Pseudo base station positioning method and device, storage medium and electronic device

Abstract

The embodiment of the invention provides a pseudo base station positioning method and device, a storage medium and an electronic device. The method comprises the following steps: acquiring a working frequency point and a cell identifier of a pseudo base station cell in a target area; accessing a test terminal to the pseudo base station cell corresponding to the cell identifier according to the working frequency point, and determining a first distance between the test terminal and the pseudo base station according to first path loss data between the test terminal and the pseudo base station; and determining the position of the pseudo base station based on a moving path corresponding to the first distance, the moving path being a path obtained by adjusting an initial path corresponding to the test terminal according to a preset adjustment rule, the preset adjustment rule is used for determining moving angles and / or moving distances of the test terminal at different position points of the moving path. By adopting the technical scheme, the problem of low positioning accuracy of the pseudo base station in related technologies is solved.

Description

Technical Field

[0001] The present application relates to the field of communications, and in particular, to a method, device, storage medium and electronic device for locating a pseudo base station. Background Art

[0002] In related technologies, a device that uses simple wireless equipment and dedicated open source software to simulate and disguise as an existing base station is called a fake base station. Figure 1 As shown in the figure, the fake base station will attract surrounding terminal users to stay in the cell through strong signals. The fake base station will carry out deceptive attacks on the terminal and interfere with the normal communication between the terminal and the operator network, affecting the performance of the operator network, such as causing handover failures and abnormal call drops. It may also cause the terminal to be disconnected from the operator network and the terminal to be attracted to the fake base station.

[0003] After the fake base station attacks normal terminal users and operator networks, it can cause the following three typical interference scenarios:

[0004] Scenario 1: Handover failure. The connected UE measures the strong signal of the pseudo base station cell and reports the result to the real base station. If the pseudo base station cell is in the neighboring cell list of the real base station service cell at this time, the real base station determines that the neighboring cell signal meets the handover conditions and triggers the UE to switch to the pseudo base station cell, resulting in handover failure. Because the UE has disconnected from the real base station at this time and resides in the pseudo base station cell. Scenario 2: Dropped call due to interference. The connected UE directly drops the call due to severe signal interference, and then the UE reselects to the pseudo base station cell with higher signal strength. Scenario 3: Cell reselection. The idle UE measures that the signal strength of the pseudo base station cell is higher and directly reselects to the pseudo base station cell.

[0005] Commonly used fake base station identification technologies can be summarized into two categories: 1) Detection methods based on signal strength; 2) Detection methods based on communication network parameters. For detection methods based on signal strength: When a fake base station attacks, it will deliberately increase the signal strength much higher than that of a legitimate base station, while the signal strength of an ordinary base station is usually within a certain range, so by monitoring abnormal signal strength, fake base stations can be discovered. For detection methods based on communication network parameters: Generally, a complete database of network-wide parameters is detected, and then the detection work is achieved through parameter analysis of the entire network, such as counting the number of abnormal network switching, and using the location identification code (Location Area Code, referred to as LAC) to analyze whether the distance between the user and the base station is within a reasonable range.

[0006] The following problems exist in the currently commonly used pseudo base station identification and positioning technologies:

[0007] The actual network environment of operators is very complex. Due to factors such as building obstruction, antenna position / angle, number of users, etc. in different areas, the operator's network information parameters are variable and have strong regional characteristics. The accuracy of a single unchanging pseudo base station identification model is very low. For example: the same network KPI problem (abnormal handover failure and dropped calls) occurs in areas A and B. The current pseudo base station identification technology determines that they are all caused by pseudo base stations. However, after actual on-site investigation, it is found that the problem is caused by pseudo base stations in area A and normal network fluctuations in area B.

[0008] With regard to the problem of low positioning accuracy of pseudo base stations in related technologies, no effective solution has been proposed so far.

[0009] Therefore, it is necessary to improve the related technology to overcome the above-mentioned defects in the related technology. Summary of the invention

[0010] The embodiments of the present application provide a method, device, storage medium and electronic device for locating a pseudo base station, so as to at least solve the problem of low positioning accuracy of pseudo base stations in the related art.

[0011] According to one aspect of an embodiment of the present application, a method for locating a pseudo base station is provided, including: obtaining an operating frequency and a cell identifier of a pseudo base station cell within a target area; connecting a test terminal to the pseudo base station cell corresponding to the cell identifier according to the operating frequency, and determining a first distance between the test terminal and the pseudo base station based on first path loss data between the test terminal and the pseudo base station; determining the position of the pseudo base station based on a moving path corresponding to the first distance, wherein the moving path is a path obtained by adjusting an initial path corresponding to the test terminal according to a preset adjustment rule, and the preset adjustment rule is used to determine a moving angle and / or moving distance of the test terminal at different position points on the moving path.

[0012] In an exemplary embodiment, the position of the pseudo base station is determined based on the moving path corresponding to the first distance, including: obtaining N coordinate information corresponding to the N positions of the test terminal on the moving path, wherein N is an integer greater than 2; and determining the position of the pseudo base station according to the N coordinate information corresponding to the N positions of the test terminal.

[0013] In an exemplary embodiment, before obtaining the operating frequency and cell identifier of the pseudo base station cell in the target area, the method also includes: obtaining a training set used to train a pseudo base station identification model; determining a plurality of neurons activated by the training set and weight values ​​corresponding to the plurality of neurons; obtaining the pseudo base station identification model by training according to the plurality of neurons, the weight values ​​corresponding to the plurality of neurons and the scenario factor quantized by the scenario model of the target area; and determining whether the pseudo base station exists in the target area through the pseudo base station identification model.

[0014] In an exemplary embodiment, after the pseudo base station identification model is obtained by training according to the multiple neurons, the weight values ​​corresponding to the multiple neurons respectively, and the scenario factors quantized by the scenario model of the target area, the method further includes: sending a collection task to a real base station in the target area, wherein the collection task is used to collect input data of the pseudo base station identification model; receiving the input data sent by the real base station, and inputting the input data into the pseudo base station identification model to obtain an output result of the pseudo base station identification model; and determining whether there is a pseudo base station in the target area according to the output result.

[0015] In an exemplary embodiment, before determining the first distance between the test terminal and the pseudo base station based on the first path loss data between the test terminal and the pseudo base station, the method also includes: obtaining the reference signal power of the pseudo base station from the broadcast message of the pseudo base station cell; and determining the first path loss data based on the reference signal power of the pseudo base station and the reference signal power received by the test terminal.

[0016] In an exemplary embodiment, before determining the position of the pseudo base station based on the moving path corresponding to the first distance, it also includes: determining the initial moving path of the test terminal based on the initial position coordinates of the test terminal accessing the pseudo base station cell and the first distance, wherein the initial moving path is a closed loop graph; adjusting the initial moving path according to the preset adjustment rules to obtain the moving path.

[0017] In an exemplary embodiment, the initial moving path is adjusted according to the preset adjustment rule to obtain the moving path, including: determining a first moving angle and a first moving distance of the test terminal; controlling the test terminal to move from an initial position corresponding to the initial position coordinates to a first position according to the first moving angle and the first moving distance; controlling the test terminal to move from the first position to a second position according to a second moving angle and a second moving distance; controlling the test terminal to move from the second position to the remaining positions in sequence until the test terminal finally moves to the initial position, wherein the moving path includes: the initial position, the first position, the second position, and the remaining positions.

[0018] In an exemplary embodiment, before controlling the test terminal to move from the initial position corresponding to the initial position coordinates to the first position according to the first moving angle and the first moving distance, the method also includes: determining the fourth position coordinates of the test terminal at the first position, the second path loss data between the test terminal at the first position and the pseudo base station, and the second distance between the test terminal at the first position and the pseudo base station; performing reinforcement learning on the target parameters through a reinforcement learning algorithm, and obtaining the second moving angle and the second moving distance corresponding to the test terminal from the results of the reinforcement learning, wherein the target parameters include: the first moving angle, the first moving distance, the initial coordinates, the first distance, the first path loss data, the fourth position coordinates, the second path loss data and the second distance.

[0019] According to another aspect of the embodiment of the present application, a positioning device for a pseudo base station is also provided, including: an acquisition module, used to acquire the working frequency and cell identifier of the pseudo base station cell in the target area; an access module, used to connect the test terminal to the pseudo base station cell corresponding to the cell identifier according to the working frequency; a first determination module, used to determine the first distance between the test terminal and the pseudo base station according to the first path loss data between the test terminal and the pseudo base station when the test terminal successfully accesses the pseudo base station cell; a second determination module, used to determine the position of the pseudo base station based on a moving path corresponding to the first distance, wherein the moving path is a path obtained by adjusting the initial path corresponding to the test terminal according to a preset adjustment rule, and the preset adjustment rule is used to determine the moving angle and / or moving distance of the test terminal at different position points on the moving path.

[0020] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned pseudo base station positioning method when running.

[0021] According to another aspect of an embodiment of the present application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the pseudo base station positioning method through the computer program.

[0022] Through the present application, a test terminal is introduced, and the test terminal is connected to the pseudo base station cell corresponding to the cell identifier according to the working frequency of the pseudo base station cell, and the first distance between the test terminal and the pseudo base station is determined according to the first path loss data between the test terminal and the pseudo base station; the position of the pseudo base station is determined based on the moving path corresponding to the first distance, wherein the moving path is a path obtained by adjusting the initial path corresponding to the test terminal according to a preset adjustment rule, and the preset adjustment rule is used to determine the moving angle and / or moving distance of the test terminal at different position points on the moving path, thereby solving the technical problem of low positioning accuracy of pseudo base stations in related technologies and providing an accurate positioning solution for pseudo base stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The exemplary embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0024] Figure 1 It is a schematic diagram of a usage scenario of a pseudo base station in the related art;

[0025] Figure 2 It is a hardware structure block diagram of a computer terminal of a method for locating a pseudo base station according to an embodiment of the present application;

[0026] Figure 3 is a flowchart of a method for locating a pseudo base station according to an embodiment of the present application (I);

[0027] Figure 4 is a schematic diagram of an initial moving path according to an embodiment of the present application;

[0028] Figure 5 is a schematic diagram of a path for adjusting an initial moving path according to an embodiment of the present application;

[0029] Figure 6 is another path schematic diagram of adjusting the initial moving path according to an embodiment of the present application;

[0030] Figure 7 is a schematic diagram of a device for model training and pseudo base station identification according to an embodiment of the present application;

[0031] Figure 8is an overall flow chart of a method for locating a pseudo base station according to an embodiment of the present application;

[0032] Fig. 9 is a schematic diagram of building a scenario model according to an embodiment of the present application;

[0033] Fig.10 is a schematic diagram of training a pseudo base station identification model according to an embodiment of the present application;

[0034] Fig.11 is another training schematic diagram of a pseudo base station identification model according to an embodiment of the present application;

[0035] Fig.12 It is a structural block diagram of a positioning device for a pseudo base station according to an embodiment of the present application. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0037] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0038] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal or a similar computing device. Taking running on a computer terminal as an example, Figure 2 : is a hardware structure block diagram of a computer terminal of a method for locating a pseudo base station in an embodiment of the present application. Figure 2 As shown, the computer terminal may include one or more ( Figure 2Only one is shown in the figure) processor 202 (processor 202 may include but is not limited to a microprocessor (Microprocessor Unit, referred to as MPU) or a programmable logic device (Programmab le logic device, referred to as PLD)) and a memory 204 for storing data. In an exemplary embodiment, the computer terminal may also include a transmission device 206 and an input and output device 208 for communication functions. It can be understood by those skilled in the art that Figure 2 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 2 More or fewer components as shown, or with Figure 2 Equivalent functions or comparisons shown Figure 2 A different configuration with more features is shown.

[0039] The memory 204 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the positioning method of the pseudo base station in the embodiment of the present application. The processor 202 executes various functional applications and data processing by running the computer program stored in the memory 204, that is, to implement the above method. The memory 204 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 204 may further include a memory remotely arranged relative to the processor 202, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0040] The transmission device 206 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 206 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 206 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0041] Figure 3 is a flowchart of a method for locating a pseudo base station according to an embodiment of the present application (I), such as Figure 3 As shown, the steps of the method include:

[0042] Step S302: Acquire the working frequency and cell identifier of the pseudo base station cell in the target area.

[0043] Step S304: connect the test terminal to the pseudo base station cell corresponding to the cell identifier according to the working frequency, and determine a first distance between the test terminal and the pseudo base station according to first path loss data between the test terminal and the pseudo base station.

[0044] Step S306, determining the position of the pseudo base station based on the moving path corresponding to the first distance, wherein the moving path is a path obtained by adjusting the initial path corresponding to the test terminal according to a preset adjustment rule, and the preset adjustment rule is used to determine the moving angle and / or moving distance of the test terminal at different position points on the moving path.

[0045] Through the present application, a test terminal is introduced, and the test terminal is connected to the pseudo base station cell corresponding to the cell identifier according to the working frequency of the pseudo base station cell, and the first distance between the test terminal and the pseudo base station is determined according to the first path loss data between the test terminal and the pseudo base station; the position of the pseudo base station is determined based on the moving path corresponding to the first distance, wherein the moving path is a path obtained by adjusting the initial path corresponding to the test terminal according to a preset adjustment rule, and the preset adjustment rule is used to determine the moving angle and / or moving distance of the test terminal at different position points on the moving path, thereby solving the technical problem of low positioning accuracy of pseudo base stations in related technologies and providing an accurate positioning solution for pseudo base stations.

[0046] There are multiple implementation schemes for step S306 in the embodiment of the present application. In an optional embodiment, the initial moving path is first determined, and then the initial moving path is adjusted to obtain the moving path. Then, the position of the pseudo base station is determined based on the N coordinate information of the N positions on the moving path. Specifically, the initial moving path of the test terminal is determined based on the initial position coordinates of the test terminal accessing the pseudo base station cell and the first distance, wherein the initial moving path is a closed loop graph; the initial moving path is adjusted according to the preset adjustment rule to obtain the moving path.

[0047] Optionally, the initial moving path can be set to be circular. Figure 4 As shown, Figure 4 The initial position 1 in the figure is equivalent to the initial position coordinates of the above embodiment. According to the above first distance and the initial position coordinates, the distance radius d1 can be determined. According to the distance radius d1, the predicted position 3 is determined. According to the initial position 1, the predicted position 3 and the distance radius d1, the predicted position 2 and the predicted position 4 can be determined. According to the initial position 1, the predicted positions 2-4 and the distance radius d1, the initial moving path can be comprehensively determined, that is, Figure 4The circle shown can be understood as an optional initial moving path of the above embodiment.

[0048] Based on the above implementation scheme, the position of the pseudo base station is determined based on the moving path corresponding to the first distance, and the position of the pseudo base station can be determined by obtaining N coordinate information corresponding to the N positions of the test terminal at the moving path, where N is an integer greater than 2; and determining the position of the pseudo base station according to the N coordinate information corresponding to the N positions of the test terminal. Optionally, Figure 5 As shown, the N positions on the moving path may be position 2, position 3, ..., position N determined after adjustment, and also include the initial position 1. Figure 6 It further displays multiple position points after adjustment from position 1 to position 3, and determines the position of the pseudo base station based on the multiple adjusted position points.

[0049] It should be noted that the initial moving path is adjusted according to the preset adjustment rule to obtain the moving path through the following scheme: Figure 5 As shown, determine the first moving angle of the test terminal (equivalent to the attached Figure 5 The initial moving angle a1) and the first moving distance (equivalent to the Figure 5 control the test terminal to move from the initial position corresponding to the initial position coordinate (equivalent to the initial position 1) to the first position (equivalent to the attached position) according to the first moving angle and the first moving distance; Figure 5 control the test terminal to move from the first position to a second moving angle (equivalent to the position of Figure 5 The moving angle a2 in the figure) and the second moving distance (equivalent to the Figure 5 The moving distance b2 in the figure moves to the second position (equivalent to the attached Figure 5 control the test terminal to move from the second position to the remaining positions in sequence until the position to which the test terminal finally moves is the initial position (i.e., the test terminal moves back to the adjacent Figure 5 1), wherein the moving path includes: the initial position, the first position, the second position and the remaining positions.

[0050] Based on the above-mentioned implementation scheme for adjusting the initial moving path, each movement between different position points requires first determining the moving angle and the moving distance. The scheme for determining the moving angle and the moving distance may be the result of reinforcement learning. For example, determine the fourth position coordinates of the test terminal at the first position, the second path loss data between the test terminal at the first position and the pseudo base station, and the second distance between the test terminal at the first position and the pseudo base station; perform reinforcement learning on the target parameters through a reinforcement learning algorithm, and obtain the second moving angle and the second moving distance corresponding to the test terminal from the results of the reinforcement learning, wherein the target parameters include: the first moving angle, the first moving distance, the initial coordinates, the first distance, the first path loss data, the fourth position coordinates, the second path loss data, and the second distance.

[0051] An important parameter is used in the scheme of the above embodiment: the first path loss data. The embodiment of the present application also provides an implementation scheme for the first path loss data. Before determining the first distance between the test terminal and the pseudo base station according to the first path loss data between the test terminal and the pseudo base station, obtain the reference signal power of the pseudo base station from the broadcast message of the pseudo base station cell; determine the first path loss data according to the reference signal power of the pseudo base station and the reference signal power received by the test terminal.

[0052] Before executing the above-mentioned solution of step S302-step S306, it is necessary to first determine whether there is a fake base station in the target area. If there is a fake base station, then consider executing the solution of step S302-step S306. Then, in the embodiment of the present application, a solution for determining whether there is a fake base station is also provided. Specifically, it can be implemented by the following steps:

[0053] Step 1: Obtain a training set for training a fake base station identification model;

[0054] Step 2: Determine a plurality of neurons activated by the training set and weight values ​​corresponding to the plurality of neurons respectively;

[0055] Step 3: The pseudo base station identification model is obtained by training according to the plurality of neurons, the weight values ​​respectively corresponding to the plurality of neurons and the scene factor quantized by the scene model of the target area;

[0056] Step 4: Determine whether the pseudo base station exists in the target area through the pseudo base station identification model.

[0057] The specific implementation scheme of step 4 may be: after obtaining the pseudo base station identification model through training based on the multiple neurons, the weight values ​​corresponding to the multiple neurons, and the scenario factor quantized by the scenario model of the target area, sending a collection task to the real base station in the target area, wherein the collection task is used to collect input data of the pseudo base station identification model; receiving the input data sent by the real base station, and inputting the input data into the pseudo base station identification model to obtain the output result of the pseudo base station identification model; determining whether there is a pseudo base station in the target area based on the output result. It should be noted that the output result of the pseudo base station identification model is actually whether there is a pseudo base station in the target area. In actual operation, the pseudo base station identification model can be adjusted according to the network characteristics of different operators in the target area.

[0058] In the embodiment of the present application, through the above implementation scheme, the pseudo base station identification model can be intelligently adjusted according to the network characteristics of the operator in different target areas. The pseudo base station identification model removes the network interference of regional heterogeneity and can greatly improve the accuracy of pseudo base station identification. After the pseudo base station is identified through the model, the pseudo base station positioning technology based on reinforcement learning is used to complete the positioning of the pseudo base station, thereby greatly improving the positioning accuracy of the pseudo base station.

[0059] In order to better understand the implementation scheme of the above-mentioned pseudo base station positioning method, the optional embodiments of the embodiments of the present application are subsequently provided for explanation, but are not used to limit the embodiments of the present application.

[0060] In this optional embodiment, if Figure 7 As shown, firstly, the pseudo base station scenario model 70 is used to accurately screen the areas and places that are vulnerable to attacks, and at the same time, the pseudo base station identification model that matches the network characteristics of the target area is combined to determine whether there is a pseudo base station in the target area (see Appendix Figure 7The recognition model training module 72, data collection module 74, and reasoning and judgment module 76 in the test terminal can be understood as serving the pseudo base station recognition model. Specifically, the recognition model training module 72 is used to train the pseudo base station recognition model, the data collection module 74 is used to collect data used to train the pseudo base station recognition model, and the reasoning and judgment module 76 is used in the pseudo base station recognition model to determine whether there is a pseudo base station in the target area). Then a test terminal is placed in the area, the access module 78 is used to access the pseudo base station cell, and the terminal line calculation module 80 uses the path loss calculated by the path loss calculation module 82 to calculate the distance between the test terminal and the pseudo base station. The test terminal will feed back its own GPS position determined by the GPS positioning module 84 and the calculated distance from the pseudo base station to the pseudo base station precise positioning module 86 in the test terminal. The pseudo base station precise positioning module 86 calculates the initial movement trajectory reference of the test terminal, and predicts and optimizes the movement route of the test terminal through the reinforcement learning algorithm. Finally, the precise position of the pseudo base station is calculated through the continuous movement and measurement of the test terminal.

[0061] The optional embodiment of the present application also provides an overall flow chart of a method for locating a pseudo base station, such as Figure 8 As shown. The following steps are included:

[0062] Step S802: Build a scenario model, especially for places vulnerable to fake base station attacks, such as shopping malls, stations, schools, scientific research institutions, etc. Input the model into the fake base station identification model so that the fake base station identification model can locate the fake base station area more efficiently and accurately;

[0063] The implementation method for constructing the scenario model in step S802 may include constructing a physical model and a channel model of the scenario, such as Fig. 9 As shown, it is divided into the following steps:

[0064] Step 1: The scenario-based model receives historical identification data of fake base stations, classifies and sorts the fake base station data according to regional characteristics, and preliminarily identifies the places and scenes where different types of fake base stations appear, such as shopping malls, stations, schools, scientific research institutions, etc.

[0065] Step 2: Select locations that are vulnerable to fake base station attacks and conduct detailed modeling of the scenario.

[0066] Step 3: In the above-mentioned modeling of the outdoor environment of the venue, digital survey technology based on a combination of 3D electronic maps and high-precision photography is used to collect physical data of the outdoor environment of the venue and establish a three-dimensional physical model.

[0067] Step 4: Report the off-site environment modeling data of step 3 to the scenario module.

[0068] Step 5: In the above-mentioned site environment modeling, digital survey technology based on three-dimensional laser radar is used to collect physical data of the site's indoor environment and establish a three-dimensional physical model.

[0069] Step 6: Report the in-venue environment modeling data from step 5 to the scenario module.

[0070] Step 7: In the above-mentioned venue environment, use ray tracing technology and field test channel data to refine the channel model.

[0071] Step 8: Use field test channel data to perform small-scale fine correction of the channel model.

[0072] Step S804: Based on the scenario model and the general neural network learning algorithm, the abnormal network parameter data of the historical pseudo base station and the network parameters of the area to be inspected are used for learning and training to obtain a regional-specific pseudo base station identification model; that is, step S804 can train a dedicated pseudo base station identification model for a dedicated area;

[0073] Optionally, a pseudo base station identification model that intelligently matches the network characteristics of the area to be inspected is constructed, such as Fig.10 As shown, the following steps are included:

[0074] Step 1: Collect a large amount of abnormal network parameter data of historical pseudo base stations to build a general training set, which includes: abnormal event report X1 and measurement report X2.

[0075] Step 2: Set the target detection area, collect historical network data of all real base stations in the target area to build a dedicated training set, which includes: base station RSSI value X3, base station switching rate X4.

[0076] Step 3: If Fig.11 As shown, neurons [X1, X2, X3, X4] are constructed after sampling from the training set, where the abnormal event report X1 = [X 11 ,X 12 ,X 13 ,...X 1n ], measurement report X2 = [X 21 ,X 22 ,X 23 ,...X 2n ], base station RSSI value X3 = [X 31 ,X 32 ,X 33 ,...X 3n ], base station switching rate X4 = [X 41 ,X 42 ,X 43 ,...X 4n ].

[0077] Step 4: Each neuron is given an initial weight [W 1i ,W 2i ,W 3i ,W 4i ].

[0078] Step 5: Quantify the scenario model of the target area into the scenario factor b.

[0079] Step 6: Quantize the weighted input of all neuron connections through basis functions, and then add it to the scene factor b to obtain the output V i , the calculation formula is:

[0080] Step 7: Pass the output through the activation function f(·) to get the final output value Y i , the calculation formula is:

[0081]

[0082] Step 8: Determine Y i Is it greater than or equal to 1? If so, it is judged as a fake base station and ends; otherwise, go to step 7.

[0083] Step 9: Find Y i The error gradient is 1, and the weights are adjusted according to the error gradient, and then the fourth step is performed until the end.

[0084] Step S806: Collecting data of the suspected fake base station area to be detected, that is, collecting data required by the dedicated fake base station identification model;

[0085] Step S808: Infer and determine whether there is a fake base station in the target area. If so, obtain the working frequency and physical cell identifier of the fake base station cell; at the same time, feed back the fake base station identification data to the scenario model to expand the attack scenarios of the fake base stations and the accuracy of the attack scenarios; in addition, input the data into the fake base station training system to improve the training accuracy.

[0086] The specific pseudo base station identification process may include the following steps:

[0087] Step 1: Determine the required data collection dimensions based on the pseudo base station dedicated identification model in the target area. The data collection dimensions include but are not limited to: abnormal event reports, measurement reports, base station RSSI values, and base station switching rates.

[0088] Step 2: Send data collection tasks to real base stations in the region.

[0089] Step 3: Collect data and report it to the pseudo base station-specific identification model in the target area.

[0090] Step 4: Determine the pseudo base station and obtain the working frequency and physical cell identifier of the pseudo base station.

[0091] Step 5: Successfully determine that there is a fake base station in the target area, and feed it back to the scene factor of the scene model of that place to increase the weight.

[0092] Step S810: connecting the test terminal to the identified pseudo base station cell in the target area according to the working frequency and the physical cell identifier, specifically, locking the test terminal to the working frequency of the pseudo base station and connecting to the cell;

[0093] Step S812: Start the GPS positioning module of the test terminal to obtain the GPS coordinates of the test terminal; specifically, the GPS coordinates are obtained by the following steps: Step 1: Start the GPS module of the test terminal; Step 2: Obtain the GPS coordinates of the test terminal (X i , Y i ), with an accuracy of meters;

[0094] Step S814: measuring the reference signal power and the received reference signal power of the pseudo base station again from the test terminal;

[0095] Step S816: Calculate the path loss according to the reference signal power and the received reference signal power, and calculate the distance radius between the test terminal and the pseudo base station through the path loss;

[0096] In an optional embodiment, obtaining the distance radius between the test terminal and the pseudo base station needs to include the following steps:

[0097] Step 1: Obtain the broadcast message of the pseudo base station cell from the test terminal;

[0098] Step 2: Obtain the reference signal power (reference Signal Power) and the received reference signal power (Reference Signal Receiving Power, referred to as RSRP) of the pseudo base station from the broadcast message field;

[0099] Step 3: Calculate the path loss PL = reference Signal Power - RSRP;

[0100] Step 4: Calculate the distance radius between the test terminal and the pseudo base station based on the propagation model in the standard protocol with an accuracy of meters, where PL is the path loss and f is the frequency point:

[0101]

[0102] Step S818: Calculate the initial moving path of the terminal according to the GPS coordinates and distance radius of the test terminal, such as Figure 4-5As shown, the solution for obtaining the initial moving path may include the following steps:

[0103] Step 1: According to the above steps, the initial position coordinates (X1, Y1), initial path loss PL1, and initial distance radius d1 of the test terminal are obtained;

[0104] Step 2: Draw a circle with a distance radius of d1, where the initial position 1 is the upper vertex of the circle;

[0105] Step 3: Get the coordinates of each position of the circle, where the key position 2 is (X1-d1, Y1-d1), position 3 is (X1, Y1-d1), and position 4 is (X1+d1, Y1+d1), with an accuracy of meters.

[0106] Step 4: The circle serves as the initial moving path of the test terminal and is a reference for the subsequent actual moving angle and distance of the test terminal.

[0107] Step S820: The test terminal continuously moves and measures according to the route, and continuously optimizes the moving route through the reinforcement learning algorithm, such as Figure 6 As shown, the adjustment scheme for the initial moving path may include the following steps:

[0108] Step 1: Determine the initial position coordinates (X1, Y1), initial path loss PL1, and initial distance radius d1 of the test terminal;

[0109] Step 2: Given an initial moving angle α1 and an initial moving distance b1, move according to these parameters;

[0110] Step 3: After arriving at the designated location 2, the location coordinates (X2, Y2) are obtained through the GPS module, and the road loss PL2 and the distance radius d2 are calculated;

[0111] Step 4: Use the reinforcement learning algorithm to train the location coordinates, path loss, distance radius, moving angle, moving distance and other data of location 1 and location 2, where the difference in distance radius d = d1-d2 is the reward function of the reinforcement learning algorithm;

[0112]

[0113] Step 5: Calculate the next moving angle α2 and moving distance b2 through the reinforcement learning algorithm with an accuracy of meters, and move according to these parameters;

[0114] Step 6: Repeat the training and calculation process from step 3 to step 5;

[0115] Step 7: The test terminal continues to move and measure according to the movement parameters until the end point of the movement coincides with the starting point.

[0116] Step S822: Calculate the precise location coordinates of the pseudo base station.

[0117] To calculate the precise coordinates of the fake base station, the following steps are required:

[0118] Step 1: Record the GPS coordinates of the test terminal after N moves [(X1, Y1), (X2, Y2), ..., (X N ,Y N )];

[0119] Step 2: Calculate the coordinates of the pseudo base station using the centroid positioning algorithm. Since the accuracy of all the above mobile coordinate calculation processes is at the meter level, the final positioning accuracy reaches the meter level:

[0120]

[0121]

[0122] The optional embodiment of the present application proposes a pseudo base station identification and positioning technology based on intelligence. Through a test terminal, it is possible to accurately identify and locate pseudo base stations, greatly improving the accuracy of pseudo base station identification while greatly improving the efficiency of pseudo base station identification. In addition, the identification time can be shortened to within the hour level, and the hundred-meter accuracy of conventional pseudo base station positioning technology can be improved to the meter level. There is no need for multiple terminals and even vehicles to conduct network inspections, which reduces cost investment and improves work efficiency. In addition, the present application uses a pseudo base station scenario model to accurately screen vulnerable areas and places, which can shorten the identification time to within the hour level, and at the same time improve the hundred-meter accuracy of conventional pseudo base station positioning technology to the meter level.

[0123] Since the pseudo base station identification model provided by the embodiment of the present application can set a dedicated pseudo base station identification model for different areas, the embodiment of the present application can be well applied in the following scenarios: 1) densely populated and complex scenes. For example, in squares and stations, the safety awareness of personnel is uneven and the environment is relatively complex. Pseudo base stations may cause network security risks by initiating false alarms, rumors, etc., which will cause relatively bad social impacts. Through the technical solution of the embodiment of the present application, network operators and social managers can be assisted to efficiently avoid or block network security risks caused by pseudo base stations, and locate potential pseudo base stations in the scene with high precision, and quickly deal with pseudo base stations. 2) In scenes such as scientific research and campuses, personnel are engaged in many network activities, and pseudo base stations may cause property losses, information leakage and other serious security risks to park personnel through telecommunications fraud, malicious attacks and other behaviors. By using the technical solution of the embodiment of the present application, network operators and park managers can be assisted to quickly identify and accurately locate pseudo base stations, and eliminate dangers in a timely manner to prevent the further expansion of bad network attack behaviors and risks. 3) In national and provincial scale deployment scenarios, fake base stations are generally deployed in a scattered manner and their attacks are highly targeted. By using the technical solutions of the embodiments of the present application and deploying them in provinces and across the country, network operators and national managers can obtain a more comprehensive picture of the attack scenarios, locations and characteristics of fake base stations, and further analyze the attack portraits of fake base stations, providing effective data and feature support for the targeted identification and positioning of fake base stations and rapid troubleshooting.

[0124] In this embodiment, a positioning device for a pseudo base station is also provided, which is used to implement the above embodiments and preferred implementation modes, and will not be repeated hereafter. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0125] Fig.12 is a structural block diagram of a positioning device for a pseudo base station according to an embodiment of the present application. Fig.12 As shown, the positioning device of the pseudo base station includes:

[0126] An acquisition module 120 is used to acquire the operating frequency and cell identifier of the pseudo base station cell in the target area;

[0127] An access module 122, configured to connect the test terminal to the pseudo base station cell corresponding to the cell identifier according to the working frequency;

[0128] A first determining module 124, configured to determine a first distance between the test terminal and the pseudo base station according to first path loss data between the test terminal and the pseudo base station;

[0129] The second determination module 126 is used to determine the position of the pseudo base station based on the moving path corresponding to the first distance, wherein the moving path is a path obtained by adjusting the initial path corresponding to the test terminal according to a preset adjustment rule, and the preset adjustment rule is used to determine the moving angle and / or moving distance of the test terminal at different position points on the moving path.

[0130] Through the present application, a test terminal is introduced, and the test terminal is connected to the pseudo base station cell corresponding to the cell identifier according to the working frequency of the pseudo base station cell, and the first distance between the test terminal and the pseudo base station is determined according to the first path loss data between the test terminal and the pseudo base station; the position of the pseudo base station is determined based on the moving path corresponding to the first distance, wherein the moving path is a path obtained by adjusting the initial path corresponding to the test terminal according to a preset adjustment rule, and the preset adjustment rule is used to determine the moving angle and / or moving distance of the test terminal at different position points on the moving path, thereby solving the technical problem of low positioning accuracy of pseudo base stations in related technologies and providing an accurate positioning solution for pseudo base stations.

[0131] In an exemplary embodiment, the second determination module 126 is also used to obtain N coordinate information corresponding to the N positions of the test terminal on the moving path, where N is an integer greater than 2; and determine the position of the pseudo base station based on the N coordinate information corresponding to the N positions of the test terminal.

[0132] In an exemplary embodiment, the acquisition module 120 is also used to obtain a training set used to train a pseudo base station identification model; determine a plurality of neurons activated by the training set and weight values ​​corresponding to the plurality of neurons; obtain the pseudo base station identification model by training according to the plurality of neurons, the weight values ​​corresponding to the plurality of neurons and the scenario factor quantized after the scenario model of the target area; and determine whether the pseudo base station exists in the target area through the pseudo base station identification model.

[0133] In an exemplary embodiment, the acquisition module 120 is also used to send a collection task to a real base station in the target area, wherein the collection task is used to collect input data of the pseudo base station identification model; receive the input data sent by the real base station, and input the input data into the pseudo base station identification model to obtain an output result of the pseudo base station identification model; and determine whether there is a pseudo base station in the target area based on the output result.

[0134] In an exemplary embodiment, the first determination module 124 is also used to obtain the reference signal power of the pseudo base station from the broadcast message of the pseudo base station cell; and determine the first path loss data based on the reference signal power of the pseudo base station and the reference signal power received by the test terminal.

[0135] In an exemplary embodiment, the first determination module 124 is also used to determine the initial moving path of the test terminal based on the initial position coordinates of the test terminal accessing the pseudo base station cell and the first distance, wherein the initial moving path is a closed-loop graph; and the initial moving path is adjusted according to the preset adjustment rules to obtain the moving path.

[0136] In an exemplary embodiment, the first determination module 124 is also used to determine a first moving angle and a first moving distance of the test terminal; control the test terminal to move from the initial position corresponding to the initial position coordinates to a first position according to the first moving angle and the first moving distance; control the test terminal to move from the first position to a second position according to a second moving angle and a second moving distance; control the test terminal to move from the second position to the remaining positions in sequence until the test terminal finally moves to the initial position, wherein the moving path includes: the initial position, the first position, the second position and the remaining positions.

[0137] In an exemplary embodiment, the first determination module 124 is also used to determine the fourth position coordinates of the test terminal at the first position, the second path loss data between the test terminal at the first position and the pseudo base station, and the second distance between the test terminal at the first position and the pseudo base station; reinforcement learning is performed on the target parameters through a reinforcement learning algorithm, and the second moving angle and the second moving distance corresponding to the test terminal are obtained from the results of the reinforcement learning, wherein the target parameters include: the first moving angle, the first moving distance, the initial coordinates, the first distance, the first path loss data, the fourth position coordinates, the second path loss data and the second distance.

[0138] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a readable storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of each embodiment of the present application.

[0139] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0140] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0141] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0142] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0143] S1, obtaining the working frequency and cell identifier of the pseudo base station cell in the target area.

[0144] S2: Access the test terminal to the pseudo base station cell corresponding to the cell identifier according to the working frequency, and determine a first distance between the test terminal and the pseudo base station according to first path loss data between the test terminal and the pseudo base station.

[0145] S3. Determine the position of the pseudo base station based on a moving path corresponding to the first distance, wherein the moving path is a path obtained by adjusting an initial path corresponding to the test terminal according to a preset adjustment rule, and the preset adjustment rule is used to determine a moving angle and / or moving distance of the test terminal at different position points on the moving path.

[0146] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0147] Optionally, in this embodiment, the electronic device may also be configured to execute steps S1, S2 and S3 via a computer program.

[0148] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0149] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0150] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for locating a pseudo base station, characterized in that: include: Obtain the operating frequency and cell identifier of the pseudo base station cell in the target area; Accessing the test terminal to the pseudo base station cell corresponding to the cell identifier according to the working frequency, and determining a first distance between the test terminal and the pseudo base station according to first path loss data between the test terminal and the pseudo base station; The position of the pseudo base station is determined based on a moving path corresponding to the first distance, wherein the moving path is a path obtained by adjusting an initial path corresponding to the test terminal according to a preset adjustment rule, and the preset adjustment rule is used to determine a moving angle and / or moving distance of the test terminal at different position points on the moving path.

2. The method for locating a pseudo base station according to claim 1, characterized in that: Determining the position of the pseudo base station based on the moving path corresponding to the first distance includes: Obtaining N coordinate information corresponding to N positions of the test terminal on the moving path, where N is an integer greater than 2; The location of the pseudo base station is determined according to the N coordinate information corresponding to the N locations of the test terminal.

3. The method for locating a pseudo base station according to claim 1, characterized in that: Before obtaining the working frequency and cell identifier of the pseudo base station cell in the target area, the method further includes: Obtain a training set used for training a fake base station identification model; Determining a plurality of neurons activated by the training set and weight values ​​corresponding to the plurality of neurons respectively; The pseudo base station identification model is obtained by training the plurality of neurons, the weight values ​​respectively corresponding to the plurality of neurons, and the scene factor quantized by the scene model of the target area; The pseudo base station in the target area is determined by using the pseudo base station identification model.

4. The method for locating a pseudo base station according to claim 3, characterized in that: After obtaining the pseudo base station identification model through training according to the plurality of neurons, the weight values ​​respectively corresponding to the plurality of neurons and the scene factor quantized by the scene model of the target area, the method further includes: Sending a collection task to a real base station in the target area, wherein the collection task is used to collect input data of the pseudo base station identification model; Receiving the input data sent by the real base station, and inputting the input data into the pseudo base station identification model to obtain an output result of the pseudo base station identification model; Determine whether there is a fake base station in the target area according to the output result.

5. The method for locating a pseudo base station according to claim 1, characterized in that: Before determining the first distance between the test terminal and the pseudo base station according to the first path loss data between the test terminal and the pseudo base station, the method further includes: Acquire the reference signal power of the pseudo base station from the broadcast message of the pseudo base station cell; The first path loss data is determined according to the reference signal power of the pseudo base station and the reference signal power received by the test terminal.

6. The method for locating a pseudo base station according to claim 1, characterized in that: Before determining the position of the pseudo base station based on the moving path corresponding to the first distance, the method further includes: Determining an initial moving path of the test terminal according to the initial position coordinates of the test terminal accessing the pseudo base station cell and the first distance, wherein the initial moving path is a closed loop graph; The initial moving path is adjusted according to the preset adjustment rule to obtain the moving path.

7. The method for locating a pseudo base station according to claim 6, characterized in that: The initial moving path is adjusted according to the preset adjustment rule to obtain the moving path, including: Determining a first moving angle and a first moving distance of the test terminal; Controlling the test terminal to move from the initial position corresponding to the initial position coordinates to a first position according to the first moving angle and the first moving distance; Control the test terminal to move from the first position to a second position according to a second moving angle and a second moving distance; The test terminal is controlled to move from the second position to other positions in sequence until the position to which the test terminal finally moves is the initial position, wherein the moving path includes: the initial position, the first position, the second position and the other positions.

8. The method for locating a pseudo base station according to claim 7, characterized in that: Before controlling the test terminal to move from the initial position corresponding to the initial position coordinates to the first position according to the first moving angle and the first moving distance, the method further includes: Determine a fourth position coordinate of the test terminal at the first position, second path loss data between the test terminal at the first position and the pseudo base station, and a second distance between the test terminal at the first position and the pseudo base station; Reinforcement learning is performed on the target parameters through a reinforcement learning algorithm, and the second moving angle and the second moving distance corresponding to the test terminal are obtained from the results of the reinforcement learning, wherein the target parameters include: the first moving angle, the first moving distance, the initial coordinates, the first distance, the first path loss data, the fourth position coordinates, the second path loss data and the second distance.

9. A positioning device for a pseudo base station, characterized in that: include: An acquisition module, used to acquire the working frequency and cell identifier of the pseudo base station cell in the target area; An access module, used to connect the test terminal to the pseudo base station cell corresponding to the cell identifier according to the working frequency; A first determining module, configured to determine a first distance between the test terminal and the pseudo base station according to first path loss data between the test terminal and the pseudo base station; A second determination module is used to determine the position of the pseudo base station based on a moving path corresponding to the first distance, wherein the moving path is a path obtained by adjusting the initial path corresponding to the test terminal according to a preset adjustment rule, and the preset adjustment rule is used to determine the moving angle and / or moving distance of the test terminal at different position points on the moving path.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 8 when executed.

11. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the method according to any one of claims 1 to 8 through the computer program.

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