NFC-based financial POS device identity authentication method and related device

Abstract

The embodiment of the application belongs to the field of financial POS device identity authentication, and relates to a financial POS device identity authentication method based on NFC. The method comprises the following steps: generating a challenge signal by a parent POS device; sending the challenge signal to a child POS device and requiring the child POS device to provide authentication data; receiving the authentication data of the child POS device, wherein the authentication data comprises physical signal characteristics and behavior pattern characteristics; verifying the authentication data by using a pre-trained machine learning model, including physical characteristic matching, behavior pattern matching and time window matching; and judging whether the child POS device is a trusted device. The application also provides a financial POS device identity authentication system based on NFC, a computer device and a storage medium. The application aims to improve the reliability and security 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 particularly relates to a financial POS device identity authentication method based on NFC and related equipment. BACKGROUND

[0002] With the rapid development of information technology, the devices of the financial payment industry have gradually become more intelligent and networked, and the application of financial POS devices in payment, authentication and key management is increasingly widespread. However, with the increase in financial transactions, the security of POS devices has become increasingly prominent. In particular, in the aspects of key loading, identity verification and device-to-device communication, how to effectively prevent security threats such as forgery, tampering and man-in-the-middle attacks has become a technical problem that needs to be solved by the industry.

[0003] Currently, the identity authentication of financial POS devices generally relies on the pre-installed key or digital certificate method. Some public key, private key pairs or digital certificates are pre-installed in the device at the time of production and shipment, and when the devices communicate with each other, encryption and decryption or digital signature verification are performed through these keys. Common authentication mechanisms include verifying the public key or certificate of the device, and using a symmetric encryption key for data encryption transmission. In the mass production process, the key or certificate of each device needs to be configured in advance, and the security of the key needs to be ensured during the life cycle of the device. Once the key is leaked, the security of the entire system will be threatened. SUMMARY

[0004] The purpose of the embodiments of the present application is to provide a financial POS device identity authentication method based on NFC and related equipment 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 embodiments of the present application provide a financial POS device identity authentication method based on NFC, which adopts the technical scheme as follows:

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

[0007] generating a challenge signal by a parent POS device;

[0008] sending the challenge signal to a child POS device and requiring the child POS device to provide authentication data;

[0009] receiving the authentication data of the child POS device, wherein the authentication data includes physical signal characteristics and behavior pattern characteristics;

[0010] verifying the authentication data by using a pre-trained machine learning model, including physical characteristic matching, behavior pattern matching and time window matching;

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

[0012] In a possible implementation, the step of verifying the authentication data by using the 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] collecting physical signal property data and behavior pattern property data, the physical signal property data including strength, frequency, and polarization property of the NFC signal of the device, and the behavior pattern property data including response delay, voltage change, and power consumption curve of the hardware interface of the device;

[0014] extracting key features of the physical signal property data and the behavior pattern property data by using a feature extraction method;

[0015] training a classification model by using a supervised learning method, and classifying and labeling the collected physical signal property data and behavior pattern property data according to normal and abnormal labels;

[0016] training the model by using the labeled physical signal property data and behavior pattern property data.

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

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

[0019] calculating a weighted score according to the weighting coefficients and matching degrees of the physical property matching, the behavior pattern matching, and the time window matching;

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

[0021] comparing the comprehensive score with a preset trusted threshold, determining that the sub-POS device is a trusted device when the comprehensive score is greater than or equal to the trusted threshold, and determining that the sub-POS device is an untrusted device when the comprehensive score is less than the trusted threshold.

[0022] In a possible implementation, the step of setting corresponding weighting coefficients for the physical property matching, the behavior pattern matching, and the time window matching includes dynamically adjusting the weighting coefficients according to accuracy, reliability, and environmental factors of historical data of the physical property matching, the behavior pattern matching, and the time window matching.

[0023] In a possible implementation, the step of comparing the comprehensive score with the preset trusted threshold, determining the sub-POS device as a trusted device when the comprehensive score is greater than or equal to the trusted threshold, and determining the sub-POS device as an untrusted device when the comprehensive score is less than the trusted threshold, when the sub-POS device is determined as an untrusted device, triggering an alarm mechanism, generating an alarm notification and sending the alarm notification to the master POS device.

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

[0025] obtaining a random challenge data, the challenge data including a device identifier, a timestamp, and a physical signal characteristic;

[0026] encrypting, by the master POS device, the random challenge data using a private key of the master POS device;

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

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

[0029] performing validity check on the authentication data, including verifying whether the signal strength and the frequency are within a preset range and whether the behavior pattern characteristic conforms to a normal use mode;

[0030] storing the authentication data in a temporary buffer.

[0031] To solve the above technical problems, the embodiments of the present application further provide a financial POS device identity authentication system based on NFC, which adopts the technical scheme as follows:

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

[0033] a generating module configured to generate, by a master POS device, a challenge signal;

[0034] a sending module configured to send the challenge signal to a sub-POS device and require the sub-POS device to provide authentication data;

[0035] a receiving module configured to receive authentication data of the sub-POS device, the authentication data including a physical signal characteristic and a behavior pattern characteristic;

[0036] a verifying module configured to verify the authentication data by using a pre-trained machine learning model, including physical characteristic matching, behavior pattern matching, and time window matching;

[0037] A judging module is configured to judge whether the sub-POS device is a trusted device.

[0038] To solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the technical scheme as follows:

[0039] A computer device comprises a memory and a processor, the memory stores computer readable instructions, and the processor executes the computer readable instructions to realize the steps of the NFC-based financial POS device identity authentication method.

[0040] To solve the above technical problems, the embodiment of the present application further provides a computer readable storage medium, which adopts the technical scheme as follows:

[0041] A computer readable storage medium stores computer readable instructions, and the computer readable instructions are executed by a processor to realize the steps of the NFC-based financial POS device identity authentication method.

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

[0043] The NFC-based financial POS device identity authentication method disclosed in the present application generates a challenge signal by a parent POS device; then sends the challenge signal to a sub-POS device, requiring the sub-POS device to provide authentication data; then receives the authentication data of the sub-POS device, the authentication data comprising physical signal characteristics and behavior pattern characteristics; verifies the authentication data by using a pre-trained machine learning model, including physical characteristic matching, behavior pattern matching and time window matching; finally, judges whether the sub-POS device is a trusted device. The parent POS device generates a challenge signal, the sub-POS device provides authentication data, and the authentication data is verified by a machine learning model, which realizes NFC-based device identity authentication and ensures the trustworthiness of the sub-POS device; by matching and verifying the physical signal characteristics (such as intensity and frequency) and the behavior pattern characteristics (such as response delay and power consumption), the system can effectively prevent fake devices from accessing the system, and the security and trustworthiness of the system are improved. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the schemes in the present application, the drawings needed in the description of the embodiments of the present application will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

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

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

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

[0048] Figure 4 is a structural schematic diagram of one embodiment of the 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 commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description herein is for describing particular embodiments only and is not intended to be limiting of the application; the description and the drawings are to be regarded as illustrative in nature and are not intended to limit the application. The terminology used in the description of the application herein, as well as in the claims that follow, unless otherwise expressly so defined by the context of the specification, shall be taken to refer to the appropriate generic

[0050] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. The appearances of the phrase "in an embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily all directed to the same

[0051] For those skilled in the art to better understand the technical solutions of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the drawings as follows.

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

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

[0054] The 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 can be a server providing various services, such as a background server providing support for the pages displayed on the terminal devices 101, 102, 103.

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

[0057] It should be understood that Figure 1 The number of terminal devices, networks and servers in

[0058] With reference to Figure 2 , a flowchart of one embodiment of the NFC-based financial POS device identity authentication method according to the present application is shown. The NFC-based financial POS device identity authentication method includes the following steps:

[0059] In step S201, a challenge signal is generated by a parent POS device.

[0060] In the present embodiment, the NFC-based financial POS device identity authentication method is run on an electronic device (such as a mobile phone, a tablet computer, etc.) which can be connected to a parent POS device through NFC. Figure 1The server (e.g., the server shown) can send or receive data through wired or wireless connections. It should be noted that the wireless connection can include, but is not limited to, 3G / 4G / 5G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other now known or future developed wireless connection.

[0061] In this embodiment, the parent POS device first generates a challenge signal, 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 the signal is to verify whether the child POS device has the authentication qualification. The challenge signal can be transmitted through radio waves (such as NFC technology), and can contain device identification, timestamp, etc. to ensure the uniqueness of each authentication process.

[0062] Step S202, send a challenge signal to the child POS device, and ask the child POS device to provide authentication data.

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

[0064] Step S203, receiving authentication data of the child POS device, the authentication data including physical signal characteristics and behavior pattern characteristics.

[0065] In this embodiment, the parent POS device receives the authentication data sent by the child POS device, which is composed of physical signal characteristics and behavior pattern characteristics. Physical signal characteristics can help identify the communication characteristics and signal strength of the device, while behavior pattern characteristics can reflect the normal operation mode of the device, helping to judge whether the behavior of the device is normal.

[0066] Step S204, verifying the authentication data using a pre-trained machine learning model, including physical characteristic 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 (such as a classification model). Physical characteristic matching ensures that the communication signal of the device is within a reasonable range, behavior pattern matching verifies whether the device operation conforms to the normal usage pattern, and time window matching ensures that the operation time is consistent with the normal range.

[0068] Step S205, judging whether the sub-POS device is a trusted device.

[0069] In this embodiment, according to the verification result of the machine learning model, the parent POS device judges whether the sub-POS device is a trusted device. If the physical characteristics, behavior patterns and time windows all meet the expectations and pass the model verification, the sub-POS device is determined to be a trusted device, otherwise it is an untrusted device.

[0070] The parent POS device of the present application generates a challenge signal, the sub-POS device provides authentication data, and the authentication data is verified by a machine learning model, realizing device identity authentication based on NFC, and ensuring the trustworthiness of the sub-POS device; by matching and verifying the physical signal characteristics (such as strength, frequency) and behavior pattern characteristics (such as response delay, power consumption), it can effectively prevent fake devices from accessing the system, and improve the security and trustworthiness of the system.

[0071] In some optional implementations of the present embodiment, the above steps of verifying the authentication data by using a pre-trained machine learning model, including physical characteristic matching, behavior pattern matching and time window matching, the training steps of the machine learning model include:

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

[0073] Extracting key features of the physical signal characteristic and behavior pattern characteristic data by a feature extraction method;

[0074] Training a classification model by using a supervised learning method, and classifying and labeling the collected physical signal characteristic and behavior pattern characteristic data according to normal and abnormal labels;

[0075] Using the labeled physical signal characteristic and behavior pattern characteristic data for model training.

[0076] In this embodiment, by obtaining the 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 the real device, these data will become the core component of the training data set, which is used to train the model to identify the normal and abnormal behavior of the device. The feature extraction method is used to extract the key features from the original data, which helps the machine learning model to identify the normal behavior pattern of the device. For example, the frequency stability, signal strength fluctuation and other characteristics 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 tree, support vector machine, neural network, etc.) are used to train the classification model. After data collection, the normal and abnormal behavior of the device is labeled and input into the model for training. Through learning these labeled data, the model can identify the normal or abnormal state of the device under different circumstances. The labeled data (normal and abnormal labels) is used for model training, and after training, the model can predict the credibility and authentication result of the device based on the input authentication data.

[0077] Through the training of the machine learning model, the application can identify and distinguish the behavior patterns of normal and abnormal devices. This makes the identity authentication system more adaptable and accurate, and can identify potential security threats in various environments, improving the recognition accuracy and attack resistance.

[0078] In some optional implementations of the embodiment, the steps of verifying the authentication data using the pre-trained machine learning model, including physical characteristic matching, behavior pattern matching and time window matching, include:

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

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

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

[0082] Comparing the comprehensive score with a preset trust threshold, when the comprehensive score is greater than or equal to the trust threshold, determining that the sub-POS device is a trusted device, and when the comprehensive score is less than the trust threshold, determining that the sub-POS device is an untrusted device.

[0083] In this embodiment, during the validation process of the machine learning model, different weighting coefficients can be assigned to each validation characteristic (such as physical characteristics, behavior pattern characteristics, and time window matching) to more flexibly evaluate the influence of different characteristics on the final validation result. The weighting coefficients of different characteristics can be dynamically adjusted according to actual conditions, accuracy of historical data, environmental factors, etc. After each validation step (physical characteristics, behavior patterns, time window) calculates the matching degree, 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 validation step are summed to obtain a final comprehensive score. This comprehensive score can be used to judge the credibility of the device. Compare the comprehensive score with the preset credibility threshold. If the comprehensive score is greater than or equal to the credibility threshold, the parent POS device determines the sub-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 validation step, the present application can more accurately evaluate the influence of each characteristic on the authentication result, 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.

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

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

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

[0088] In some optional implementations of the embodiment, when the comprehensive score is greater than or equal to the trusted threshold, the sub-POS device is determined to be a trusted device; when the comprehensive score is less than the trusted threshold, the sub-POS device is determined to be an untrusted device; and when the sub-POS device is determined to be an untrusted device, an alarm mechanism is triggered, an alarm notification is generated, and the alarm notification is sent to the master POS device.

[0089] In the embodiment, if the authentication result is an untrusted device, the system automatically triggers an alarm mechanism, generates an alarm notification, and timely transmits the alarm notification to the master POS device, so that when a counterfeit or attack behavior occurs, a quick response and subsequent measures (such as disconnecting the connection, marking the device, etc.) can be taken.

[0090] The application adds an alarm mechanism, which can immediately issue an alarm when the system determines that the sub-POS device is untrusted, thereby improving the protection capability of the system. This design ensures that the system can respond to counterfeit or attack behavior in a timely manner, enhances security, and provides real-time monitoring and exception handling capabilities.

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

[0092] obtaining a random challenge data, the challenge data including a device identifier, a timestamp, and a physical signal characteristic;

[0093] the master POS device encrypts the random challenge data using a private key thereof;

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

[0095] In the embodiment, when the master POS device generates a challenge signal, the challenge signal includes a device identifier (ID), a timestamp (to prevent a replay attack), and a physical signal characteristic. 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 master POS device and a legitimate sub-POS device can decrypt it, ensuring the confidentiality and integrity of the data transmission process. The master POS device sends the encrypted challenge data to the sub-POS device, and the sub-POS device needs to correctly decrypt and generate a corresponding response to complete the authentication process.

[0096] The 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, not only the security of the identity authentication is improved, but also the challenge signal can be prevented from being counterfeited by external illegal devices, thereby enhancing the anti-attack capability of the authentication system.

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

[0098] performing validity check 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 a normal use pattern;

[0099] storing the authentication data in a temporary buffer.

[0100] In the embodiment, the received authentication data is subjected to validity check to ensure that the signal strength, frequency, etc. conform to the preset communication standards and the behavior pattern characteristics conform to the expectation. The authentication data is temporarily stored in the buffer for further analysis for subsequent verification and processing.

[0101] The application can detect abnormal and erroneous data during data transmission by performing validity check on the authentication data, thereby ensuring the integrity and accuracy of the data. By temporarily storing the authentication data, more time is provided for verification and analysis, ensuring that the authentication process is more robust and avoiding the influence of instantaneous errors on the authentication result.

[0102] The embodiment of the application can acquire and process related data based on artificial intelligence technology. Artificial intelligence (AI) is the use of digital computers or computer-controlled machines to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use the knowledge to obtain the best results.

[0103] The basic technologies of artificial intelligence generally include technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing, operation / interaction systems, mechatronics, etc. The software technologies of artificial intelligence mainly include computer vision technology, robot technology, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning, etc.

[0104] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by computer readable instructions instructing related hardware, 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 above-mentioned embodiments of each method. The storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0105] It should be understood that, although each step in the flowchart of the accompanying drawings is shown in sequence according to the direction of the arrow, these steps are not necessarily executed in sequence according to the direction of the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and they can be executed in other sequences. Moreover, at least part of the steps in the flowchart of the accompanying drawings can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence is not necessarily sequential, but can be alternately or alternately executed with at least part of other steps or sub-steps or stages of other steps.

[0106] Further referring to Figure 3 , as an implementation of the method shown in the above Figure 2 , the present application provides an NFC-based financial POS device identity authentication system. An embodiment of the NFC-based financial POS device identity authentication system corresponds to the method embodiment shown in Figure 2 , and the system can be specifically applied to various electronic devices.

[0107] As shown in Figure 3 , the NFC-based financial POS device identity authentication system 300 includes a generation module 301, a sending module 302, a receiving module 303, a verification module 304, and a judgment module 305.

[0108] Among them:

[0109] The generation module 301 is configured to generate a challenge signal by a parent POS device.

[0110] The sending module 302 is configured to send the challenge signal to a child POS device, and require the child POS device to provide authentication data.

[0111] The receiving module 303 is configured to receive authentication data of the child POS device, and the authentication data includes physical signal characteristics and behavior pattern characteristics.

[0112] The verification module 304 is configured to verify the authentication data by using a pre-trained machine learning model, including physical characteristic matching, behavior pattern matching, and time window matching.

[0113] The judgment module 305 is configured to judge whether the child POS device is a trusted device.

[0114] The NFC-based financial POS device identity authentication system provided in the application, 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, realizing NFC-based device identity authentication and ensuring the credibility of the child POS device; by matching and verifying the physical signal characteristics (such as intensity and frequency) and behavior pattern characteristics (such as response delay and power consumption), the system can effectively prevent fake devices from accessing the system, and the security and credibility of the system are improved.

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

[0116] Collect physical signal characteristic and behavior pattern characteristic data, wherein the physical signal characteristic includes the intensity, frequency and polarization characteristic of the NFC signal of the device, and the behavior pattern characteristic includes the response delay, voltage change and power consumption curve of the hardware interface of the device;

[0117] Extract key features of the physical signal characteristic and behavior pattern characteristic data through a feature extraction method;

[0118] Train a classification model by using a supervised learning method, and classify and label the collected physical signal characteristic and behavior pattern characteristic data according to normal and abnormal labels;

[0119] Train the model using the labeled physical signal characteristic and behavior pattern characteristic data.

[0120] The NFC-based financial POS device identity authentication system provided in the application can identify and distinguish the behavior patterns of normal and abnormal devices through the training of the machine learning model. This makes the identity authentication system have stronger adaptability and accuracy, can identify potential security threats in various environments, and improves the identification accuracy and attack resistance.

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

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

[0123] Calculate a weighted score according to the weighting coefficients and the matching degrees of the physical characteristic matching, behavior pattern matching and time window matching;

[0124] Cumulatively obtain a comprehensive score from the weighted score;

[0125] Compare the comprehensive score with a preset credibility threshold, determine that the child POS device is a credible device when the comprehensive score is greater than or equal to the credibility threshold, and determine that the child POS device is an incredible device when the comprehensive score is less than the credibility threshold.

[0126] The NFC-based financial POS device identity authentication system provided by the application can more accurately evaluate the influence of each feature 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 implementation manners of the embodiment, the verification module 304 is further configured to:

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

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

[0130] In some optional implementation manners of the embodiment, the judgment module 305 is further configured to:

[0131] When it is determined that the sub-POS device is 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 application can immediately issue an alarm when the system determines that the sub-POS device is untrusted by adding an alarm mechanism, thereby improving the protection capability of the system. This design ensures that the system can respond to counterfeit or attack behavior in a timely manner, thereby enhancing security and providing real-time monitoring and exception handling capabilities.

[0133] In some optional implementation manners of the embodiment, the generation module 301 is further configured to:

[0134] Obtain 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] The encrypted challenge data is sent to the sub-POS device.

[0137] The NFC-based financial POS device identity authentication system provided in the application ensures the security of the challenge signal through encryption technology, effectively prevents man-in-the-middle attacks and data leakage. After the challenge signal is encrypted, not only the security of the identity authentication is improved, but also the challenge signal can be prevented from being forged by external illegal devices, and the anti-attack capability of the authentication system is enhanced.

[0138] In some optional implementation manners of the embodiment, the sending module 302 is further used for:

[0139] The validity of the authentication data is checked, including verifying whether the signal strength and frequency are within the preset range and whether the behavior pattern characteristics conform to the normal use mode.

[0140] The authentication data is stored in a temporary buffer.

[0141] The NFC-based financial POS device identity authentication system provided in the application can detect abnormal and error data in the data transmission process 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 influence of instantaneous errors on the authentication result.

[0142] To solve the above technical problems, the embodiment of the application further provides a computer device. For details, please refer to Figure 4 , Figure 4 The basic structure block diagram of the computer device of the embodiment is shown in the figure.

[0143] The computer device 4 includes a memory 41, a processor 42 and a network interface 43 which are connected to each other through a system bus. It should be pointed out that only the computer device 4 with components 41-43 is shown in the figure, but it should be understood that all the shown components are not required to be implemented, and more or fewer components can be alternatively implemented. Among them, those skilled in the art can understand that the computer device here is a device capable of automatically performing numerical calculation 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 (ASICs), field programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0144] The computer device can be a desktop computer, a notebook computer, a palm computer and a cloud server, etc. The computer device can interact with the user through a keyboard, a mouse, a remote controller, a touchpad or a voice control device, etc.

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

[0146] The processor 42 can 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 computer readable instructions or process data stored in the memory 41, such as running computer readable instructions of the NFC-based financial POS device identity authentication method.

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

[0148] The computer device provided in the present application is used to generate a challenge signal by a parent POS device, provide authentication data by a child POS device, and verify the authentication data through a machine learning model, so as to realize device identity authentication based on NFC and ensure the credibility of the child POS device. By matching and verifying the physical signal characteristics (such as intensity and frequency) and the behavior pattern characteristics (such as response delay and power consumption), the system security and credibility can be improved, and the counterfeit device can be effectively prevented from accessing the system.

[0149] The application also provides another implementation, that is, providing a computer readable storage medium, the computer readable storage medium stores computer readable instructions, the computer readable instructions can be executed by at least one processor, so that the at least one processor executes the steps of the NFC-based financial POS device identity authentication method as described above.

[0150] The computer readable storage medium provided by the application, the mother POS device generates a challenge signal, the child POS device provides authentication data, and the authentication data is verified by the machine learning model, which realizes the NFC-based device identity authentication and guarantees the credibility of the child POS device; by matching and verifying the physical signal characteristics (such as intensity, frequency) and behavior pattern characteristics (such as response delay, power consumption), the system can be effectively prevented from being accessed by a fake device, and the security and credibility of the system are improved.

[0151] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment method can be realized by means of software and necessary general hardware platform, of course, it can also be realized by hardware, but in many cases, the former is a better implementation. Based on such understanding, the technical solutions of the application 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 plurality of instructions for making a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) execute the method described in each embodiment of the application.

[0152] Obviously, the above-described embodiments are only some of the embodiments of the application, not all the embodiments, and the preferred embodiments of the application are given in the drawings, but do not limit the patent scope of the application. The application can be implemented in many different forms, and conversely, the purpose of providing these embodiments is to make the disclosure of the application more thorough and comprehensive. Although the application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing specific embodiments, or make equivalent replacements to some technical features. Any equivalent structure made by using the content of the specification and drawings, directly or indirectly applied to other related technical fields, is also within the scope of the patent protection of the application.

Claims

1. A method for identity authentication of financial POS devices based on NFC, characterized in that, Includes the following steps: A challenge signal is generated via the parent POS device; Send a challenge signal to the sub-POS device, requesting the sub-POS device to provide authentication data; The authentication data received from the sub-POS device includes physical signal characteristics and behavioral pattern characteristics. The physical signal characteristics include the strength, frequency, and polarization characteristics of the device's NFC signal. The behavioral pattern characteristics include the response delay, voltage variation, and power consumption curve of the device's hardware interface. The authentication data is verified using a pre-trained machine learning model, including physical characteristic matching, behavioral pattern matching, and time window matching. Determine whether the sub-POS device is a trusted device; The step of generating a challenge signal through the master POS device includes: Obtain random challenge data, which includes device identifier, timestamp, and physical signal characteristics; The parent POS device uses its private key to encrypt the random challenge data; The encrypted challenge data is sent to the sub-POS device.

2. The NFC-based financial POS device authentication method according to claim 1, characterized in that, The step of verifying the authentication data using a pre-trained machine learning model, including physical characteristic matching, behavioral pattern matching, and time window matching, includes the following training steps for the machine learning model: Collect physical signal characteristics and behavioral pattern characteristics data. The physical signal characteristics include the strength, frequency, and polarization characteristics of the device's NFC signal. The behavioral pattern characteristics include the response delay, voltage change, and power consumption curve of the device's hardware interface. Key features of physical signal characteristics and behavioral pattern characteristics data are extracted using feature extraction methods; A classification model is trained using supervised learning methods, and the collected physical signal characteristics and behavioral pattern characteristics data are classified and labeled as normal and abnormal. Model training is performed using labeled physical signal characteristics and behavioral pattern characteristics data.

3. The NFC-based financial POS device authentication method according to claim 2, characterized in that, The verification of the authentication data using a pre-trained machine learning model includes steps such as physical characteristic matching, behavioral pattern matching, and time window matching, including: Set corresponding weighting coefficients for the physical characteristic matching, behavior pattern matching, and time window matching; A weighted score is calculated based on the weighting coefficients and the degree of matching of the physical characteristics, behavioral patterns, and time windows. The weighted scores are summed to obtain the overall 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 authentication method according to claim 3, characterized in that, In the step of setting corresponding weighting coefficients for physical characteristic matching, behavioral pattern matching, and time window matching, the weighting coefficients are dynamically adjusted based on the accuracy, reliability, and environmental factors of historical data for physical characteristic matching, behavioral pattern matching, and time window matching.

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

6. The NFC-based financial POS device authentication method according to claim 1, characterized in that, In the step of receiving authentication data from the sub-POS device, wherein the authentication data includes physical signal characteristics and behavioral pattern characteristics, after the step of receiving authentication data from the sub-POS device, the method further includes: The authentication data is validated, including verifying whether the signal strength and frequency are within a preset range and whether the behavior pattern characteristics conform to the normal usage pattern. Authentication data is stored in a temporary buffer.

7. An NFC-based identity authentication system for financial POS devices, characterized in that, include: The generation module is used to generate challenge signals from the parent POS device; The sending module is used to send a challenge signal to the sub-POS device, requesting the sub-POS device to provide authentication data; The receiving module is used to receive authentication data from the sub-POS device. The authentication data includes physical signal characteristics and behavioral pattern characteristics. The physical signal characteristics include the strength, frequency, and polarization characteristics of the device's NFC signal. The behavioral pattern characteristics include the response delay, voltage change, and power consumption curve of the device's hardware interface. The verification module is used to verify the authentication data using a pre-trained machine learning model, including physical characteristic matching, behavioral pattern matching, and time window matching. The judgment module is used to determine whether the sub-POS device is a trusted device; The system is also used for: Obtain random challenge data, which includes device identifier, timestamp, and physical signal characteristics; The parent POS device uses its private key to encrypt the random challenge data; The encrypted challenge data is sent to the sub-POS device.

8. A computer device, characterized in that, The device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the NFC-based financial POS device authentication method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the NFC-based financial POS device authentication method as described in any one of claims 1 to 6.

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