False base station positioning method and apparatus, storage medium, and electronic apparatus

Abstract

The present disclosure provides a false base station positioning method and apparatus, a storage medium, and an electronic apparatus. The method comprises: acquiring a working frequency and a cell identity of a false base station cell in a target region; causing a test terminal to access, according to the working frequency, the false base station cell corresponding to the cell identity, and, on the basis of first path loss data between the test terminal and the false base station, determining a first distance between the test terminal and the false base station; and, on the basis of a movement path corresponding to the first distance, determining the location of the false base station, the movement path being a path obtained after an initial path corresponding to the test terminal is adjusted according to a preset adjustment rule, and the preset adjustment rule being used for determining a movement angle and / or a movement distance of the test terminal at different position points of the movement path.

Description

Positioning method, device, storage medium and electronic device for pseudo base station

[0001] This disclosure claims priority to the Chinese patent application filed with the China Patent Office on October 27, 2023, with application number 202311433347.0 and invention name “Positioning method, device, storage medium and electronic device for pseudo base station”, the entire contents of which are incorporated by reference in this disclosure. Technical Field

[0002] The present disclosure 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

[0003] In related technologies, attackers use simple wireless devices and dedicated open-source software to simulate and disguise themselves as existing base stations, known as rogue base stations. As shown in Figure 1, rogue base stations use strong signals to attract nearby end users to their cell. Rogue base stations can deceptively attack terminals and disrupt normal communications between them and the carrier network, impacting the carrier network's performance, such as causing handover failures and abnormal call drops. They can also cause terminals to disconnect from the carrier network, attracting them to the rogue base station.

[0004] When fake base stations attack legitimate end users and operator networks, they can cause the following three typical interference scenarios:

[0005] 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.

[0006] Commonly used techniques for identifying fake base stations can be broadly divided 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, fake base stations intentionally increase their signal strength significantly when attacking, while the signal strength of ordinary base stations is typically within a certain range. Therefore, fake base stations can be detected by monitoring abnormal signal strength. For detection methods based on communication network parameters, detection is generally performed by checking a comprehensive database of network-wide parameters and then analyzing the parameters of the entire network. For example, this involves counting the number of abnormal network handoffs and using the Location Area Code (LAC) to analyze whether the distance between the user and the base station is within a reasonable range.

[0007] The following problems exist in the currently used pseudo base station identification and location technologies:

[0008] Operators' live network environments are extremely complex. Due to factors such as building obstruction, antenna position / angle, and the number of users, network information parameters vary widely across regions, creating highly diverse regional characteristics. A single, unchanging pseudo-base station identification model has very low accuracy. For example, if areas A and B experience the same network KPI issue (abnormal handover failure and dropped calls), current pseudo-base station identification technology would identify both as pseudo-base stations. However, actual on-site investigations revealed that the issue was a pseudo-base station in area A, while normal network fluctuations were the cause in area B.

[0009] With regard to the problem of low positioning accuracy of pseudo base stations in related technologies, no effective solution has been proposed so far.

[0010] Therefore, it is necessary to improve the related technology to overcome the above-mentioned defects in the related technology.

[0011] Summary of the Invention

[0012] The present disclosure provides a method, device, storage medium, and electronic device for positioning a pseudo base station, so as to at least solve the problem of low positioning accuracy of pseudo base stations in related technologies.

[0013] According to one aspect of the present disclosure, a method for locating a pseudo base station is provided, comprising: 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.

[0014] 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, 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 test terminal at the N positions.

[0015] 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 the pseudo base station identification model; determining the multiple neurons activated by the training set and the weight values ​​corresponding to the multiple neurons; 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; and determining whether the pseudo base station exists in the target area through the pseudo base station identification model.

[0016] In an exemplary embodiment, after the pseudo base station identification model is obtained by 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, 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 based on the output result.

[0017] 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 further 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.

[0018] 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.

[0019] 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 the 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 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 remaining positions.

[0020] In an exemplary embodiment, before controlling the test terminal to move from the initial position corresponding to the initial position coordinate 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.

[0021] According to another aspect of the present disclosure, a positioning device for a pseudo base station is also provided, including: an acquisition module, configured to acquire the working frequency and cell identifier of the pseudo base station cell within the target area; an access module, configured 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, configured 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, configured 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.

[0022] According to another aspect of the present disclosure, 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.

[0023] According to another aspect of the present disclosure, an electronic device is 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.

[0024] Through the present disclosure, by introducing a test terminal, 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 based on 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 the 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, which solves the technical problem of low positioning accuracy of pseudo base stations in related technologies and provides an accurate positioning solution for pseudo base stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings described herein are used to provide a further understanding of the present disclosure and constitute a part of the present disclosure. The exemplary embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation of the present disclosure. In the drawings:

[0026] FIG1 is a schematic diagram of a usage scenario of a pseudo base station in the related art;

[0027] FIG2 is a hardware structure block diagram of a computer terminal for the pseudo base station positioning method disclosed herein;

[0028] FIG3 is a flow chart (I) of a method for locating a pseudo base station according to the present disclosure;

[0029] FIG4 is a schematic diagram of an initial movement path according to the present disclosure;

[0030] FIG5 is a schematic diagram of a path for adjusting an initial moving path according to the present disclosure;

[0031] FIG6 is another schematic diagram of adjusting the initial moving path according to the present disclosure;

[0032] FIG7 is a schematic diagram of a device for model training and pseudo base station identification according to the present disclosure;

[0033] FIG8 is an overall flow chart of a method for locating a pseudo base station according to the present disclosure;

[0034] FIG9 is a schematic diagram of constructing a scenario model according to the present disclosure;

[0035] FIG10 is a schematic diagram of training a pseudo base station identification model according to the present disclosure;

[0036] FIG11 is another training diagram of a pseudo base station identification model according to the present disclosure;

[0037] FIG12 is a structural block diagram of a positioning device for a pseudo base station according to the present disclosure. DETAILED DESCRIPTION

[0038] In order to enable those skilled in the art to better understand the disclosed solutions, the technical solutions of the disclosed solutions will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the disclosed solutions, not all of them. Based on the embodiments of the disclosed solutions, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of protection of the disclosed solutions.

[0039] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure 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 numbers used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes 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.

[0040] The method embodiments provided in the present disclosure 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 the pseudo base station positioning method disclosed in the present disclosure. As shown in Figure 2, the computer terminal may include one or more (only one is shown in Figure 2) processors 202 (the processor 202 may include but is not limited to a microprocessor (Microprocessor Unit, abbreviated as MPU) or a programmable logic device (Programmable logic device, abbreviated as PLD)) and a memory 204 for storing data. In an exemplary embodiment, the above-mentioned 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 the structure shown in Figure 2 is only for illustration, and it does not limit the structure of the above-mentioned computer terminal. For example, the computer terminal may also include more or fewer components than those shown in Figure 2, or have a different configuration with the same function as shown in Figure 2 or more functions than those shown in Figure 2.

[0041] 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 present disclosure. The processor 202 executes various functional applications and data processing by running the computer program stored in the memory 204, that is, implementing 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 located relative to the processor 202, and these remote memories can 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 a combination thereof.

[0042] Transmission device 206 is used to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by a communications provider of a computer terminal. In one embodiment, transmission device 206 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, transmission device 206 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0043] FIG3 is a flow chart (I) of a method for locating a pseudo base station according to the present disclosure. As shown in FIG3 , the method includes the following steps:

[0044] Step S302: Acquire the operating frequency and cell identifier of the pseudo base station cell in the target area.

[0045] 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 based on first path loss data between the test terminal and the pseudo base station.

[0046] Step S306: Determine the position of the pseudo base station based on the moving path corresponding to the first distance, wherein the moving path is the 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.

[0047] Through the present disclosure, by introducing a test terminal, 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 based on 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 the 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, which solves the technical problem of low positioning accuracy of pseudo base stations in related technologies and provides an accurate positioning solution for pseudo base stations.

[0048] There are multiple implementations of step S306 in the present disclosure. In one optional embodiment, an initial movement path is first determined, and then the initial movement path is adjusted to obtain the movement path. The location of the pseudo base station is then determined based on the N coordinate information of the N positions on the movement path. Specifically, the initial movement 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 movement path is a closed loop graph. The initial movement path is then adjusted according to the preset adjustment rules to obtain the movement path.

[0049] Optionally, the initial movement path can be set to a circle. As shown in Figure 4, the initial position 1 in Figure 4 is equivalent to the initial position coordinates of the above embodiment. The distance radius d1 can be determined based on the above first distance and the initial position coordinates. The predicted position 3 is determined based on the distance radius d1, and the predicted position 2 and predicted position 4 can be determined based on the initial position 1, the predicted position 3 and the distance radius d1. Then, based on the initial position 1, the predicted positions 2-4, and the distance radius d1, the initial movement path can be comprehensively determined. That is, the circle shown in Figure 4 can be understood as an optional initial movement path of the above embodiment.

[0050] 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. The N coordinate information corresponding to the N positions of the test terminal on the moving path can be obtained, wherein N is an integer greater than 2; and the position of the pseudo base station is determined according to the N coordinate information corresponding to the N positions of the test terminal. Optionally, as shown in FIG5 , the N positions on the moving path can be position 2, position 3, ..., position N determined after adjustment, and also include the initial position 1. FIG6 further shows a plurality of position points after adjustment of different position points from position 1 to position 3, and the position of the pseudo base station is determined based on the plurality of adjusted position points.

[0051] It should be noted that the initial movement path is adjusted according to the preset adjustment rule to obtain the movement path, which can be achieved by the following scheme: as shown in Figure 5, determining the first movement angle (equivalent to the initial movement angle a1 in Figure 5) and the first movement distance (equivalent to the initial movement distance b1 in Figure 5) of the test terminal; controlling 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 position 2 in Figure 5) according to the first movement angle and the first movement distance; controlling the test terminal to move from the first position to the second position (equivalent to position 3 in Figure 5) according to the second movement angle (equivalent to the movement angle a2 in Figure 5) and the second movement distance (equivalent to the movement distance b2 in Figure 5); 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 (that is, the test terminal moves back to the initial position 1 in Figure 5), wherein the movement path includes: the initial position, the first position, the second position and the remaining positions.

[0052] 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 moving distance. The scheme for determining the moving angle and moving distance can 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.

[0053] The above-described embodiment utilizes an important parameter: first path loss data. This disclosure 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 based on the first path loss data between the test terminal and the pseudo base station, the reference signal power of the pseudo base station is obtained from a broadcast message of the pseudo base station cell; and the first path loss data is determined based on the reference signal power of the pseudo base station and the reference signal power received by the test terminal.

[0054] Before executing the above-mentioned solution of steps S302 to 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 steps S302 to S306. Then, the present disclosure also provides an implementation scheme for determining whether there is a fake base station. Specifically, it can be implemented by the following steps:

[0055] Step 1: Obtain the training set used to train the fake base station identification model;

[0056] Step 2: Determine the multiple neurons activated by the training set and the weight values ​​corresponding to the multiple neurons;

[0057] Step 3: obtaining the pseudo base station identification model through training based on the multiple neurons, the weight values ​​corresponding to the multiple neurons, and the quantized scenario factor of the scenario model of the target area;

[0058] Step 4: Determine whether the pseudo base station exists in the target area using the pseudo base station identification model.

[0059] 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, a collection task is sent 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.

[0060] In this disclosure, the aforementioned implementation allows for intelligent adjustment of the pseudo-base station identification model based on the network characteristics of the operator in each target area. This model eliminates regionally diverse network interference and significantly improves pseudo-base station identification accuracy. After pseudo-base station identification using this model, pseudo-base station positioning technology based on reinforcement learning is then used to locate the pseudo-base station, significantly improving its positioning accuracy.

[0061] In order to better understand the implementation scheme of the above-mentioned pseudo base station positioning method, optional embodiments of the present disclosure are subsequently provided for explanation, but are not used to limit the present disclosure.

[0062] In this optional embodiment, as shown in FIG7 , first, the pseudo base station scenario model 70 is used to accurately screen 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 the target area has a pseudo base station (the identification model training module 72, the data acquisition module 74, and the reasoning and judgment module 76 in FIG7 can be understood as serving the pseudo base station identification model. Specifically, the identification model training module 72 is used to train the pseudo base station identification model, the data acquisition module 74 is used to collect data used to train the pseudo base station identification model, and the reasoning and judgment module 76 is used in the pseudo base station identification model to determine whether there is a pseudo base station in the target area). Then a test terminal is placed in the area, and the access module 78 is set to access the pseudo base station cell. 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 feeds 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.

[0063] An optional embodiment of the present disclosure also provides an overall flow chart of a pseudo base station positioning method, as shown in FIG8 . The method includes the following steps:

[0064] Step S802: Build a scenario model, especially for places that are vulnerable to fake base station attacks, such as shopping malls, stations, schools, scientific research institutions, etc. The model is input 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;

[0065] The implementation of building the scenario model in step S802 may include building a physical model of the scenario and a channel model, as shown in FIG9 , which is specifically divided into the following steps:

[0066] 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 scenarios where different types of fake base stations appear, such as shopping malls, stations, schools, scientific research institutions, etc.

[0067] Step 2: Select a location that is vulnerable to fake base station attacks and conduct detailed modeling of the scenario.

[0068] 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.

[0069] Step 4: Report the off-site environment modeling data from step 3 to the scenario module.

[0070] 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.

[0071] Step 6: Report the in-venue environment modeling data from step 5 to the scenario module.

[0072] Step 7: In the above-mentioned venue environment, use ray tracing technology and field test channel data to fine-tune the channel model.

[0073] Step 8: Use field test channel data to perform small-scale fine correction of the channel model.

[0074] Step S804: Based on the scenario model and the general neural network learning algorithm, abnormal network parameter data of historical pseudo base stations and network parameters of the area to be inspected are used for learning and training to obtain a region-specific pseudo base station identification model. In other words, step S804 can train a dedicated pseudo base station identification model for a specific area.

[0075] Optionally, a pseudo base station identification model that intelligently matches the network characteristics of the area to be inspected is constructed, as shown in FIG10 , including the following steps:

[0076] 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.

[0077] Step 2: Set the target detection area and collect historical network data of all real base stations in the target area to build a dedicated training set. The training set includes: base station RSSI value X3 and base station switching rate X4.

[0078] Step 3: As shown in Figure 11, construct neurons [X1, X2, X3, X4] 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 ,X43 ,...X 4n ].

[0079] Step 4: Each neuron is given an initial weight [W 1i ,W 2i ,W 3i ,W 4i ].

[0080] Step 5: Quantify the scenario model of the target area into the scenario factor b.

[0081] Step 6: Quantize the weighted input of all neuron connections through the basis function, and then add it to the scene factor b to obtain the output V i , the calculation formula is:

[0082] Step 7: Then pass the output through the activation function f(·) to get the final output value Y i , the calculation formula is:

[0083] Step 8: Determine Y i Is it greater than or equal to 1? If so, it is determined to be a fake base station and the process ends; otherwise, go to step 7.

[0084] 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.

[0085] Step S806: Collecting data of the suspected pseudo base station area to be detected, that is, collecting data required by the dedicated pseudo base station identification model;

[0086] Step S808: Infer and determine whether there is a fake base station in the target area. If so, obtain the operating frequency and physical cell identifier of the fake base station cell; at the same time, feed the fake base station identification data back to the scenario model to expand the vulnerable fake base station attack scenarios and the accuracy of the attack scenarios; in addition, input the data into the fake base station training system to improve the training accuracy.

[0087] The specific process of identifying a fake base station may include the following steps:

[0088] Step 1: Determine the required data collection dimensions based on the pseudo base station 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 handover rates.

[0089] Step 2: Send data collection tasks to real base stations in the area.

[0090] Step 3: Collect data and report it to the pseudo base station identification model in the target area.

[0091] Step 4: Determine the pseudo base station and obtain the operating frequency and physical cell identifier of the pseudo base station.

[0092] 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.

[0093] Step S810: Connecting the test terminal to a pseudo base station cell that has been identified in the target area according to the operating frequency and physical cell identifier. Specifically, the test terminal is frequency-locked to the operating frequency of the pseudo base station and connected to the cell.

[0094] 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 meter level;

[0095] Step S814: The reference signal power and received reference signal power of the pseudo base station are measured again from the test terminal;

[0096] Step S816: Calculate the path loss based on the reference signal power and the received reference signal power, and calculate the distance radius between the test terminal and the pseudo base station based on the path loss;

[0097] In an optional embodiment, obtaining the distance radius between the test terminal and the pseudo base station requires the following steps:

[0098] Step 1: Obtain the broadcast message of the pseudo base station cell from the test terminal;

[0099] Step 2: Obtain the pseudo base station's reference signal power (RSRP) and received reference signal power (RSRP) from the broadcast message field.

[0100] Step 3: Calculate path loss PL = reference signal power - RSRP;

[0101] 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:

[0102] Step S818: Calculate the initial movement path of the terminal based on the GPS coordinates and distance radius of the test terminal. As shown in FIG4-5 , the solution for obtaining the initial movement path may include the following steps:

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

[0104] Step 2: Draw a circle with a 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 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 movement path of the test terminal and is a reference for the subsequent actual movement 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 movement route through a reinforcement learning algorithm. As shown in FIG6 , the adjustment scheme for the initial movement 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 designated location 2, obtain the location coordinates (X2, Y2) through the GPS module, and calculate the path loss PL2 and distance radius d2;

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

[0112] Step 5: Calculate the next movement angle α2 and movement distance b2 through the reinforcement learning algorithm with an accuracy of meters, and move according to these parameters;

[0113] Step 6: Repeat the training and calculation process from step 3 to step 5;

[0114] 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.

[0115] Step S822: Calculate the precise location coordinates of the pseudo base station.

[0116] To calculate the precise coordinates of the pseudo base station, the following steps are required:

[0117] Step 1: Record the GPS coordinates of the test terminal after N moves [(X1, Y1), (X2, Y2), ..., (X N ,Y N )];

[0118] Step 2: Calculate the pseudo base station coordinates 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:

[0119] The optional embodiment of the present disclosure 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. 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 carry out network inspections, which reduces cost investment and improves work efficiency. In addition, the present disclosure 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.

[0120] Since the pseudo base station identification model provided by the present disclosure can set up dedicated pseudo base station identification models for different areas, the present disclosure can be well applied in the following scenarios: 1) Densely populated and complex scenarios. For example, in squares and stations, people’s safety awareness varies and the environment they are in 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 present disclosure, network operators and social management personnel can be assisted to efficiently avoid or block network security risks caused by pseudo base stations, and locate potential pseudo base stations in the scenario with high precision, and quickly deal with pseudo base stations. 2) In scenarios such as scientific research and campuses, people often engage in network activities. Pseudo base stations may cause serious security risks such as property losses and information leaks to park personnel through telecommunications fraud, malicious attacks, etc. By using the technical solution of the present disclosure, network operators and park management personnel can be assisted to quickly identify and accurately locate pseudo base stations, eliminate dangers in a timely manner, and prevent the further expansion of bad network attacks and risks. 3) In nationwide and provincial-scale deployment scenarios, fake base stations are generally deployed in a dispersed manner and their attacks are highly targeted. By using the technical solution disclosed in this disclosure 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. They can further analyze the attack portraits of fake base stations, providing effective data and feature support for the targeted identification, positioning, and rapid investigation of fake base stations.

[0121] In this embodiment, a positioning device for a pseudo base station is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments. The details that have been described will not be repeated here. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.

[0122] FIG12 is a block diagram of a pseudo base station positioning device according to the present disclosure. As shown in FIG12 , the pseudo base station positioning device includes:

[0123] An acquisition module 120 is configured to acquire an operating frequency and a cell identifier of a pseudo base station cell within the target area;

[0124] The access module 122 is configured to connect the test terminal to the pseudo base station cell corresponding to the cell identifier according to the working frequency;

[0125] A first determining module 124 is configured to determine 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;

[0126] The second determination module 126 is configured 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.

[0127] Through the present disclosure, by introducing a test terminal, 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 based on 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 the 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, which solves the technical problem of low positioning accuracy of pseudo base stations in related technologies and provides an accurate positioning solution for pseudo base stations.

[0128] In an exemplary embodiment, the second determination module 126 is further configured 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 test terminal at the N positions.

[0129] In an exemplary embodiment, the acquisition module 120 is also configured to obtain a training set used for training a pseudo base station identification model; determine the multiple neurons activated by the training set and the weight values ​​corresponding to the multiple neurons; obtain 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; and determine whether the pseudo base station exists in the target area through the pseudo base station identification model.

[0130] In an exemplary embodiment, the acquisition module 120 is also configured 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.

[0131] In an exemplary embodiment, the first determination module 124 is further configured 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.

[0132] In an exemplary embodiment, the first determination module 124 is also configured 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.

[0133] In an exemplary embodiment, the first determination module 124 is further configured 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.

[0134] In an exemplary embodiment, the first determination module 124 is also configured 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; 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.

[0135] 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 the necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present disclosure, 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, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present disclosure.

[0136] 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.

[0137] 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 here.

[0138] An embodiment of the present disclosure further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0139] Optionally, in this embodiment, the processor may be configured to execute the following steps through a computer program:

[0140] S1, obtaining the operating frequency and cell identifier of the pseudo base station cell in the target area.

[0141] 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 based on first path loss data between the test terminal and the pseudo base station.

[0142] S3. Determine the position of the pseudo base station based on the moving path corresponding to the first distance, wherein the moving path is the 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.

[0143] 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.

[0144] Optionally, in this embodiment, the electronic device may also be configured to execute steps S1, S2 and S3 via a computer program.

[0145] 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 here.

[0146] Obviously, those skilled in the art should understand that the modules or steps of the present disclosure described above can be implemented using a general-purpose computing device, they can be concentrated on a single computing device, or distributed across a network composed of multiple computing devices, they can be implemented using program code executable by the computing device, and thus, 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 performed in a different order than herein, or they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. Thus, the present disclosure is not limited to any particular combination of hardware and software.

[0147] The foregoing description is merely a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Those skilled in the art will readily appreciate that various modifications and variations of the present disclosure are possible. Any modifications, equivalent substitutions, or improvements made within the principles of the present disclosure shall be included within the scope of protection of the present disclosure.

Claims

1. A method for locating a pseudo base station, comprising: 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, wherein: 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 respectively.

3. The method for locating a pseudo base station according to claim 1, wherein: Before acquiring 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, wherein: 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, wherein: 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, wherein: 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, wherein: 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, wherein: 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, comprising: An acquisition module is configured to acquire the working frequency and cell identifier of the pseudo base station cell in the target area; An access module, configured 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 configured 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. Leave.

10. A computer-readable storage medium, wherein a computer program is stored in the storage medium, 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, comprising 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.

Images