Financial POS (Point Of Sale) equipment identity authentication method based on NFC (Near Field Communication) and related equipment

Abstract

The embodiment of the invention belongs to the field of financial POS equipment identity authentication, and relates to an NFC-based financial POS equipment identity authentication method, which comprises the following steps: generating a challenge signal through mother POS equipment; sending a challenge signal to the sub-POS device, and requiring the sub-POS device to provide authentication data; receiving authentication data of the sub POS equipment, wherein the authentication data comprises physical signal characteristics and behavior mode characteristics; verifying the authentication data by using a pre-trained machine learning model, wherein the verification comprises physical characteristic matching, behavior pattern matching and time window matching; and judging whether the sub POS equipment is credible equipment or not. The invention further provides an NFC-based financial POS equipment identity authentication system, computer equipment and a storage medium. The invention aims to improve the reliability and safety of authentication between financial POS devices.

Description

Technical Field

[0001] The present application relates to the technical field of financial POS device identity authentication, and in particular to an NFC-based financial POS device identity authentication method and related devices. Background Art

[0002] With the rapid development of information technology, the equipment in the financial payment industry has gradually become more intelligent and networked, especially the application of financial POS equipment in payment, authentication and key management. However, with the increase in financial transactions, the security issues of POS equipment have become more prominent. Especially in the links of key filling, identity authentication and communication between devices, how to effectively prevent security threats such as forgery, tampering and man-in-the-middle attacks has become a technical problem that the industry needs to solve urgently.

[0003] Currently, the identity authentication of financial POS devices generally relies on pre-set keys or digital certificates. When the device is manufactured and shipped, some public keys, private key pairs or digital certificates are pre-installed. When communicating between devices, these keys are used for encryption and decryption or digital signature verification. Common authentication mechanisms include verifying the public key or certificate of the device and using symmetric encryption keys for data encryption transmission. During mass production, the key or certificate of each device needs to be configured in advance, and the security of the key needs to be guaranteed during the life cycle of the device. Once the key is leaked, the security of the entire system will be threatened. Summary of the invention

[0004] The purpose of the embodiments of the present application is to propose a financial POS device identity authentication method and related devices based on NFC to solve the technical problem of insufficient reliability and security of authentication between financial POS devices.

[0005] In order to solve the above technical problems, the embodiment of the present application provides a financial POS device identity authentication method based on NFC, which adopts the following technical solution:

[0006] A financial POS device identity authentication method based on NFC, comprising the following steps:

[0007] Generate a challenge signal through the parent POS device;

[0008] Send a challenge signal to the sub-POS device, requiring the sub-POS device to provide authentication data;

[0009] receiving authentication data of a sub-POS device, the authentication data comprising physical signal characteristics and behavioral pattern characteristics;

[0010] Verifying the authentication data using a pre-trained machine learning model, including physical property matching, behavioral pattern matching, and time window matching;

[0011] Determine whether the sub-POS device is a trusted device.

[0012] In a possible implementation, the verification of the authentication data using a pre-trained machine learning model includes the steps of physical property matching, behavior pattern matching, and time window matching, and the training step of the machine learning model includes:

[0013] Collect physical signal characteristics and behavior pattern characteristics data, the physical signal characteristics include the strength, frequency, and polarization characteristics of the device's NFC signal, and the behavior pattern characteristics include the response delay, voltage change, and power consumption curve of the device's hardware interface;

[0014] Extract key features of physical signal characteristics and behavior pattern characteristic data through feature extraction methods;

[0015] The classification model is trained using supervised learning methods to classify and annotate the collected physical signal characteristics and behavior pattern characteristics data according to normal and abnormal labels;

[0016] Use the labeled physical signal characteristics and behavioral pattern characteristics data for model training.

[0017] In a possible implementation, the verification of the authentication data using a pre-trained machine learning model includes the steps of physical property matching, behavior pattern matching, and time window matching, including:

[0018] Setting corresponding weighting coefficients for the physical property matching, behavior pattern matching, and time window matching;

[0019] Calculating a weighted score according to the weighting coefficient and the degree of matching of the physical characteristic matching, the behavior pattern matching, and the time window matching;

[0020] Adding up the weighted scores to obtain a comprehensive score;

[0021] The comprehensive score is compared with a preset trust threshold. When the comprehensive score is greater than or equal to the trust threshold, the sub-POS device is determined to be a trustworthy device. When the comprehensive score is less than the trust threshold, the sub-POS device is determined to be an untrustworthy device.

[0022] In a possible implementation, in the step of setting corresponding weighting coefficients for the physical property matching, behavior pattern matching, and time window matching, the weighting coefficients are dynamically adjusted based on the accuracy, reliability, and environmental factors of the historical data of the physical property matching, behavior pattern matching, and time window matching.

[0023] In a possible implementation, in the step of comparing the comprehensive score with a preset trust threshold, determining that the sub-POS device is a trustworthy device when the comprehensive score is greater than or equal to the trust threshold, and determining that the sub-POS device is an untrustworthy device when the comprehensive score is less than the trust threshold, when the sub-POS device is determined to be an untrustworthy device, an alarm mechanism is triggered, an alarm notification is generated and sent to the parent POS device.

[0024] In a possible implementation, the step of generating a challenge signal by the parent POS device includes:

[0025] Obtaining a random challenge data, wherein the challenge data includes a device identifier, a timestamp, and a physical signal characteristic;

[0026] The parent POS device encrypts the random challenge data using its private key;

[0027] Send the encrypted challenge data to the sub-POS device.

[0028] In a possible implementation, in the step of receiving authentication data of the sub-POS device, wherein the authentication data includes physical signal characteristics and behavior pattern characteristics, after the step of receiving authentication data of the sub-POS device, the step further includes:

[0029] Performing validity checks on the authentication data, including verifying whether the signal strength and frequency are within a preset range and whether the behavior pattern characteristics conform to normal usage patterns;

[0030] Store authentication data in a temporary buffer.

[0031] In order to solve the above technical problems, the embodiment of the present application also provides a financial POS device identity authentication system based on NFC, which adopts the following technical solution:

[0032] A financial POS device identity authentication system based on NFC, comprising:

[0033] A generation module, used for generating a challenge signal through a parent POS device;

[0034] A sending module, used for sending a challenge signal to the sub-POS device, requiring the sub-POS device to provide authentication data;

[0035] A receiving module, used to receive authentication data of a sub-POS device, wherein the authentication data includes physical signal characteristics and behavior pattern characteristics;

[0036] A verification module, for verifying the authentication data using a pre-trained machine learning model, including physical property matching, behavior pattern matching, and time window matching;

[0037] The judgment module is used to judge whether the sub-POS device is a trusted device.

[0038] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0039] A computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the NFC-based financial POS device identity authentication method as described above are implemented.

[0040] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0041] A computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the NFC-based financial POS device identity authentication method as described above.

[0042] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0043] The NFC-based financial POS device identity authentication method disclosed in the present application generates a challenge signal through a parent POS device; then sends the challenge signal to a child POS device, requiring the child POS device to provide authentication data; then, receives the authentication data of the child POS device, the authentication data including physical signal characteristics and behavior pattern characteristics; uses a pre-trained machine learning model to verify the authentication data, including physical characteristic matching, behavior pattern matching, and time window matching; finally, determines whether the child POS device is a trusted device. In the present application, the parent POS device generates a challenge signal, the child POS device provides authentication data, and the authentication data is verified by a machine learning model, thereby realizing NFC-based device identity authentication and ensuring the credibility of the child POS device; by matching and verifying physical signal characteristics (such as intensity and frequency) and behavior pattern characteristics (such as response delay and power consumption), it can effectively prevent counterfeit devices from accessing the system, thereby improving the security and credibility of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0046] Figure 2 is a flow chart of an embodiment of an NFC-based financial POS device identity authentication method according to the present application;

[0047] Figure 3 It is a structural diagram of an embodiment of an NFC-based financial POS device identity authentication system according to the present application;

[0048] Figure 4 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0050] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0051] 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 accompanying drawings.

[0052] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0053] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0054] Terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 (Moving Picture Experts Group Audio Layer III) players, MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop computers, desktop computers, etc.

[0055] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0056] It should be noted that the NFC-based financial POS device identity authentication method provided in the embodiment of the present application is generally executed by a server, and accordingly, the NFC-based financial POS device identity authentication system is generally set in the server.

[0057] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0058] Continue to refer Figure 2 , shows a flow chart of an embodiment of a method for authenticating a financial POS device based on NFC according to the present application. The method for authenticating a financial POS device based on NFC comprises the following steps:

[0059] Step S201: Generate a challenge signal through a parent POS device.

[0060] In this embodiment, the NFC-based financial POS device identity authentication method is executed on the electronic device (eg Figure 1The server shown in the figure) can send or receive data through a wired connection or a wireless connection. It should be noted that the above wireless connection method may include but is not limited to 3G / 4G / 5G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0061] In this embodiment, the mother POS device first generates a challenge signal, which is usually a signal containing specific physical signal characteristics (such as signal strength, frequency, etc.), which can be used as a trigger for the child POS device to respond. The purpose of this signal is to verify whether the child POS device is qualified for authentication. The challenge signal can be transmitted via radio waves (such as NFC technology) and can contain information such as device identification and timestamp to ensure the uniqueness of each authentication process.

[0062] Step S202: Send a challenge signal to the sub-POS device, requiring the sub-POS device to provide authentication data.

[0063] In this embodiment, the master POS device sends the generated challenge signal to the slave POS device through wireless communication technology such as NFC, and after receiving the challenge signal, the slave POS device responds and sends authentication data. The authentication data usually includes the physical characteristics of the device (such as signal strength, frequency, etc.) and behavior pattern characteristics (such as operation history, power consumption, response delay, etc.).

[0064] Step S203: receiving authentication data of the sub-POS device, wherein the authentication data includes physical signal characteristics and behavior pattern characteristics.

[0065] In this embodiment, the master POS device receives authentication data sent by the slave POS device, and the authentication data is composed of physical signal characteristics and behavior pattern characteristics. The physical signal characteristics can help identify the communication characteristics and signal strength of the device, while the behavior pattern characteristics can reflect the normal operation mode of the device and help determine whether the behavior of the device is normal.

[0066] Step S204, using a pre-trained machine learning model to verify the authentication data, including physical property matching, behavior pattern matching, and time window matching.

[0067] In this embodiment, the parent POS device verifies the received authentication data through a trained machine learning model (e.g., a classification model). Physical property matching ensures that the communication signal of the device is within a reasonable range, behavioral pattern matching verifies whether the device operation conforms to the normal usage mode, and time window matching ensures that the operation time is consistent with the normal range.

[0068] Step S205: determine whether the sub-POS device is a trusted device.

[0069] In this embodiment, based on the verification result of the machine learning model, the parent POS device determines whether the child POS device is a trusted device. If the physical characteristics, behavior patterns, time windows, etc. are all in line with expectations and pass the model verification, the child POS device is determined to be a trusted device, otherwise it is an untrusted device.

[0070] In this application, the parent POS device generates a challenge signal, the child POS device provides authentication data, and verifies the authentication data through a machine learning model, thereby realizing NFC-based device identity authentication and ensuring the credibility of the child POS device; by matching and verifying physical signal characteristics (such as intensity, frequency) and behavioral pattern characteristics (such as response delay, power consumption), it can effectively prevent counterfeit devices from accessing the system, thereby improving the security and credibility of the system.

[0071] In some optional implementations of this embodiment, in the above-mentioned steps of verifying the authentication data using a pre-trained machine learning model, including physical property matching, behavior pattern matching, and time window matching, the training step of the machine learning model includes:

[0072] Collect physical signal characteristics and behavior pattern characteristics data, the physical signal characteristics include the strength, frequency, and polarization characteristics of the device's NFC signal, and the behavior pattern characteristics include the response delay, voltage change, and power consumption curve of the device's hardware interface;

[0073] Extract key features of physical signal characteristics and behavior pattern characteristic data through feature extraction methods;

[0074] The classification model is trained using supervised learning methods to classify and annotate the collected physical signal characteristics and behavior pattern characteristics data according to normal and abnormal labels;

[0075] Use the labeled physical signal characteristics and behavioral pattern characteristics data for model training.

[0076] In this embodiment, by obtaining data of physical signal characteristics (such as signal strength, frequency, polarization characteristics, etc.) and behavior pattern characteristics (such as device response delay, voltage change, power consumption curve, etc.) of real devices, these data will become the core components of the training data set, which are used to train the model to identify the normal and abnormal behaviors of the device. The feature extraction method is used to extract key features from the original data, which help the machine learning model to identify the normal behavior pattern of the device. For example, frequency stability, signal strength fluctuation and other features are extracted from the physical signal characteristics, and the regularity of power consumption, response time and other information are extracted from the behavior pattern characteristics. Supervised learning methods (such as decision trees, support vector machines, neural networks, etc.) are used to train the classification model. After data collection, it will be labeled according to the normal and abnormal behaviors of the device and input into the model for training. By learning these labeled data, the model can identify the normal or abnormal state of the device under different circumstances. Use the labeled data (normal and abnormal labels) for model training. After the training is completed, the model can predict the credibility and authentication results of the device based on the input authentication data.

[0077] This application can identify and distinguish the behavior patterns of normal and abnormal devices through the training of machine learning models. This makes the identity authentication system more adaptable and accurate, able to identify potential security threats in various environments, and improves recognition accuracy and anti-attack capabilities.

[0078] In some optional implementations of this embodiment, the verification of the authentication data using a pre-trained machine learning model, including the steps of physical property matching, behavior pattern matching, and time window matching, includes:

[0079] Setting corresponding weighting coefficients for the physical property matching, behavior pattern matching, and time window matching;

[0080] Calculating a weighted score according to the weighting coefficient and the degree of matching of the physical characteristic matching, the behavior pattern matching, and the time window matching;

[0081] Adding up the weighted scores to obtain a comprehensive score;

[0082] The comprehensive score is compared with a preset trust threshold. When the comprehensive score is greater than or equal to the trust threshold, the sub-POS device is determined to be a trustworthy device. When the comprehensive score is less than the trust threshold, the sub-POS device is determined to be an untrustworthy device.

[0083] In this embodiment, in the verification process of the machine learning model, in order to more flexibly evaluate the impact of different characteristics on the final verification result, different weighting coefficients can be assigned to each verification characteristic (such as physical characteristics, behavior pattern characteristics and time window matching). The weighting coefficients of different characteristics can be dynamically adjusted according to the actual situation, the accuracy of historical data, environmental factors, etc. After calculating the matching degree of each verification step (physical characteristics, behavior pattern, time window), it is multiplied by the corresponding weighting coefficient to obtain a weighted score. In this way, the importance of each characteristic to the overall authentication result can be more accurately evaluated. The weighted scores of each verification step are summed up to obtain a final comprehensive score. This comprehensive score can be used to judge the credibility of the device. The comprehensive score is compared with a preset trust threshold. If the comprehensive score is greater than or equal to the trust threshold, the mother POS device determines the child POS device as a trusted device; if it is less than the threshold, it is determined as an untrusted device, thereby deciding whether to continue the transaction or authentication.

[0084] By assigning different weights to each verification step, this application can more accurately evaluate the impact of each feature on the authentication result, thereby improving the flexibility and reliability of the authentication process. The introduction of dynamic weighting coefficients enables the authentication system to adapt to different environments and conditions, thereby maintaining efficient authentication results in a variety of different scenarios.

[0085] In some optional implementations of the present embodiment, in the above-mentioned steps of setting corresponding weighting coefficients for the physical property matching, behavior pattern matching and time window matching, the weighting coefficients are dynamically adjusted according to the accuracy, reliability and environmental factors of the historical data of the physical property matching, behavior pattern matching and time window matching.

[0086] In this embodiment, the weighting coefficient is not fixed but dynamically adjusted. The weighting coefficient is dynamically adjusted to adapt to different authentication environments through historical data analysis, the accuracy of signal characteristics in the actual authentication process, the reliability of behavior patterns, and changes in external environmental factors (such as temperature, interference, etc.).

[0087] This application improves the flexibility of the authentication process and enhances the adaptability and robustness of the system by adjusting the weighting coefficient in real time. This means that in different environments (such as signal interference, equipment failure, etc.), the authentication process can still ensure efficiency and accuracy, thereby improving the reliability of the system in complex environments.

[0088] In some optional implementations of the present embodiment, in the above step of comparing the comprehensive score with a preset trust threshold, when the comprehensive score is greater than or equal to the trust threshold, the sub-POS device is determined to be a trustworthy device, and when the comprehensive score is less than the trust threshold, the sub-POS device is determined to be an untrustworthy device, when the sub-POS device is determined to be an untrustworthy device, an alarm mechanism is triggered, an alarm notification is generated and sent to the parent POS device.

[0089] In this embodiment, if the authentication result is an untrusted device, the system will automatically trigger the alarm mechanism, generate an alarm notification and transmit it to the parent POS device in a timely manner, so that when counterfeiting or attack occurs, it can respond quickly and take subsequent measures (such as disconnecting, marking the device, etc.).

[0090] This application improves the system's protection capabilities by adding an alarm mechanism. When the system determines that the sub-POS device is untrustworthy, an alarm can be immediately issued. This design ensures that the system can respond to counterfeiting or attack behaviors in a timely manner, enhances security, and provides real-time monitoring and exception handling capabilities.

[0091] In some optional implementations of this embodiment, the step of generating a challenge signal by the parent POS device includes:

[0092] Obtaining a random challenge data, wherein the challenge data includes a device identifier, a timestamp, and a physical signal characteristic;

[0093] The parent POS device encrypts the random challenge data using its private key;

[0094] Send the encrypted challenge data to the sub-POS device.

[0095] In this embodiment, when the parent POS device generates a challenge signal, it will include a device identifier (ID), a timestamp (to prevent replay attacks), and physical signal characteristics. The challenge data is random, ensuring the uniqueness and security of each authentication process. The challenge data is encrypted using a private key, and only the parent POS device and the legal child POS device can decrypt it, ensuring confidentiality and integrity during data transmission. The parent POS device sends the encrypted challenge data to the child POS device, which needs to correctly decrypt and generate a corresponding response to complete the authentication process.

[0096] This application ensures the security of the challenge signal through encryption technology, effectively preventing man-in-the-middle attacks and data leakage. After the challenge signal is encrypted, it not only improves the security of identity authentication, but also prevents external illegal devices from forging challenge signals, thereby enhancing the anti-attack capability of the authentication system.

[0097] In some optional implementations of this embodiment, in the step of receiving the authentication data of the sub-POS device, wherein the authentication data includes physical signal characteristics and behavior pattern characteristics, after the step of receiving the authentication data of the sub-POS device, the step further includes:

[0098] Performing validity checks on the authentication data, including verifying whether the signal strength and frequency are within a preset range and whether the behavior pattern characteristics conform to normal usage patterns;

[0099] Store authentication data in a temporary buffer.

[0100] In this embodiment, the received authentication data is checked for validity to ensure that the signal strength, frequency, etc. meet the preset communication standards and that the behavior pattern characteristics meet expectations. For subsequent verification and processing, the authentication data is temporarily stored in a buffer for further analysis.

[0101] This application can detect abnormal and erroneous data during data transmission by checking the validity of the authentication data, thereby ensuring the integrity and accuracy of the data. By temporarily storing the authentication data, more time can be provided for verification and analysis, ensuring that the authentication process is more robust and avoiding the impact of transient errors on the authentication results.

[0102] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0103] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0104] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0105] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0106] Further references Figure 3 , as a response to the above Figure 2 The present application provides an embodiment of a financial POS device identity authentication system based on NFC. The embodiment of the system is similar to Figure 2 Corresponding to the method embodiment shown, the system can be specifically applied to various electronic devices.

[0107] like Figure 3 As shown, the NFC-based financial POS device identity authentication system 300 described in this embodiment includes: a generating module 301 , a sending module 302 , a receiving module 303 , a verifying module 304 and a judging module 305 .

[0108] in:

[0109] A generating module 301, configured to generate a challenge signal through a parent POS device;

[0110] The sending module 302 is used to send a challenge signal to the sub-POS device, requiring the sub-POS device to provide authentication data;

[0111] A receiving module 303, configured to receive authentication data of a sub-POS device, wherein the authentication data includes physical signal characteristics and behavior pattern characteristics;

[0112] A verification module 304, for verifying the authentication data using a pre-trained machine learning model, including physical property matching, behavior pattern matching, and time window matching;

[0113] The determination module 305 is used to determine whether the sub-POS device is a trusted device.

[0114] The NFC-based financial POS device authentication system provided by the present application, in which the parent POS device generates a challenge signal, the child POS device provides authentication data, and the authentication data is verified through a machine learning model, thereby realizing NFC-based device authentication and ensuring the credibility of the child POS device; by matching and verifying physical signal characteristics (such as intensity, frequency) and behavioral pattern characteristics (such as response delay, power consumption), it can effectively prevent counterfeit devices from accessing the system, thereby improving the security and credibility of the system.

[0115] In some optional implementations of this embodiment, the verification module 304 is further configured to:

[0116] Collect physical signal characteristics and behavior pattern characteristics data, the physical signal characteristics include the strength, frequency, and polarization characteristics of the device's NFC signal, and the behavior pattern characteristics include the response delay, voltage change, and power consumption curve of the device's hardware interface;

[0117] Extract key features of physical signal characteristics and behavior pattern characteristic data through feature extraction methods;

[0118] The classification model is trained using supervised learning methods to classify and annotate the collected physical signal characteristics and behavior pattern characteristics data according to normal and abnormal labels;

[0119] Use the labeled physical signal characteristics and behavioral pattern characteristics data for model training.

[0120] The NFC-based financial POS device authentication system provided in this application can identify and distinguish the behavior patterns of normal and abnormal devices through the training of machine learning models. This makes the authentication system more adaptable and accurate, able to identify potential security threats in various environments, and improves recognition accuracy and anti-attack capabilities.

[0121] In some optional implementations of this embodiment, the verification module 304 is further configured to:

[0122] Setting corresponding weighting coefficients for the physical property matching, behavior pattern matching, and time window matching;

[0123] Calculating a weighted score according to the weighting coefficient and the degree of matching of the physical characteristic matching, the behavior pattern matching, and the time window matching;

[0124] Adding up the weighted scores to obtain a comprehensive score;

[0125] The comprehensive score is compared with a preset trust threshold. When the comprehensive score is greater than or equal to the trust threshold, the sub-POS device is determined to be a trustworthy device. When the comprehensive score is less than the trust threshold, the sub-POS device is determined to be an untrustworthy device.

[0126] The NFC-based financial POS device identity authentication system provided by the present application can more accurately evaluate the impact of each characteristic on the authentication result by assigning different weights to each verification step, thereby improving the flexibility and reliability of the authentication process. The introduction of dynamic weighting coefficients enables the authentication system to adapt to different environments and conditions, thereby maintaining efficient authentication results in various different scenarios.

[0127] In some optional implementations of this embodiment, the verification module 304 is further configured to:

[0128] The weighting coefficient is dynamically adjusted according to the accuracy, reliability and environmental factors of the historical data of physical property matching, behavior pattern matching and time window matching.

[0129] The NFC-based financial POS device identity authentication system provided by the present application improves the flexibility of the authentication process and enhances the adaptability and robustness of the system by adjusting the weighting coefficient in real time. This means that in different environments (such as signal interference, equipment failure, etc.), the authentication process can still ensure high efficiency and accuracy, thereby improving the reliability of the system in complex environments.

[0130] In some optional implementations of this embodiment, the determination module 305 is further configured to:

[0131] When the child POS device is determined to be an untrusted device, an alarm mechanism is triggered, an alarm notification is generated and sent to the parent POS device.

[0132] The NFC-based financial POS device identity authentication system provided by the present application adds an alarm mechanism. When the system determines that the sub-POS device is untrustworthy, it can immediately issue an alarm, thereby improving the system's protection capabilities. This design ensures that the system can respond to counterfeiting or attack behaviors in a timely manner, enhances security, and provides real-time monitoring and exception handling capabilities.

[0133] In some optional implementations of this embodiment, the generating module 301 is further used to:

[0134] Obtaining a random challenge data, wherein the challenge data includes a device identifier, a timestamp, and a physical signal characteristic;

[0135] The parent POS device encrypts the random challenge data using its private key;

[0136] Send the encrypted challenge data to the sub-POS device.

[0137] The NFC-based financial POS device identity authentication system provided by this application ensures the security of the challenge signal through encryption technology, effectively preventing man-in-the-middle attacks and data leakage. After the challenge signal is encrypted, it not only improves the security of identity authentication, but also prevents external illegal devices from forging challenge signals, thereby enhancing the anti-attack capability of the authentication system.

[0138] In some optional implementations of this embodiment, the sending module 302 is further configured to:

[0139] Performing validity checks on the authentication data, including verifying whether the signal strength and frequency are within a preset range and whether the behavior pattern characteristics conform to normal usage patterns;

[0140] Store authentication data in a temporary buffer.

[0141] The NFC-based financial POS device identity authentication system provided by the present application can detect abnormal and erroneous data during data transmission by checking the validity of the authentication data, thereby ensuring the integrity and accuracy of the data. By temporarily storing the authentication data, more time can be provided for verification and analysis, ensuring that the authentication process is more robust and avoiding the impact of transient errors on the authentication results.

[0142] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0143] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 4 with components 41-43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0144] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0145] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the computer device 4. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions of the financial POS device identity authentication method based on NFC. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0146] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run the computer-readable instructions stored in the memory 41 or process data, such as running the computer-readable instructions of the NFC-based financial POS device identity authentication method.

[0147] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0148] In the computer device provided by this application, the parent POS device generates a challenge signal, the child POS device provides authentication data, and verifies the authentication data through a machine learning model, thereby realizing NFC-based device identity authentication and ensuring the credibility of the child POS device; by matching and verifying physical signal characteristics (such as intensity, frequency) and behavioral pattern characteristics (such as response delay, power consumption), it is possible to effectively prevent counterfeit devices from accessing the system, thereby improving the security and credibility of the system.

[0149] The present application also provides another implementation, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the above-mentioned NFC-based financial POS device identity authentication method.

[0150] The computer-readable storage medium provided by the present application, the parent POS device generates a challenge signal, the child POS device provides authentication data, and verifies the authentication data through a machine learning model, thereby realizing NFC-based device identity authentication and ensuring the credibility of the child POS device; by matching and verifying physical signal characteristics (such as intensity, frequency) and behavioral pattern characteristics (such as response delay, power consumption), it is possible to effectively prevent counterfeit devices from accessing the system, thereby improving the security and credibility of the system.

[0151] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods 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 storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0152] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A financial POS device identity authentication method based on NFC, characterized in that: The steps include: Generate a challenge signal through the parent POS device; Send a challenge signal to the sub-POS device, requiring the sub-POS device to provide authentication data; receiving authentication data of a sub-POS device, the authentication data comprising physical signal characteristics and behavioral pattern characteristics; Verifying the authentication data using a pre-trained machine learning model, including physical property matching, behavioral pattern matching, and time window matching; Determine whether the sub-POS device is a trusted device.

2. The NFC-based financial POS device identity authentication method according to claim 1, characterized in that: In the step of verifying the authentication data by using a pre-trained machine learning model, including physical property matching, behavior pattern matching, and time window matching, the training step of the machine learning model includes: Collect physical signal characteristics and behavior pattern characteristics data, the physical signal characteristics include the strength, frequency, and polarization characteristics of the device's NFC signal, and the behavior pattern characteristics include the response delay, voltage change, and power consumption curve of the device's hardware interface; Extract key features of physical signal characteristics and behavior pattern characteristic data through feature extraction methods; The classification model is trained using supervised learning methods to classify and annotate the collected physical signal characteristics and behavior pattern characteristics data according to normal and abnormal labels; Use the labeled physical signal characteristics and behavioral pattern characteristics data for model training.

3. The NFC-based financial POS device identity authentication method according to claim 2, characterized in that: The verification of the authentication data using a pre-trained machine learning model, including the steps of physical property matching, behavior pattern matching, and time window matching, comprises: Setting corresponding weighting coefficients for the physical property matching, behavior pattern matching, and time window matching; Calculating a weighted score according to the weighting coefficient and the degree of matching of the physical characteristic matching, the behavior pattern matching, and the time window matching; Adding up the weighted scores to obtain a comprehensive score; The comprehensive score is compared with a preset trust threshold. When the comprehensive score is greater than or equal to the trust threshold, the sub-POS device is determined to be a trustworthy device. When the comprehensive score is less than the trust threshold, the sub-POS device is determined to be an untrustworthy device.

4. The NFC-based financial POS device identity authentication method according to claim 3, characterized in that: In the step of setting corresponding weighting coefficients for the physical property matching, behavior pattern matching and time window matching, the weighting coefficients are dynamically adjusted according to the accuracy, reliability and environmental factors of the historical data of the physical property matching, behavior pattern matching and time window matching.

5. The NFC-based financial POS device identity authentication method according to claim 3, characterized in that: In the step of comparing the comprehensive score with a preset trust threshold, determining that the sub-POS device is a trustworthy device when the comprehensive score is greater than or equal to the trust threshold, and determining that the sub-POS device is an untrustworthy device when the comprehensive score is less than the trust threshold, when the sub-POS device is determined to be an untrustworthy device, an alarm mechanism is triggered, an alarm notification is generated and sent to the parent POS device.

6. The NFC-based financial POS device identity authentication method according to claim 1, characterized in that: The step of generating a challenge signal by the mother POS device comprises: Obtaining a random challenge data, wherein the challenge data includes a device identifier, a timestamp, and a physical signal characteristic; The parent POS device encrypts the random challenge data using its private key; Send the encrypted challenge data to the sub-POS device.

7. The NFC-based financial POS device identity authentication method according to claim 6, characterized in that: In the step of receiving authentication data of the sub-POS device, wherein the authentication data includes physical signal characteristics and behavior pattern characteristics, after the step of receiving authentication data of the sub-POS device, the step further includes: Performing validity checks on the authentication data, including verifying whether the signal strength and frequency are within a preset range and whether the behavior pattern characteristics conform to normal usage patterns; Store authentication data in a temporary buffer.

8. A financial POS device identity authentication system based on NFC, characterized in that: include: A generation module, used for generating a challenge signal through a parent POS device; A sending module, used for sending a challenge signal to the sub-POS device, requiring the sub-POS device to provide authentication data; A receiving module, used to receive authentication data of a sub-POS device, wherein the authentication data includes physical signal characteristics and behavior pattern characteristics; A verification module, for verifying the authentication data using a pre-trained machine learning model, including physical property matching, behavior pattern matching, and time window matching; The judgment module is used to judge whether the sub-POS device is a trusted device.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the NFC-based financial POS device identity authentication method as described in any one of claims 1 to 7 when executing the computer-readable instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps of the NFC-based financial POS device identity authentication method according to any one of claims 1 to 7 are implemented.

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