A method for identifying pseudo base stations based on a POS terminal

Abstract

The application discloses a kind of AI identification pseudo base station methods based on POS terminal, including POS terminal start-up frequency scanning registration network, record the frequency point EAFCN of service cell, TAC, cell ID and minimum access level, and store the information configured in system message SIB5;Search adjacent cell, record the signal strength of adjacent cell system message SIB1, SIB5 and;Parse adjacent cell SIB1 in TAC, and judge whether it is pseudo base station;If there are multiple adjacent cells, whether it is pseudo base station is judged by cell ID;Parse the minimum access level of adjacent cell system message SIB1 actually scanned and cell reselection priority in system message SIB3, and whether it is suspicious pseudo base station cell is judged by the minimum access level and the cell priority;When suspicious pseudo base station cell is rejected in terminal reselection registration network, it is judged as pseudo base station.The scheme can quickly identify pseudo base station, and is suitable for promotion and application.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to an AI-based method for identifying fake base stations based on a POS terminal. Background Technology

[0002] In order to collect user information, multiple different types of LTE fake base stations were deployed in urban areas. They used the frequency points of neighboring base stations with information from normal base stations, and increased the access priority and signal strength of fake base stations to guide cellular mobile network terminals to register with fake base stations. During the process of accessing fake base stations, user information was obtained, and then the terminal's access was rejected.

[0003] 3GPP does not define or identify fake base stations. Terminals, following 3GPP's guidelines, handle rejections based on different error reasons. After rejection, the terminal searches for other cells to register. Upon successful registration, it searches for neighboring cells and re-registers for the fake base station. Some rejection reasons require five rejections before the fake base station is disabled for a period. After the disabling timer expires, it reconnects. For example, Cause #9 (UE identity cannot be derived by the network) causes a period of network outage during fake base station registration. POS terminals (including cloud speakers) are primarily used for receiving and broadcasting payment information in fixed locations. During fake base station access, terminals may experience network outages, delays, or even failures, as well as delayed or missed broadcasts.

[0004] Currently, 3GPP does not define or handle fake base stations. Therefore, terminals reconnect to fake base stations based on the error value of being rejected by normal base stations, which seriously affects the normal transactions of POS terminals. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an AI-based method for identifying fake base stations based on POS terminals, thereby avoiding or reducing POS terminal connections to fake base stations, reducing POS terminal downtime, and thus minimizing the impact of fake base stations on cellular mobile network POS payment terminals.

[0006] To achieve the above-mentioned technical objectives, the technical solution adopted by this invention is: an AI-based method for identifying fake base stations based on a POS terminal, comprising:

[0007] S1: The POS terminal scans and registers the network upon power-on, records the serving cell's frequency point EAFCN, tracking area code TAC, cell ID (cellIdentity), and minimum access level q-RxLevMin, and stores the information configured in system message SIB5 locally on the terminal.

[0008] S2: Search and scan neighboring cells, and record the system information SIB1, SIB5 and signal strength of each neighboring cell;

[0009] S3: parse the TAC in neighboring cell SIB1 and determine whether it is a fake base station by using the TAC;

[0010] S4: If there are multiple neighboring cells, determine whether these cells are fake base stations by using the ID (cellIdentity) of the neighboring cells;

[0011] S5: Analyze the minimum access level q-RxLevMin of the actual scanned neighbor cell system message SIB1 and the cell reselection priority cellReselectionPriority in the system message SIB3, and determine whether it is a suspicious fake base station cell by using the cell priority cellReselectionPriority and the minimum access level q-RxLevMin.

[0012] If a cell is marked as a suspicious fake base station and the terminal is rejected when reselecting a network for registration, then the cell is determined to be a fake base station.

[0013] As one possible implementation, the information configured in SIB5 in step S1 further includes neighboring cell frequency points, TAC, and cellReselectionPriority.

[0014] As one possible implementation, further, in step S3, the determination of whether it is a fake base station is made by TAC, specifically as follows:

[0015] If the TAC in the neighboring cell SIB1 is not a valid 4-digit hexadecimal, it is judged to be a fake base station.

[0016] As one possible implementation, further, in step S4, it is determined whether these cells are fake base stations by using the IDs of neighboring cells. Specifically, the method is as follows:

[0017] If there are multiple neighboring cells, and two or more neighboring cells have the same cell ID, then these cells are determined to be fake base stations.

[0018] As a possible implementation, further, in step S5, it is determined whether the cell is a suspicious fake base station cell by using the cell priority (cellReselectionPriority) and the minimum access level (q-RxLevMin). Specifically, the method is as follows:

[0019] If the minimum access level of a cell is 20 dBm higher than that of a stored serving cell, or if its priority is higher than that of a stored neighboring cell at the same frequency point, then it is identified as a suspicious fake base station cell.

[0020] The present invention also provides a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement the above-described AI-based method for identifying fake base stations based on a POS terminal.

[0021] By adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art:

[0022] Compared to the behavior specified by 3GPP, this invention can quickly identify fake base stations, thereby avoiding or reducing POS terminal connections to fake base stations, shortening the POS terminal's network outage time, and improving the POS transaction success rate, making it suitable for further promotion and application. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a simplified flowchart of the present invention. Detailed Implementation

[0025] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be particularly noted that the following embodiments are for illustrative purposes only and do not limit the scope of the invention. Similarly, the following embodiments are only some, not all, embodiments of the present invention, and all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] See attached document Figure 1 As shown, this embodiment provides an AI-based method for identifying fake base stations based on a POS terminal, including the following steps:

[0027] S1: The POS terminal scans and registers the network upon power-on, recording the serving cell's frequency point EAFCN, tracking area code TAC, cell ID (cellIdentity), minimum access level q-RxLevMin, neighboring cell frequency points configured in system message SIB5, TAC, and cellReselectionPriority, etc., to the terminal's local storage.

[0028] S2: Search and scan neighboring cells, and record the system messages SIB1, SIB5 and signal strength of each neighboring cell.

[0029] S3: Parse the TAC in the neighboring cell SIB1. If the TAC is not a valid 4-digit hexadecimal, it is determined to be a fake base station.

[0030] S4: If there are multiple neighboring cells, and two or more neighboring cells have the same cell ID (cellIdentity), then these cells are determined to be fake base stations.

[0031] S5: Analyze the minimum access level q-RxLevMin in the actual scanned neighbor cell system message SIB1 and the cell reselection priority cellReselectionPriority in the system message SIB3; if the minimum access level of the cell is 20dBm higher than the stored serving cell, or the priority is higher than the priority of the stored neighbor cells at the same frequency, then mark it as a suspicious fake base station cell; if it is marked as a suspicious fake base station cell and the terminal is rejected when reselecting the registration network, then the cell is determined to be a fake base station.

[0032] The aforementioned AI-based method for identifying fake base stations based on POS terminals can effectively and quickly identify fake base stations, avoid repeated connections of the terminal to the base station (and even prevent the POS terminal from connecting to fake base stations), thereby shortening the network outage time, reducing the impact of fake base stations on cellular mobile network POS payment terminals, and improving the success rate of POS transactions.

[0033] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0034] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0035] The above description is only a part of the embodiments of the present invention and does not limit the scope of protection of the present invention. Any equivalent device or equivalent process transformation made based on the content of the present invention specification and drawings, or direct or indirect application in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for identifying fake base stations using AI based on a POS terminal, characterized in that, include: S1: The POS terminal powers on, scans the frequency and registers with the network, records the serving cell's frequency point EAFCN, tracking area code TAC, cell ID (cellIdentity), and minimum access level q-RxLevMin, and stores the information configured in system message SIB5 locally on the terminal; the information configured in SIB5 includes neighboring cell frequencies, TAC, and cellReselectionPriority. S2: Search and scan neighboring cells, and record the system information SIB1, SIB5 and signal strength of each neighboring cell; S3: Parse the TAC in neighboring cell SIB1 and determine whether it is a fake base station by the TAC. Specifically, if the TAC in neighboring cell SIB1 is not a valid 4-digit hexadecimal, it is determined to be a fake base station. S4: If there are multiple neighboring cells, determine whether these cells are fake base stations by their cell IDs (cellIdentity). Specifically, if there are multiple neighboring cells and two or more neighboring cells have the same cell ID (cellIdentity), then these cells are determined to be fake base stations. S5: Analyze the minimum access level q-RxLevMin of the actual scanned neighbor cell system message SIB1 and the cell reselection priority cellReselectionPriority in the system message SIB3, and determine whether it is a suspicious fake base station cell by using the cell priority cellReselectionPriority and the minimum access level q-RxLevMin. Specifically, if the minimum access level of the cell is 20dBm higher than the stored serving cell, or the priority is higher than the priority of the stored neighbor cells at the same frequency point, then mark it as a suspicious fake base station cell. If a cell is marked as a suspicious fake base station and the terminal is rejected when reselecting a network for registration, then the cell is determined to be a fake base station.

2. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the AI-based method for identifying fake base stations based on a POS terminal as described in claim 1.

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