CN112905301A - Detection method and device for Android simulator - Google Patents
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Abstract
The invention discloses a method and a device for detecting an Android simulator, which are characterized by firstly judging whether current equipment is the Android simulator or not according to whether equipment information of the current equipment contains a simulation value or whether a value is modified or not; then judging whether the current equipment is an Android simulator or not according to whether the hardware information of the current equipment contains an analog value or has a normal function or not; and finally, integrating the equipment information, the hardware information, the special file information and the user trace information of the current equipment, inputting the generated characteristic vector into a detection model, and judging whether the current equipment is an Android simulator. The invention firstly adopts a portable and rapid judgment method to ensure the detection efficiency of a common simulator, then combines special file information which is difficult to simulate and user trace information to construct a characteristic vector, and uses a machine learning method, thereby improving the detection accuracy.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for detecting an Android simulator.
Technical Field
An android simulator is a simulator that can run on a computer and simulate an android phone system. The android simulator can install, use and uninstall android mobile phone application software. The android simulator can simulate the android mobile phone running environment on the computer, and even if a user does not have mobile phone hardware equipment, the user can use android mobile phone application software on the computer by using the android simulator.
The development of android simulators also brings some adverse effects. For example, data monitoring, account number stealing, hacking, malicious registration, false authentication, ticket swiping, cash registering, loan application, wool of the seller, etc. are performed by using an android simulator. In the current android simulator detection scheme, whether the operating environment of the android phone application software is an android simulator is mostly judged according to the hardware information of the android phone. However, some android simulators can modify configuration files at the bottom layer of the android system, so that hardware information of manufacturers, brands, models, CPUs, memories, flash memories, sensors, screen resolutions, bluetooth, IMEI, mac addresses, mobile phone numbers, network operators and the like of the android mobile phones is forged, and the existing detection scheme of the android simulators is successfully deceived.
Disclosure of Invention
The technical problem to be solved by the invention is to overcome the defects of the prior art and provide a method and a device for detecting an Android simulator.
In order to achieve the purpose, the invention provides the following technical scheme:
a method for detecting an Android simulator comprises the following steps:
1) collecting device simulation values appearing in various device information in various simulators, acquiring the device information of the current device, and judging whether the current device is an Android simulator or not according to the device simulation values and the device information; if not, performing step 2); if the Android simulator is the Android simulator, obtaining a detection result, and turning to the step 4);
2) collecting hardware simulation values of various hardware information in various simulators, acquiring various hardware information of current equipment, and judging whether the current equipment is an Android simulator or not according to the hardware simulation values and the hardware information; if not, performing step 3); if the Android simulator is the Android simulator, obtaining a detection result, and turning to the step 4);
3) acquiring special file information and user trace information of current equipment, extracting equipment characteristics by combining the equipment information and hardware information, inputting the equipment characteristics into a simulator detection model, and acquiring a detection result;
4) outputting a detection result;
the simulator detection model is obtained through the following steps:
a) acquiring equipment information, hardware information, special file information and user trace information of a plurality of sample equipment;
b) extracting the equipment characteristics of each sample equipment;
c) and performing machine learning training according to the equipment characteristics of each sample equipment to obtain a simulator detection model.
Further, the device information includes: device attributes and production information.
Further, the device attributes include: an international mobile subscriber identity, an international mobile equipment identity and/or a cell phone number.
Further, the production information includes: the mobile phone comprises a mobile phone brand, a manufacturer, a product full name, mainboard information, CPU _ ABI, an industrial model number, equipment fingerprints, hardware information, a product version number, a production serial number and/or a public model number.
Further, in step 1), judging whether the current device is an Android simulator or not by the following steps:
(1) generating a first blacklist according to the equipment simulation value;
(2) acquiring a corresponding value of the equipment attribute through android.telephony.telephony manager; if the corresponding value of any equipment attribute is in the first blacklist, judging that the current equipment is an Android simulator; otherwise, entering the step (3);
(3) acquiring corresponding values of the first production information through constants in the android.os.built; if the corresponding value of any one first production information is in the first blacklist, judging that the current equipment is an Android simulator; otherwise, entering the step (4);
(4) reading/system/built.prop files to obtain corresponding values of the second production information; and if the corresponding value of any one first production information is inconsistent with the corresponding value of the corresponding second production information, judging that the current equipment is the Android simulator.
Further, the hardware information includes: sensor information, CPU information, battery information, speaker information, camera information, WiFi information, and/or bluetooth adapter information.
Further, in step 2), judging whether the current device is an Android simulator or not through the following steps:
(1) generating a second blacklist according to the hardware simulation value;
(2) acquiring each sensor in the current equipment, and reading sensor parameters at intervals of a set time; if the sensor cannot be obtained, the sensor parameters cannot be read normally, the corresponding values of the read sensor parameters are in a second blacklist or the sensor parameters read each time are consistent, judging that the current equipment is an Android simulator; otherwise, entering the step (3);
(3) acquiring CPU information of current equipment; if the CPU information cannot be normally acquired or the corresponding value of the acquired CPU information is in the second blacklist, judging that the current equipment is the Android simulator; otherwise, entering the step (4);
(4) acquiring battery information of current equipment, and reading battery parameters at intervals of a set time; if the battery information cannot be normally acquired, the corresponding values of the battery parameters are read in a second blacklist or the battery parameters read each time are consistent, judging that the current equipment is an Android simulator; otherwise, entering the step (5);
(5) acquiring speaker information of current equipment and playing any audio; if the audio cannot be played normally, judging that the current environment is an Android simulator, otherwise, entering the step (6);
(6) acquiring camera information of current equipment, and shooting a plurality of photos by using a shooting function; if the camera information cannot be normally acquired, the corresponding values of the camera information are in a second blacklist, the pictures cannot be shot, or the pictures are the same, judging that the current equipment is an Android simulator; otherwise, entering the step (7);
(7) acquiring WiFi information; if the WiFi information cannot be normally acquired or the corresponding value of each WiFi information is in the second blacklist, judging that the current equipment is an Android simulator; otherwise, entering the step (8);
(8) acquiring a Bluetooth adapter instance, opening a Bluetooth function, and reading connectable equipment; and if the Bluetooth adapter example cannot be normally acquired, the Bluetooth function cannot be opened, and the corresponding value of the connectable device or the Bluetooth adapter example cannot be read to be in the second blacklist, judging that the current device is the Android simulator.
Further, the sensor includes: acceleration sensors, pressure sensors, magnetic field sensors, direction sensors, distance sensors and/or temperature sensors.
Further, the sensor parameters include: acceleration, pressure, magnetic field strength, direction, distance, and/or temperature. Further, the CPU information includes: a CPU model, a CPU temperature, and/or a CPU architecture.
Further, the battery information includes: battery voltage, battery charge, battery model, and/or battery temperature.
Further, the battery parameters include: battery charge and/or battery temperature.
Further, the extracting the directory of the special file information includes: the system includes a bus interface, a bus interface.
Further, the user trace information includes: contact lists, call records, short messages, browsing history, installed programs, chat records of social applications, purchase records of online shopping applications, whether to install an Xpos frame, and/or whether to obtain root privileges.
Further, a method of machine learning training includes: and (4) a decision tree.
A storage medium having a computer program stored therein, wherein the computer program is arranged to perform the above-mentioned method when executed.
An electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer to perform the method as described above.
Compared with the prior art, the invention has the following advantages:
1) firstly, a portable and rapid judging method is adopted to detect whether the attribute value has an analog value, if so, the attribute value is judged to be an Android simulator, and the efficiency of detecting the common simulator is ensured.
2) The current environment is judged innovatively by using a machine learning model, the user trace and the information of the special file which cannot be simulated by the simulator are extracted, the decision tree classifier is trained by combining the acquired equipment information and hardware information, the current environment is judged by using the decision tree model, and the judgment rule is better than that made by using a man-made judgment rule and more accurate.
3) The special file information and the user trace information are difficult to simulate, and the artificial simulation and the actual environment generate great difference, so that the accuracy rate can be greatly improved by extracting the two types of information as features.
Drawings
FIG. 1 is a schematic flow diagram of the present invention.
Fig. 2 is a schematic diagram of the device information detected by the device information module of the present invention.
FIG. 3 is a diagram of hardware information detected by the hardware information module according to the present invention.
FIG. 4 is a schematic diagram of a directory for collecting information of a particular file by the integrated rating module according to the present invention.
FIG. 5 is a schematic diagram of user trace information collected by the integrated assessment module according to the present invention.
Detailed Description
In order to make the technical solutions in the embodiments of the present invention better understood and make the objects, features, and advantages of the present invention more comprehensible, the technical core of the present invention is described in further detail below with reference to the accompanying drawings and examples.
The flow chart of the Android simulator detection method is shown in fig. 1, and the Android simulator detection method comprises an equipment information detection module, a hardware information detection module and a comprehensive judgment module. The method comprises the following steps:
1. firstly, an equipment information detection module is started, and the module judges whether the current environment is in the Android simulator environment or not according to the equipment information. As shown in fig. 2, the device information detected by the module includes device attributes and production information, the device attributes refer to three attributes of IMSI (international mobile subscriber identity), IMEI (international mobile equipment identity) and mobile phone number of the current device, and the production information includes information such as mobile phone brand, manufacturer, product full name, motherboard information, CPU _ ABI, industrial model, device fingerprint, hardware information, product version number, production serial number, and open model. For the detection of the information, the analog value generated by each piece of information in the Android simulator needs to be set, and if the acquired value is matched with the analog value, the current environment is judged to be under the simulator.
And for the equipment attribute, calling a related interface to directly obtain the value of the corresponding information, and matching the value with the analog value to finish the judgment. The production information may be determined by comparing a value acquired using the system interface with the simulator value. In addition, the information can be obtained not only by using a system interface, but also by reading a configuration file/system/built.
Specifically, the device information detection module flow:
firstly, collecting analog values of various pieces of equipment information in various simulators, and generating an analog value matching blacklist. And if the value is matched with the value in the blacklist, judging that the current environment is the Android simulator environment.
And secondly, acquiring equipment attribute information of the current environment, including IMSI, IMEI and mobile phone number, by using android.
And thirdly, detecting whether the IMSI, the IMEI and the mobile phone number of the current environment exist in a blacklist or not. If the current environment is the Android simulator, judging that the current environment is the Android simulator; otherwise, the next step is carried out.
And fourthly, obtaining the value of the production information of the current environment through the constant in the android.
And fifthly, detecting whether the production information of the current environment has an analog value. If the current environment is the Android simulator, judging that the current environment is the Android simulator; otherwise, the next step is carried out.
And sixthly, reading the/system/built.prop file, and detecting whether the obtained production information is consistent with the information in the file. If the current environment is inconsistent with the Android simulator, judging that the current environment is the Android simulator; otherwise, entering a hardware information detection module.
2. And if the equipment information detection module does not judge that the current environment is under the Android simulator, entering a hardware information detection module. As shown in fig. 3, the hardware information includes sensor information, CPU information, battery information, speaker information, camera information, WiFi information, bluetooth adapter information. Whether the hardware equipment is in the Android environment or not is judged by judging whether the hardware equipment can work normally or not.
The sensor information includes various sensors in the device: acceleration sensors, pressure sensors, magnetic field sensors, direction sensors, distance sensors, temperature sensors, etc. And respectively using various sensors to acquire information of corresponding types, and if the information cannot be normally acquired, judging that the information is in the simulator environment.
The CPU information includes information such as CPU model, CPU temperature, CPU architecture, etc., and if the information cannot be normally acquired or the acquired value is an analog value, the information is determined to be in the simulator environment.
The battery information includes information such as voltage, electric quantity, model, temperature, etc. If the model is an analog value, or the voltage, the electric quantity and the temperature are kept unchanged all the time in a period of time, the model is judged to be in the simulator environment.
And if the loudspeaker information, the camera information, the WiFi information and the Bluetooth adapter information cannot be used, judging that the loudspeaker information, the camera information, the WiFi information and the Bluetooth adapter information are currently in the simulator environment.
Specifically, the hardware information detection module flow:
firstly, collecting analog values of various hardware information in various simulators, and generating an analog value matching blacklist. And if the value is matched with the value in the blacklist, judging that the current environment is the Android simulator environment.
And secondly, acquiring examples of various sensors under the current environment, wherein the examples comprise an acceleration sensor, a pressure sensor, a magnetic field sensor, a direction sensor, a distance sensor and a temperature sensor. Acceleration, pressure, magnetic field strength, direction, distance, temperature were read separately using sensor examples, once every 60 seconds for a total of 10 readings. If the instance cannot be normally acquired, the information cannot be normally read, the read information is an analog value, or the information read for 10 times is completely the same, judging that the current environment is the Android simulator, and if not, carrying out the next step.
And thirdly, acquiring the information of the CPU under the current environment, including the model, the temperature and the architecture. And if the current environment cannot be normally acquired and the acquired value is an analog value, judging that the current environment is an Android simulator, and if not, carrying out the next step.
And fourthly, acquiring battery information under the current environment, wherein the battery information comprises the model, the electric quantity, the voltage and the temperature, and reading the electric quantity and the temperature once every 60 seconds for 10 times in total. If the current environment is not normally acquired, the acquired value is an analog value, or the electric quantity and the temperature are the same after 10 times of reading, judging that the current environment is an Android simulator, and if not, carrying out the next step.
And fifthly, acquiring the current loudspeaker example and playing any audio. And if the current environment cannot be played normally, judging that the current environment is the Android simulator, and if not, carrying out the next step.
And sixthly, acquiring current camera information including the number of cameras and pixels, and taking 10 pictures by using a shooting function. If the current environment is judged to be the Android simulator, and if the current environment cannot be normally obtained, the number of the cameras and the number of the pixels have analog values, the pictures cannot be shot, or the shot 10 pictures are the same, otherwise, the next step is carried out.
And seventhly, obtaining WiFi information, if the WiFi information cannot be normally obtained and the obtained value is an analog value, judging that the current environment is an Android simulator, and if not, carrying out the next step.
And eighthly, acquiring the instance of the Bluetooth adapter, opening the Bluetooth function and reading the connectable equipment. If the examples cannot be normally acquired, the Bluetooth function cannot be opened, the connectable equipment cannot be read, and analog values exist in the acquired examples, the current environment is judged to be the Android simulator, and if not, the comprehensive evaluation module is started.
3. And if the hardware information detection module still does not judge that the current environment is the simulator environment, starting the comprehensive judgment module. The module does not judge the current environment by matching or mismatching of a certain information item, but comprehensively judges the current environment by using a decision tree model. The current environment information comprises equipment information acquired by the equipment information module, hardware information acquired by the hardware information module, special file information and user trace information.
Since the information of the simulator can be modified in the prior art, the detection and judgment of the device information and the hardware information independently are easily interfered. Therefore, the invention innovatively provides a comprehensive judgment module to integrate all information for judgment. If the device information and the hardware information are modified in the simulator and changed into real values, the simulation values do not appear in the information items. However, such modifications may destroy the association between the original information items. Therefore, by combining these information for determination, information items that do not match can be found, thereby determining that the current environment is under the simulator.
The comprehensive judgment module is also innovatively added with the extraction and judgment of the user trace information. For a real environment, traces of user usage may occur, including photo albums, text messages, calls, browsing records, installed software packages, and so on. Under a real environment, the photo album, the short message, the call and the browsing record have the traces of the use of the user and some common application programs are installed. Therefore, whether the current environment is in the Android simulator environment or not can be judged by acquiring the information. The Xpos frame can modify the return value of the system interface, and the installation of the Xpos frame requires the root authority, so the installation of the Xpos frame and the acquisition of the root authority are also used as user trace information.
And integrating the equipment information, the hardware information, the special file information and the user trace information, extracting the characteristics, and classifying by using a decision tree model. If the final output result is 1, indicating that the current environment is under the Android simulator; if the current environment is 0, the current environment is in the real device.
The comprehensive evaluation module process comprises the following steps:
first, special file information is collected, as shown in fig. 4, which mainly includes file names under the directories of/system,/system/bus,/system/class,/system/module,/dev/socket,/system,/bin,/system/lib.
And secondly, collecting user trace information, as shown in fig. 5, including a contact list, call records, short messages, browsing history, installed programs, chat records of social application programs, purchase records of online shopping application programs, whether to install an Xposed frame, whether to acquire root authority, and the like.
And thirdly, generating a vector for describing the current environment by taking the equipment information, the hardware information, the special file information and the user characteristic information as characteristics.
And fourthly, collecting data. Collecting equipment information, hardware information, special file information and user characteristic information in the Android system environment of each version of each Android simulator; the method comprises the steps of collecting equipment information, hardware information, special file information and user characteristic information on various types of mobile phones with different service lives.
And fifthly, training a decision tree model by using the data collected in the last step.
And sixthly, inputting a current environment vector, and predicting by using a decision tree model. If the output is 1, the current environment is an Android simulator environment; if the output is 0, the current environment is the real equipment.
The above examples are provided only for the purpose of describing the present invention, and are not intended to limit the scope of the present invention. The scope of the invention is defined by the appended claims. Various equivalent substitutions and modifications can be made without departing from the spirit and principles of the invention, and are intended to be within the scope of the invention.
Claims (10)
1. A method for detecting an Android simulator comprises the following steps:
1) collecting device simulation values appearing in various device information in various simulators, acquiring the device information of the current device, and judging whether the current device is an Android simulator or not according to the device simulation values and the device information; if not, performing step 2); if the Android simulator is the Android simulator, obtaining a detection result, and turning to the step 4);
2) collecting hardware simulation values of various hardware information in various simulators, acquiring various hardware information of current equipment, and judging whether the current equipment is an Android simulator or not according to the hardware simulation values and the hardware information; if not, performing step 3); if the Android simulator is the Android simulator, obtaining a detection result, and turning to the step 4);
3) acquiring special file information and user trace information of current equipment, extracting equipment characteristics by combining the equipment information and hardware information, inputting the equipment characteristics into a simulator detection model, and acquiring a detection result;
4) outputting a detection result;
the simulator detection model is obtained through the following steps:
a) acquiring equipment information, hardware information, special file information and user trace information of a plurality of sample equipment;
b) extracting the equipment characteristics of each sample equipment;
c) and performing machine learning training according to the equipment characteristics of each sample equipment to obtain a simulator detection model.
2. The method of claim 1, wherein the device information comprises: device attributes and production information; the device attributes include: an international mobile subscriber identity, an international mobile equipment identity and/or a mobile phone number; the production information includes: the mobile phone comprises a mobile phone brand, a manufacturer, a product full name, mainboard information, CPU _ ABI, an industrial model number, equipment fingerprints, hardware information, a product version number, a production serial number and/or a public model number.
3. The method of claim 2, wherein in the step 1), whether the current device is an Android simulator is determined by:
(1) generating a first blacklist according to the equipment simulation value;
(2) acquiring a corresponding value of the equipment attribute through android.telephony.telephony manager; if the corresponding value of any equipment attribute is in the first blacklist, judging that the current equipment is an Android simulator; otherwise, entering the step (3);
(3) acquiring corresponding values of the first production information through constants in the android.os.built; if the corresponding value of any one first production information is in the first blacklist, judging that the current equipment is an Android simulator; otherwise, entering the step (4);
(4) reading/system/built.prop files to obtain corresponding values of the second production information; and if the corresponding value of any one first production information is inconsistent with the corresponding value of the corresponding second production information, judging that the current equipment is the Android simulator.
4. The method of claim 1, wherein the hardware information comprises: sensor information, CPU information, battery information, speaker information, camera information, WiFi information, and/or bluetooth adapter information.
5. The method of claim 4, wherein in the step 2), whether the current device is an Android simulator is determined by:
(1) generating a second blacklist according to the hardware simulation value;
(2) acquiring each sensor in the current equipment, and reading sensor parameters at intervals of a set time; if the sensor cannot be obtained, the sensor parameters cannot be read normally, the corresponding values of the read sensor parameters are in a second blacklist or the sensor parameters read each time are consistent, judging that the current equipment is an Android simulator; otherwise, entering the step (3);
(3) acquiring CPU information of current equipment; if the CPU information cannot be normally acquired or the corresponding value of the acquired CPU information is in the second blacklist, judging that the current equipment is the Android simulator; otherwise, entering the step (4);
(4) acquiring battery information of current equipment, and reading battery parameters at intervals of a set time; if the battery information cannot be normally acquired, the corresponding values of the battery parameters are read in a second blacklist or the battery parameters read each time are consistent, judging that the current equipment is an Android simulator; otherwise, entering the step (5);
(5) acquiring speaker information of current equipment and playing any audio; if the audio cannot be played normally, judging that the current environment is an Android simulator, otherwise, entering the step (6);
(6) acquiring camera information of current equipment, and shooting a plurality of photos by using a shooting function; if the camera information cannot be normally acquired, the corresponding values of the camera information are in a second blacklist, the pictures cannot be shot, or the pictures are the same, judging that the current equipment is an Android simulator; otherwise, entering the step (7);
(7) acquiring WiFi information; if the WiFi information cannot be normally acquired or the corresponding value of each WiFi information is in the second blacklist, judging that the current equipment is an Android simulator; otherwise, entering the step (8);
(8) acquiring a Bluetooth adapter instance, opening a Bluetooth function, and reading connectable equipment; and if the Bluetooth adapter example cannot be normally acquired, the Bluetooth function cannot be opened, and the corresponding value of the connectable device or the Bluetooth adapter example cannot be read to be in the second blacklist, judging that the current device is the Android simulator.
6. The method of claim 5, wherein the sensor comprises: acceleration sensors, pressure sensors, magnetic field sensors, direction sensors, distance sensors and/or temperature sensors; the sensor parameters include: acceleration, pressure, magnetic field strength, direction, distance, and/or temperature. Further, the CPU information includes: CPU model, CPU temperature, and/or CPU architecture; the battery information includes: battery voltage, battery charge, battery model, and/or battery temperature; the battery parameters include: battery charge and/or battery temperature.
7. The method of claim 1, wherein extracting a directory of special file information comprises: a/system,/system/bus,/system/class,/system/module,/dev/socket,/system/bin and/system/lib; the user trace information includes: contact lists, call records, short messages, browsing history, installed programs, chat records of social applications, purchase records of online shopping applications, whether to install an Xpos frame, and/or whether to obtain root privileges.
8. The method of claim 1, wherein the method of machine learning training comprises: and (4) a decision tree.
9. A storage medium having a computer program stored thereon, wherein the computer program is arranged to, when run, perform the method of any of claims 1-8.
10. An electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the method according to any of claims 1-8.
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Cited By (1)
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| CN114860348A (en) * | 2022-06-09 | 2022-08-05 | 北京奇艺世纪科技有限公司 | Android simulator identification method and device, electronic equipment and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109062667A (en) * | 2018-07-27 | 2018-12-21 | 平安科技（深圳）有限公司 | A kind of simulator recognition methods, identification equipment and computer-readable medium |
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| CN110196795A (en) * | 2018-06-21 | 2019-09-03 | 腾讯科技（深圳）有限公司 | Detect the method and relevant apparatus of mobile terminal application operating status |
| CN109062667A (en) * | 2018-07-27 | 2018-12-21 | 平安科技（深圳）有限公司 | A kind of simulator recognition methods, identification equipment and computer-readable medium |
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| CN110619210A (en) * | 2019-08-27 | 2019-12-27 | 苏宁云计算有限公司 | Simulator detection method and system |
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| CN114860348A (en) * | 2022-06-09 | 2022-08-05 | 北京奇艺世纪科技有限公司 | Android simulator identification method and device, electronic equipment and storage medium |
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