settle a payment to a provider of a promotion channel 5 according to the number of terminals 4 that use the promotion channel 5 to download its application 1. Under the driving of benefit, some bad suppliers of popularization channels 5 incorporate fake cheating terminals into terminals for actually downloading application programs through the action of fake terminals 4 for downloading application programs. And reports more terminals 4 to the developer of the application 1 to cheat more popularizing rewards, thereby bringing economic loss to the developer of the application 1. Therefore, the developer of the application 1 needs to determine whether the terminal actually downloads the application after acquiring the terminal of the application 1 downloaded through the popularization channel 5, which is reported by the provider of the popularization channel 5. With the prior art, when a developer detects whether the terminal 4 downloads an application program, it is necessary to analyze the behavior of the terminal 4 based on the service data of the application program 1 used by the terminal 4 in the server 2 corresponding to the application program 1, so as to determine whether the terminal 4 actually downloads the application program 1.
However, since the behavior of the terminal 4 is very easy to forge, especially, the terminal 4 using the android system, once the operating system authority is maliciously obtained, the operating system parameters of the terminal 4 can be arbitrarily modified, so that the behavior of the terminal 4 is forged, and the service data recorded in the server 2 may come from the forged terminal 4 instead of the terminal actually downloading the application program 1, which further causes that a developer cannot accurately detect whether the application program is downloaded by the terminal.
Therefore, the invention provides a method and a device for detecting the downloading of an application program, aiming at the problems in the prior art, and when the terminal needs to detect whether the target application program is downloaded or not, SDK data uploaded to a server by the terminal is obtained. And determining whether the terminal downloads the target application program according to the attribute information of the terminal in the SDK data. The method and the device for detecting whether the terminal downloads the target application program are realized based on the application program, and the SDK data are directly uploaded to the server by the terminal and are not easy to tamper and forge by a popularization channel of a third party, so that a developer of the application program can more accurately detect whether the terminal downloads the application program based on the SDK data.
Specifically, fig. 2 is a schematic structural diagram of a system to which the method for detecting downloading of an application program according to the present invention is applied, where the system shown in fig. 2 further includes, on the basis of the system shown in fig. 1: a server 6 for receiving and storing SDK data including terminal attribute information transmitted from the terminal 4 via the internet. The server 2 of the developer of the application 1 can acquire SDK data of the terminal 4 in the server 6 through the internet. And for each of the terminals 4 of the system shown in fig. 2, after determining the attribute information of the terminal, the attribute information is transmitted to the server 6 as SDK data.
The following details of the method for detecting the downloading of the application program in this embodiment are described with reference to fig. 3, fig. 4 and fig. 5, and the following specific embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. The execution subject of the method shown in fig. 3 and fig. 4 may be the server 2 shown in fig. 2, and the execution subject of the method shown in fig. 5 may be any terminal 4 shown in fig. 2.
Fig. 3 is a flowchart of an embodiment of a method for detecting downloading of an application program according to the present invention, as shown in fig. 3, where the method provided in the embodiment includes:
s101: determining SDK data sent to a server by a terminal; wherein the SDK data comprises attribute information of the terminal.
Specifically, the present embodiment is applied to the server 2 of the developer of the application 1 shown in fig. 2 detecting whether the terminal 4 actually downloads the target application, the server 2 first determines SDK data sent from the terminal 4 to the server 6 in S101. Wherein, the server 6 and the server 2 may be different servers deployed in the internet; alternatively, the server 6 and the server 2 may perform different functions from each other by the same server.
Alternatively, the terminal 4 determined in this embodiment may be reported by the promotion channel 5, and the terminal 4 of the application 1 is downloaded through the promotion channel 5. At this time, the server 2 of the developer of the application 1 needs to detect whether the terminal 4 has actually downloaded the application 1 through the promotion channel 5.
Alternatively, in order to enable the terminal to actively acquire the SDK data and send the SDK data to the server 6, a developer of the application 1 may add an instruction to the application 1 to enable the terminal to report the SDK data by itself, for example, in the form of a plug-in when the application is developed. The terminal downloaded with the application program 1 can acquire the attribute information of the terminal according to the indication of the plug-in unit, and then add the attribute information into the SDK data and send the SDK data to the server 6.
Alternatively, the terminal may send the SDK data to the server 6 at preset time intervals, for example, the terminal sends the SDK data to the server 6 in the early morning every day when idle, and the server 6 stores the received SDK data. When the server 2 acquires the SDK data of the terminal 4 in the server 6, it is the SDK data that the terminal has uploaded to the server 6 that should be acquired.
S102: and detecting whether the terminal downloads the target application program according to the SDK data.
Subsequently, the server 2 may detect whether the terminal downloads the target application, i.e., the application 1 in the example shown in fig. 2, according to the attribute information of the terminal in the SDK data acquired in S101. The attribute information of the terminal provided in this embodiment specifically includes one or more of the following: the hardware information, software information, security environment information, and network environment information of the terminal 4 are described below, respectively.
The hardware information includes: identification Information (ID) of the terminal, international Mobile Equipment Identification (IMEI) of the terminal, medium access control address (Media Access Control Address, MAC) of the terminal, user identity (Subscriber Identification Module, SIM) card, and information such as model number, production lot of the terminal when the terminal is a mobile phone or a tablet computer. The hardware information of the terminal cannot be forged due to the uniqueness of the hardware information, so that the terminal reports the hardware information to the server through the SDK data, and the terminal can be uniquely identified when judging whether the terminal downloads the application program through the popularization channel or not.
The software information includes: the version of the operating system used by the terminal and whether the terminal has specified software installed. For example, the version of the operating system may be a version of an iOS operating system or an android operating system. From the aspect of security, the terminal may report to the server through the SDK data whether the terminal installs the designated software, for example: if a certain piece of tampered software can be used for modifying the network address of the terminal, after the terminal reports the tampered software to the server, the terminal can be enabled to be modified due to the existence of the tampered software when judging whether the terminal downloads the application program through the popularization channel or not, so that the terminal can be judged that the application program is not actually downloaded through the popularization channel. Or, for example, the designated software refers to chat software, weather software, or shopping class software that is commonly used, and considering that a terminal, if it installs the designated software, may be considered as a terminal actually used by a user or may be referred to as an "active user". When judging whether the terminal downloads the application program through the popularization channel or not later, the terminal confirms that the downloading behavior of the terminal is real, but not the false cheating downloading behavior of the abandoned terminal or the idle terminal.
The secure environment information includes: whether the rights of the operating system of the terminal are tampered with, the act of tampering with the operating system rights may be referred to as "root". The root refers to the highest management authority in the operating system, after the operating system of the terminal is subjected to the root, the root is not constrained by various management mechanisms of the operating system, and various operating system parameters of the terminal can be modified by a user. Therefore, in this embodiment, the terminal needs to report the information about whether the terminal is root through the SDK data, and when judging whether the terminal downloads the application program through the popularization channel later, consider that the behavior of downloading the application program through the popularization channel for the terminal after the root may be a false cheating behavior.
The network environment information includes: an internet protocol (Internet Protocol, abbreviated as IP) address of the terminal accessing the internet, whether the IP address is tampered with, whether the terminal uses a virtual private network (Virtual Private Network, abbreviated as VPN), etc. The IP address used by the terminal can be used for judging whether the terminal downloads the application program through the popularization channel, and if the IP address of the terminal is artificially modified, the behavior that the terminal downloads the application program through the popularization channel by using the IP address can be judged to be false cheating behavior.
Alternatively, in one possible implementation manner of S102 of this embodiment, the server may specifically determine, according to the attribute information of each terminal through a machine learning algorithm, whether the terminal downloads the target application according to the similarity degree with the attribute information of the terminal that does not download the target application.
For example, if the attribute information of the terminal specifically includes the security environment information, the attribute information of the terminal acquired in S101 may be: the terminal is shot, the machine learning model in the machine learning algorithm is obtained by learning whether the terminal without downloading the target application program is shot or not, so that the server in S102 can determine that the terminal does not download the target application program by comparing the shot safety environment information of the terminal acquired in S101 with the safety environment information of the terminal without downloading the target application program in the machine learning model, if the two safety environment information are identical, and if the two safety environment information are different, the terminal downloads the target application program. As another example, if the attribute information of the terminal includes hardware information, software information, security environment information, and network environment information, the attribute information of the terminal acquired in S101 may be: the MAC address of the terminal, the operating system of the terminal, the root of the terminal, and the IP address of the terminal, the server may also compare the acquired attribute information of the terminal with the security environment information of the terminal in which the target application is not downloaded in the machine learning model in S102, since the machine learning model determines whether the terminal downloads the target application program according to the similarity between the MAC address of the terminal, the operating system of the terminal, the root of the terminal and the IP address of the terminal, which are not downloaded with the target application program.
Wherein the machine learning algorithm may use commonly used techniques such as: a K-Nearest Neighbor algorithm (KNN), a support vector machine (Support Vector Machine, SVM) or a deep learning algorithm, and the like. After the server can conduct feature learning in advance through the attribute information of the terminal which does not download the target application program and obtain a machine learning feature model, the attribute information of the terminal to be detected is subjected to feature extraction through a machine learning algorithm and is compared with the machine learning feature model, and whether the attribute information of the terminal to be detected downloads the target application program is determined according to the output similarity degree. The algorithm implementation and principle of the machine learning detection can refer to the prior art, and the invention is not particularly limited.
In summary, in the method for detecting downloading of an application program provided in this embodiment, when it is required to detect whether a terminal downloads a target application program, SDK data uploaded to a server by the terminal is obtained. And determining whether the terminal downloads the target application program according to the attribute information of the terminal in the SDK data. The attribute information of the terminal in this embodiment includes: hardware information, software information, security environment information and network environment information, the attribute information based on the terminal characteristics is difficult to tamper and forge, and compared with a detection method based on terminal behaviors in the prior art, the detection method based on the terminal behaviors has higher accuracy and uniqueness. In addition, since the SDK data used when the terminal is used for detecting whether the terminal downloads the target application program is realized based on the application program, the SDK data is directly uploaded to the server by the terminal, and the popularization channel of the third party is not easy to tamper and forge, so that a developer of the application program can more accurately detect whether the terminal downloads the application program based on the SDK data.
Fig. 4 is a flowchart illustrating an embodiment of a method for detecting downloading of an application program according to the present invention. The embodiment shown in fig. 4 provides a specific application method for detecting whether the terminal downloads the target application program on the basis of the embodiment shown in fig. 3, wherein when the method is applied to the system shown in fig. 2, the server 2 of the developer of the application program 1 can evaluate the popularization channel 5. Specifically, as shown in fig. 3, the method provided in this embodiment includes:
s201 the method comprises the following steps: and determining a plurality of terminals of the target application program which are reported by the suppliers of the popularization channels and are downloaded through the popularization channels.
Specifically, in order to evaluate the promotion channel 5, the server 2 first determines a plurality of terminals 4 through which the targeted application program is downloaded via the promotion channel 5, which are reported by the provider of the promotion channel 5. The target application is an application designated by a developer corresponding to the server 2, that is, the application 1 in fig. 2.
Alternatively, the promotion channel 5 shown in fig. 2 may send a terminal list to the server 2 at preset time intervals; alternatively, the promotion channel 5 may transmit the terminal list to the server 2 when settling the promotion of the application 1 with the server. The terminal list includes identification information of the plurality of terminals 4, for example, information such as serial numbers of the terminals, time for downloading the target application program through the popularization channel 5, and the like, so that after the server 2 determines the plurality of terminals 4 according to the terminal list, it determines whether the plurality of terminals 4 download the application program 1 through the popularization channel 5 through subsequent steps.
S202: acquiring SDK data sent to a server by the plurality of terminals; the SDK data includes attribute information of the terminal.
Specifically, as shown in fig. 2, after the server 2 determines the plurality of terminals 4 reported by the promotion channel 5 through S101, SDK data sent from the plurality of terminals 4 to the server 6 is acquired. The manner in which the server 2 acquires the SDK data from the server 6 may refer to S101, except that the SDK data of the plurality of terminals 4 are acquired in S202.
S203: and evaluating the popularization channels according to the SDK data of the plurality of terminals.
Subsequently, the server 2 as shown in fig. 2 evaluates the promotion channel based on the SDK data of the multi-terminal 4 acquired from the server 6 in S102.
Alternatively, in S103, the server 2 may determine whether the plurality of terminals 4 have downloaded the application 1 from the promotion channel 5 through attribute information of the plurality of terminals 4, and evaluate the promotion channel according to the proportion of the cheating terminals among the plurality of terminals 4. The cheating terminal refers to a terminal which is reported by a provider of a popularization channel in S101 and downloads the application program 1 through the popularization channel, but the terminal does not actually download the application program 1 through the popularization channel.
Optionally, the evaluation of the promotion channels in this embodiment may be to classify the promotion channels, for example, classifying the promotion channels into a high-quality channel, a suspected channel and a risk channel. And after the popularization channel is evaluated, the evaluation result can be pushed to relevant management personnel of the application program. Therefore, the manager can acquire the evaluation result of the popularization channel, and decide whether to continue entrusting the popularization channel to popularize the application program according to the evaluation.
Further, on the basis of the embodiment shown in FIG. 3 described above, the invention also provides a specific implementation method for evaluating the popularization channel, wherein the step S203 specifically comprises the following steps:
s2031: determining the duty ratio of cheating information included in the attribute information of the plurality of terminals; the cheating information comprises attribute information of a terminal which does not download the target application program through the popularization channel.
S2032: and evaluating the popularization channel according to the occupancy ratio of the cheating information.
Specifically, in the specific implementation method provided in this embodiment, the server as the execution subject of this embodiment first determines, through S2031, the occupancy rate of the cheating information included in each attribute information according to the attribute information in the SDK data of the plurality of terminals, and then evaluates, through S1032, the popularization channel according to the occupancy rate of the cheating information in all the attribute information.
Optionally, in this embodiment, the server may perform calculation of a ratio of the cheating information to each of the attribute information of all the plurality of terminals, and specifically calculate, in sequence, the ratio of the cheating information included in each of the attribute information of the plurality of terminals. For example, after acquiring the SDK information of 1000 terminals, the server processes each attribute information in the SDK information of 1000 terminals, respectively. The ratio of the cheating information in the hardware information of 1000 terminals, the ratio of the cheating information in the software information of 1000 terminals, the ratio of the cheating information in the security environment information of 1000 terminals and the ratio of the cheating information in the network environment information of 1000 terminals can be respectively determined. And finally, normalizing the duty ratio of the obtained cheating information of the four attribute information to obtain the duty ratio of the cheating information included in the attribute information of the plurality of terminals. Optionally, one possible implementation manner of the normalization processing described in this embodiment is that a value obtained by adding the ratios of the cheating information in the four attribute information is used as a total ratio of the cheating information included in the attribute information of the plurality of terminals; alternatively, in another possible implementation manner of normalization processing, the four attribute information may be given different weights and then added in a weighted manner, for example, software information capable of evaluating the popularization channel more accurately is given a larger weight, and hardware information is given a smaller weight and added.
Alternatively, in the above-described embodiment, the duty ratio of the cheating information included in each attribute information may be calculated by means of machine learning. For example, the server may determine whether the attribute information is the cheating information according to the similarity degree of each attribute information and the cheating information through a machine learning algorithm, thereby determining the duty ratio of the cheating information included in each attribute information. The server can acquire a machine learning model according to the training of the cheating information, judge each attribute information according to the machine model, determine whether the attribute information is the cheating information according to the similarity degree of each attribute information and the cheating information, and determine the duty ratio of the cheating information. For example, machine learning algorithms that can be used in the present embodiment include, but are not limited to: the principle of classifying and identifying whether the attribute information is cheating information by using a K-Nearest Neighbor algorithm (KNN), a support vector machine (Support Vector Machine, SVM) or a deep learning algorithm and the like can refer to the prior art, and is not repeated. More specifically, in this embodiment, the classification and judgment can be performed on whether the attribute information is the cheating information by using an iterative decision tree algorithm (Gradient Boosting Decision Tree, GBDT) algorithm machine learning algorithm.
For example: the server firstly acquires cheating information of 500 terminals which do not download application programs through a popularization channel, wherein the cheating information comprises four attribute information of hardware information, software information, security environment information and network environment information of the 500 terminals. The server learns the cheating information of the 500 terminals through the GBDT algorithm to obtain a GBDT weight calculation model, wherein the GBDT weight calculation model comprises the characteristics of the cheating information of the 500 terminals. Then, when determining whether the attribute information of the terminal is the cheating information in S2031, the server performs feature extraction on the attribute information of the terminal through the trained GBDT weight calculation model including the characteristics of the cheating information, and determines whether the attribute information to be determined is the cheating information after performing comparison. In this embodiment, the algorithm principle and implementation manner of applying the GBDT algorithm to the classification and identification can refer to the application of the GBDT algorithm in the classification and identification in the prior art, the cheating information is identified as a training sample, then the training is performed through the GBDT algorithm, and the sample to be identified is used for identification to output whether the sample to be identified is a cheating sample, so that the principle is not repeated. For example: the software information in which the tampered software is installed in the attribute information may be marked as 1, and the software information in which the tampered software is not installed in the attribute information may be marked as 0. After the GBDT weight calculation model is obtained through learning the information of 500 terminals, the attribute information marked as 1 to be identified is sent into the GBDT weight calculation model for classification calculation, and the GBDT algorithm is not limited in detail.
Further, after the server determines the ratio of total cheating information in all attribute information of the plurality of terminals through S2031, the quality evaluation level of the corresponding popularization channel is determined continuously through S2032 according to the range of the numerical value of the ratio of the cheating information; wherein, the quality evaluation grade at least comprises: the range of the numerical value of the duty ratio of the cheating information corresponds to the quality evaluation grade one by one; and thereby, the popularization channel is evaluated according to the determined quality evaluation grade.
For example, in a specific implementation of this embodiment, the threshold value may be set to 0.7 between the high-quality channel and the suspected channel of the quality evaluation level, and the threshold value may be set to 0.9 between the suspected channel and the risk channel. Determining that the evaluation level of the popularization channel is a risk channel when the ratio value of the cheating information determined in the S1031 is larger than or equal to 0.9; when the proportion value of the cheating information is smaller than 0.9 and larger than 0.7, determining the evaluation grade of the popularization channel as a suspected channel; when the proportion value of the cheating information is smaller than 0.7, the evaluation grade of the popularization channel is determined to be a high-quality channel.
In summary, in the method for detecting downloading of an application program provided in this embodiment, after a popularization channel reports that a plurality of terminals download the application program through the popularization channel, SDK data including terminal attribute information uploaded by the plurality of terminals to a server is obtained. And determining the proportion of terminals actually downloading the application program through the popularization channel in the plurality of terminals according to the attribute information of the terminals in the SDK data so as to evaluate the popularization channel of the application program. Therefore, the popularization channel for popularizing the application program can be evaluated, so that a developer of the subsequent application program can determine whether the popularization channel can be continuously entrusted for popularizing the application program through the evaluation result of the popularization channel. In addition, since the terminal attribute information used when evaluating the popularization channel is based on the behavior of actually using the terminal by the user, the forging and tampering cost is high; meanwhile, the SDK data used in the embodiment is realized based on the application program, and the popularization channel of the third party is not easy to tamper and forge. In addition, the popularization channel evaluation method provided by the embodiment does not need to rely on service data of an application program, and only starts with attribute information of equipment, so that portability is high, and popularization and use cost can be saved. In conclusion, the embodiment can enable the server to judge whether the terminal downloads the application program through the popularization channel more accurately and safely, and finally enable the server to accurately evaluate the application program popularization channel.
Fig. 5 is a flow chart of an embodiment of a method for detecting downloading of an application program according to the present invention, where an execution body of the embodiment may be any terminal in the system shown in fig. 2, and as shown in fig. 5, the method provided in the embodiment includes:
s301: the terminal determines SDK data, wherein the SDK data comprises: and the SDK data are used for evaluating the popularization channel of the target application program.
Specifically, the present embodiment is applicable to a system as shown in fig. 2, which is executed by any one of the at least one terminal 4. Wherein the terminal 4 first determines attribute information of the terminal through S301 and serves as SDK data to transmit the SDK data to the server 6 through the subsequent steps.
Optionally, attribute information of the terminal in this embodiment includes: hardware information, software information, security environment information and network environment information of the terminal. For the specific content of the attribute information, reference may be made to the description in the embodiment shown in fig. 3, and no further description is given.
Optionally, in one possible implementation manner of S301, in order to enable the terminal to actively acquire and send the SDK data to the server, a developer of the application program may add an instruction to the application program, where the instruction exists in the form of a plug-in, to enable the terminal to report the SDK data by itself, when the application program is developed. The method shown in fig. 5 is executed at intervals of preset time according to the indication of the plug-in after the terminal installs the application program, for example, the terminal determines SDK data of the terminal and sends the SDK data to the server when the terminal is idle, for example, in the early morning time of day according to the indication of the plug-in.
Or, alternatively, in another possible implementation manner of S301, after the terminal downloads the target application program for the first time, the terminal performs a flow as shown in fig. 5, that is, determines the SDK data and sends the SDK data to the server.
S302: and the terminal sends the SDK data to a server.
Subsequently, in S302, the terminal transmits the SDK data determined in S301 to the server. Thus, the system shown in fig. 2 can be applied, so that after the server 6 receives and stores the SDK data transmitted from the terminal 4, the server 2 can acquire the SDK data of the terminal 4 from the server 6 when the terminal 4 detects whether to download the application 1.
In summary, the method for detecting the download of an application program provided in this embodiment can be applied to a system as shown in fig. 2, and after determining SDK data including attribute information of a terminal 4 for the terminal 4 that downloads the application program 1, the SDK data is sent to a server 6. So that the server 2 can acquire the SDK data of the terminal 4 from the server 6 and detect according to the attribute information of the terminal 4 in the SDK data when detecting whether the terminal 4 downloads the application 1 later. Therefore, in the method for detecting the download of the application program provided in the embodiment, since the SDK data sent from the terminal to the server by the embodiment is forged and tampered by a third party, the cost is high; meanwhile, the SDK data used in the embodiment is realized based on the application program, so that the popularization channel of the third party is not easy to tamper and forge. In addition, in the method provided by the embodiment, the terminal can actively acquire the information, the server is not required to passively detect the attribute information of the terminal when the server needs to detect whether the terminal 4 downloads the application program, and the detection efficiency when detecting whether the terminal downloads the application program is also improved.
Fig. 6 is a schematic structural diagram of an embodiment of an application program download detection apparatus according to the present invention, where the apparatus shown in fig. 6 may be used to perform a method shown in fig. 3 or fig. 4, and the apparatus includes: the determination module 601 processes the module 602. The determining module 501 is configured to determine SDK data sent by a terminal to a server; the SDK data comprises attribute information of the terminal; the processing module 602 is configured to detect whether the terminal passes the download of the target application according to the SDK data.
The application program download detection device provided in this embodiment may be used to execute the application program download detection method shown in fig. 3, and its implementation manner and principle are the same and will not be described again.
Optionally, the attribute information of the terminal includes one or more of the following: hardware information, software information, security environment information and network environment information of the terminal.
Alternatively, the processing module 602 is specifically configured to determine, according to the attribute information of each terminal by using a machine learning algorithm, whether the terminal downloads the target application according to the similarity degree of the attribute information of the terminal that does not download the target application.
Optionally, the determining module 601 is further configured to determine a plurality of terminals of the target application program that are reported by the provider of the promotion channel and are downloaded through the promotion channel; the determining module 601 is specifically configured to determine SDK data sent by the plurality of terminals to the server; the processing module 602 is specifically configured to determine whether the plurality of terminals download the target application program through the promotion channel according to the SDK data.
Optionally, the processing module 602 is further configured to evaluate the promotion channel according to whether the target application program is downloaded by the plurality of terminals.
Optionally, the processing module 602 is specifically configured to determine a duty ratio of the cheating information included in the attribute information of the plurality of terminals; the cheating information comprises attribute information of a terminal which does not download the target application program through a popularization channel; and evaluating the popularization channel according to the occupation ratio of the cheating information.
Optionally, the processing module 602 is specifically configured to sequentially calculate a duty ratio of cheating information included in each attribute information in attribute information of a plurality of terminals; and normalizing the duty ratio of the cheating information of each attribute information to obtain the duty ratio of the cheating information included in the attribute information of the plurality of terminals.
Optionally, the processing module 602 is specifically configured to determine, by using a machine learning algorithm, whether the attribute information is cheating information according to a similarity degree between each attribute information and the cheating information, so as to determine a duty ratio of the cheating information included in each attribute information.
Optionally, the processing module 602 is specifically configured to determine a range in which a numerical value of a duty ratio of the cheating information is located, and a quality evaluation level of a corresponding popularization channel; wherein, the quality evaluation grade at least comprises: the range of the numerical value of the duty ratio of the cheating information corresponds to the quality evaluation grade one by one; and evaluating the popularization channel according to the quality evaluation grade.
The application program download detection device provided in this embodiment may be used to execute the application program download detection method shown in the foregoing embodiment, and its implementation manner is the same as the principle, and will not be described again.
The application program download detection device provided in this embodiment may be used to execute the application program download detection method shown in fig. 3, and its implementation manner and principle are the same and will not be described again.
The division of the modules in the embodiments of the present application is schematically only one logic function division, and there may be another division manner in actual implementation, and in addition, each functional module in each embodiment of the present application may be integrated in one processor, or may exist separately and physically, or two or more modules may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules.
Fig. 7 is a schematic structural diagram of an embodiment of an electronic device according to the present invention; fig. 7 shows a schematic diagram of an electronic device that can be used to carry out the method shown in fig. 3 or fig. 4 or as an apparatus shown in fig. 6 to carry out the method shown in fig. 3 or fig. 4.
As shown in fig. 7, an electronic device 700 provided in this embodiment includes: transceiver 710, memory 730, and processor 720. Memory 730 may be a separate physical unit coupled to processor 720 via bus 740. The memory 730, the processor 720 may also be integrated together, implemented by hardware, etc. The memory 730 is used to store a computer program implementing the above method embodiments, which the processor 740 invokes, and the transceiver 710 communicates through an interface to perform the operations of the above method embodiments.
Alternatively, when part or all of the methods of the above embodiments are implemented by software, the electronic device 700 may include only a processor. The memory for storing the program is located outside the electronic device 700 and the processor is connected to the memory via circuitry/wires for reading and executing the computer program stored in the memory. Processor 720 may be a central processor (Central Processing Unit, CPU), a network processor (Network Processor, NP) or a combination of CPU and NP. Processor 720 may further include a hardware chip. The hardware chip may be an Application-specific integrated circuit (ASIC), a programmable logic device (Programmable Logic Device, PLD), or a combination thereof. The PLD may be a complex programmable logic device (Complex Programmable Logic Device, CPLD), a Field programmable gate array (Field-Programmable Gate Array, FPGA), general array logic (Generic Array Logic, GAL), or any combination thereof. Memory 730 may include Volatile Memory (Volatile Memory), such as Random-Access Memory (RAM); the Memory may also include a Non-volatile Memory (Non-volatile Memory), such as a Flash Memory (Flash Memory), a Hard Disk (HDD) or a Solid State Drive (SSD); the memory may also comprise a combination of the above types of memories.
Illustratively, when the electronic device 700 as shown in fig. 7 is used to perform the method as shown in fig. 3, the electronic device 700 may determine SDK data transmitted from the terminal to the server through its transceiver 710; and detects whether the terminal passes the download of the target application according to the SDK data through its processor 720. The specific implementation method can refer to the description of the embodiment shown in fig. 3, and will not be repeated.
In addition, the present invention also provides a program product, for example, a computer-readable storage medium, including: computer program which, when executed, is adapted to carry out the method according to any one of the preceding claims.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the method embodiments described above may be performed by hardware associated with program instructions. The foregoing program may be stored in a computer readable storage medium. The program, when executed, performs steps including the method embodiments described above; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (5)
1. A download detection method of an application program, comprising:
determining a plurality of terminals of the target application program, which are reported by a provider of a popularization channel and downloaded through the popularization channel;
determining SDK data sent to a server by the plurality of terminals; the SDK data comprises attribute information of the terminal; wherein the attribute information includes: the terminal is provided with an instruction in an application program downloaded by the terminal, wherein the instruction is used for enabling the terminal to send the SDK data to the server after the application program is downloaded; the SDK data is determined by the terminal;
comparing the attribute information of each terminal with the attribute information of the terminal which does not download the target application program in a machine learning model in a machine learning algorithm aiming at any one item of attribute information in hardware information, software information, security environment information and network environment information in the attribute information, if the attribute information is the same with the attribute information of the terminal, determining that the attribute information of the terminal is cheating information, and if the attribute information is different from the cheating information, determining that the attribute information of the terminal is not the cheating information; the machine learning model is obtained by performing feature learning on attribute information of a terminal which does not download the target application program;
Determining the proportion of cheating information in hardware information of the plurality of terminals, the proportion of cheating information in software information of the plurality of terminals, the proportion of cheating information in security environment information of the plurality of terminals and the proportion of cheating information in network environment information of the plurality of terminals respectively; the cheating information comprises attribute information of a terminal which does not download the target application program through the popularization channel;
giving different weights to each attribute information of each terminal in the plurality of terminals, and then carrying out weighted addition to obtain the duty ratio of the cheating information included in the attribute information of the plurality of terminals; wherein the weight of the software information is larger than the weight of the hardware information;
determining the quality evaluation grade of a popularization channel corresponding to the range of the numerical value of the ratio of the cheating information; wherein the quality evaluation grade comprises at least: the range of the numerical value of the duty ratio of the cheating information corresponds to the quality evaluation grade one by one;
and evaluating the popularization channel according to the quality evaluation grade.
2. The method of claim 1, wherein determining the duty cycle of the cheating information in the hardware information of the plurality of terminals, the duty cycle of the cheating information in the software information of the plurality of terminals, the duty cycle of the cheating information in the security environment information of the plurality of terminals, and the duty cycle of the cheating information in the network environment information of the plurality of terminals, respectively, comprises:
For any one item of attribute information of hardware information, software information, security environment information and network environment information in the attribute information, determining whether the attribute information is cheating information or not according to the similarity degree of each item of attribute information and the cheating information through a machine learning algorithm, so as to determine the duty ratio of the cheating information included in each item of attribute information.
3. An application download detection apparatus, comprising:
the determining module is used for determining a plurality of terminals for downloading the target application program through the popularization channel, wherein the terminals are reported by a supplier of the popularization channel;
determining SDK data sent to a server by the plurality of terminals; the SDK data comprises attribute information of the terminal; wherein the attribute information includes: the terminal is provided with an instruction in an application program downloaded by the terminal, wherein the instruction is used for enabling the terminal to send the SDK data to the server after the application program is downloaded; the SDK data is determined by the terminal;
the processing module is used for comparing the attribute information of each terminal with the attribute information of the terminal which does not download the target application program in a machine learning model in a machine learning algorithm aiming at any one item of attribute information in hardware information, software information, security environment information and network environment information in the attribute information, if the attribute information and the attribute information are the same, determining that the attribute information of the terminal is cheating information, and if the attribute information and the attribute information are different, determining that the attribute information of the terminal is not the cheating information; the machine learning model is obtained by performing feature learning on attribute information of a terminal which does not download the target application program;
The processing module is specifically configured to determine a ratio of the cheating information in the hardware information of the plurality of terminals, a ratio of the cheating information in the software information of the plurality of terminals, a ratio of the cheating information in the security environment information of the plurality of terminals, and a ratio of the cheating information in the network environment information of the plurality of terminals, respectively; the cheating information comprises attribute information of a terminal which does not download the target application program through the popularization channel; giving different weights to each attribute information of each terminal in the plurality of terminals, and then carrying out weighted addition to obtain the duty ratio of the cheating information included in the attribute information of the plurality of terminals, wherein the weight of the software information is larger than that of the hardware information; determining the quality evaluation grade of a popularization channel corresponding to the range of the numerical value of the ratio of the cheating information; wherein the quality evaluation grade comprises at least: the range of the numerical value of the duty ratio of the cheating information corresponds to the quality evaluation grade one by one;
and evaluating the popularization channel according to the quality evaluation grade.
4. An electronic device, comprising:
A processor, a memory and a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor, the computer program comprising instructions for performing the method of any of claims 1-2.
5. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed, implements the method according to any of claims 1-2.
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