CN114520975B - Lightweight passive identity authentication system and method based on wireless network - Google Patents
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Abstract
The invention belongs to the technical field of identity authentication, and provides a lightweight passive identity authentication system and method based on a wireless network. The invention finally provides theoretical basis and practical experience for the application of the machine learning technology in the fields of passive perception identity authentication and identification by combining the technologies of signal processing, machine learning and the like, and the designed system provides new design ideas and application prototypes for the application of passive identification and tracking, indoor intrusion detection, company sign-in and the like. The invention designs and develops a lightweight passive identity authentication system based on machine learning. And preprocessing the WiFi signal to extract the CSI characteristics, and adopting an algorithm based on an SNN+GhostNet network to realize high-precision real-time authentication of the identity. The developed system is hopeful to fill the blank of passive identity authentication application, and creates an application example for passive identification and positioning based on machine learning. The system can be widely applied to the fields of identification and tracking, indoor intrusion detection, company check-in and the like.
Description
Technical Field
The invention belongs to The technical field of identity authentication, and relates to a lightweight passive identity authentication system and method based on a wireless network.
Background
Identity authentication is a key technology in pervasive computing and man-machine interaction. The existing identity authentication technology is mainly divided into two methods, namely active authentication and passive authentication. The active identity authentication method requires additional special equipment such as cameras and multifunctional sensors, and the equipment cost is high. In addition, such methods often require accurate recognition by the user in proximity to the sensing device, such as iris recognition, gesture recognition, etc., not only present privacy concerns, but also do not cover large areas. Passive identity authentication methods, particularly machine learning based methods, typically require a large number of data samples to ensure high accuracy authentication. Furthermore, this approach tends to result in reduced accuracy when crossing environments. In order to solve the problems of the method, the invention takes Jiang Pushi WiFi signals as a research object, fully extracts rich characteristics from limited WiFi data by using a lightweight machine learning method, and realizes passive identity authentication in a rapid mode.
WiFi signals have ubiquitous, fine-grained characteristics. After the WiFi signal sent by the transmitting end of the COTS equipment is transmitted through paths in various modes such as signal scattering, signal direct incidence and signal reflection, signals with various paths overlapped together, namely multipath overlapped signals, are formed at the receiving end of the COTS equipment. The signal, affected by the actual physical space environment during propagation, contains information that is representative of the characteristics of the environment. The environment referred to herein is the physical space in which signals propagate, including both human factors (human position, characteristics, posture, motion, etc.) and other external factors. In this way, the corresponding environment information is modulated in the WiFi signal propagation process, the environment information can be obtained by demodulating the information at the receiving end, and the behavior characteristics of people in the perceived environment are also included, so that the scientificity of realizing high-precision person identification by utilizing the WiFi signal is ensured in principle.
Disclosure of Invention
The invention aims to solve the technical problem of how to use the WiFiCSI (Channel State Information ) of a physical layer to realize authentication of human identity in a lightweight machine learning mode. The invention finally provides theoretical basis and practical experience for the application of the machine learning technology in the fields of passive perception identity authentication and identification by combining the technologies of signal processing, machine learning and the like, and the designed system provides new design ideas and application prototypes for the application of passive identification and tracking, indoor intrusion detection, company sign-in and the like. The invention aims to provide a lightweight identity authentication method based on machine learning.
The technical scheme of the invention is as follows:
a lightweight passive identity authentication system based on a wireless network comprises a COTS equipment transmitting end, a COTS equipment receiving end, a cloud server and a control end;
the COTS equipment transmitting end and the COTS equipment receiving end are both provided with wireless network cards, and the wireless network cards are arranged in a room at intervals; the COTS equipment transmitting end is used for transmitting the WiFi signal carrying the CSI information, and the COTS equipment receiving end is used for receiving the WiFi signal in the environment and uploading the WiFi signal to the cloud server;
the cloud server comprises a data collection module, a preprocessing module and an authentication module; the data collection module collects CSI data of a user and user actions by using COTS equipment; the preprocessing module uses a low-pass filter to reduce noise of the CSI data collected by the data collection module, screens the antenna and the CSI stream, and generates CSI characteristic data; the authentication module trains, verifies and tests the preprocessed CSI characteristic data by using a machine learning method, and completes identity authentication and action recognition through a trained network model;
the control end is a webpage or mobile phone user end, is communicated with the COTS equipment and the cloud server, and sends basic information of the user to the cloud server to monitor and early warn the identity of the user in real time.
A lightweight passive identity authentication method based on a wireless network comprises the following steps:
step one, a user enters a room, a transmitting end of COTS equipment transmits WiFi signals, the user walks to cause disturbance to the WiFi signals, at the moment, a receiving end of the COTS equipment collects and extracts four pieces of gait CSI data of the user, and the gait CSI data are uploaded to a cloud server; simultaneously, the control end is used for transmitting the basic information of the user to the cloud server;
step two, the cloud server pre-processes the CSI data in the step one, and then models the pre-processed data by using a machine learning algorithm;
the WiFi signal preprocessing comprises the following specific steps:
(1) Denoising data: removing high-frequency noise with the frequency exceeding 80Hz by using a Butterworth filter;
(2) And (3) antenna screening: the limited data samples are increased with the CSI stream in each piece of CSI data as the smallest data unit. The COTS equipment is provided with 3 antennas, the correlation coefficient of the CSI flow between the antennas is calculated to evaluate the correlation of the antennas, 1 antenna with the lowest correlation is removed, and 2 antennas with higher correlation are reserved;
(3) CSI stream screening: respectively calculating the amplitudes and variances of all the CSI streams on each antenna, and screening the CSI streams with low amplitudes and high variances;
the machine learning algorithm comprises the following specific steps:
(1) Snn+ghostnet network structure: in order to extract complete characteristics from limited data and rapidly realize identity recognition, the invention combines SNN (Siamese Neural Network, twin Network) and GhostNet (Ghost Network) together to design a SNN+GhostNet Network structure. The SNN+GhostNet network structure includes four modules, namely GhostNet, global average pool (Global Average Pooling) and Euclidean distance (Euclidean Distance), sigmoid regression, and Cross-entcopy function modules. Wherein the GhostNet module is a core part;
the SNN framework groups individual data in the limited data into data pairs to further increase the data sample size. The GhostNet module contains 2 CNN (Convolutional NeuralNetwork ) units and 1GhostNet unit. The first CNN unit performs data sampling, collates input data, and the second CNN unit adjusts data dimension so as to facilitate subsequent processing, and the GhostNet unit is used for extracting complete deep action characteristics;
(2) Global average pool to euclidean distance: and uniformly receiving and retaining all the extracted characteristic information by adopting a global average pooling layer, and reducing the dimension of the data into characteristic vectors. Then calculating Euclidean distance of the feature vector in the feature space;
(3) Sigmoid regression: calculating a normalized Euclidean distance by using a Sigmoid regression method, and mapping the normalized Euclidean distance to (0, 1);
(4) Cross-entropy function: minimizing normalized Euclidean distances from the same class to approach 0 and maximizing normalized Euclidean distances from different classes to approach 1 using a Cross-entopyloss function;
and step three, when the user enters the room again, the receiving end of the COTS equipment collects gait CSI data again and uploads the gait CSI data to the cloud server. And the cloud server performs character identity matching verification on the CSI data by using the trained model, displays the character identity and sends the character identity to the control end, and when the character information is not found in the database, the cloud server sends alarm information to the control end.
The invention has the beneficial effects that: lightweight passive identity authentication systems based on machine learning are designed and developed. And preprocessing the WiFi signal to extract the CSI characteristics, and adopting an algorithm based on an SNN+GhostNet network to realize high-precision real-time authentication of the identity. The developed system is hopeful to fill the blank of passive identity authentication application, and creates an application example for passive identification and positioning based on machine learning. The system can be widely applied to the fields of identification and tracking, indoor intrusion detection, company check-in and the like.
Drawings
Fig. 1 is a schematic structural diagram of a cloud server.
In the figure: 1COTS equipment receiving end; a 2COTS device transmitting end; 3, users; 4, a data collection module; 5, a pretreatment module; 6, an authentication module; 7SNN framework; 8, denoising the data; 9, screening an antenna; 10CSI stream screening; 11GhostNet network architecture; 12 global average pool and euclidean distance; 13Sigmoid regression; 14 Cross-entopy function.
Detailed Description
Specific embodiments of the present invention will be described in detail below with reference to the following technical schemes (and accompanying drawings).
A lightweight passive identity authentication system based on a wireless network comprises a COTS equipment transmitting end, a COTS equipment receiving end, a control end and a cloud server;
firstly, a user enters a room for the first time, a WiFi signal is sent by a COTS equipment transmitting end, the WiFi signal is obviously disturbed by the user walking, and at the moment, four pieces of gait CSI data of the user are collected and extracted by a COTS equipment receiving end and uploaded to a cloud server; simultaneously, the control end is used for transmitting the basic information of the user to the cloud server;
step two, the cloud server pre-processes the CSI data in the step one, and then models the pre-processed data by using a machine learning algorithm;
the WiFi signal preprocessing comprises the following specific steps:
(1) Denoising data: the linear interpolation algorithm is applied to the passive sensing field, small gaps in the CSI data are filled, the small gaps are uniformly distributed in a time domain, and a Butterworth filter is utilized to remove high-frequency noise with the frequency exceeding 80 Hz;
(2) And (3) antenna screening: the limited data samples are increased with the CSI stream in each piece of CSI data as the smallest data unit. The COTS equipment is provided with 3 antennas, the correlation coefficient of the CSI flow between the antennas is calculated to evaluate the correlation of the antennas, 1 antenna with the lowest correlation is removed, and 2 antennas with higher correlation are reserved;
wherein ,is the correlation coefficient between the ith and jth CSI streams,/>For covariance between ith and jth CSI streams, +.> and />Respectively representing standard deviations of an ith CSI stream and a jth CSI stream;
(3) CSI stream screening: respectively calculating the amplitudes and variances of all the CSI streams on each antenna, and screening the CSI streams with low amplitudes and high variances;
the amplitude of the CSI stream may be replaced by the maximum value of the set of discrete data. And taking the maximum value of each group of CSI stream data as an abscissa and the variance as an ordinate, and making a scatter diagram in a two-dimensional coordinate system. Then defining an optimal center point P as follows:
wherein Maximum i And Variance j Representing the maximum value and variance of the corresponding CSI streams;
the center point P is a point with the minimum value of the maximum values of all CSI stream data on each antenna as the abscissa and the maximum value of the variance as the ordinate. Calculating Euclidean distances between other points and a central point P, and reserving CSI streams corresponding to the first 6 points with the minimum Euclidean distances according to experience;
the machine learning algorithm comprises the following specific steps:
(1) Snn+ghostnet network structure: in order to extract complete characteristics from limited data and rapidly realize identification, the invention combines an SNN framework and a GhostNet together and designs an SNN+GhostNet network structure. The SNN+GhostNet network structure comprises four modules, namely GhostNet, global average pool and Euclidean distance, sigmoid regression and Cross-entopy function modules. Wherein the GhostNet module is a core part;
the SNN framework groups individual data in the limited data into data pairs to further increase the data sample size, and defines the label Y of the data pairs as:
wherein ya and yb For constituent data pairs X a and Xb Is a label of (a).
Then pair X data a and Xb Input to the GhostNet module. It contains two CNN units and 1GhostNet unit. The first CNN unit performs data sampling, collates input data, the second CNN unit adjusts data dimension so as to facilitate subsequent processing, and the GhostNet unit is used for rapidly extracting deep action features;
wherein G(Xa) and G(Xb ) To obtain a characteristic diagram;
(2) Global average pool to euclidean distance: uniformly receiving and retaining all extracted characteristic information by adopting a global average pooling layer, and reducing the dimension of data into characteristic vectors;
calculated at A (X a) and A(Xb ) Euclidean distance of feature vectors in feature space;
Euc(X a ，X b )＝||A(X a )-A(X b )||. (6)
(3) Sigmoid regression: calculation of normalized Euclidean distance using Sigmoid regression method, will Euc (X a ，X b ) Mapping to (0, 1);
(4) Cross-entropy function: minimizing S (X) from the same class using Cross-entopyloss function a ，X b ) Approaching it to 0, maximizing S (X) from different classes a ，X b ) Making the temperature approach to 1;
L(X a ，X b ，Y)＝(1-Y)log(1-S(X a ，X b ))+Ylog S(X a ，X b )+λω， (8)
wherein ω is L 2 A regulater, λ representing a regularization coefficient;
empirically, a threshold of 0.2 is set to determine whether the input data is from the same category or from a different category.
And thirdly, when the user enters the room again, the receiving end of the COTS equipment collects gait CSI data again and uploads the gait CSI data to the cloud server, the cloud server verifies the physical identity of the person with the trained model, the physical identity is displayed and is sent to the control end, and if the physical information does not exist in the database, the cloud server sends alarm information to the control end.
Claims (1)
1. The lightweight passive identity authentication method based on the wireless network is characterized by being realized through a lightweight passive identity authentication system based on the wireless network, wherein the lightweight passive identity authentication system comprises a COTS device transmitting end, a COTS device receiving end, a cloud server and a control end;
the COTS equipment transmitting end and the COTS equipment receiving end are both provided with wireless network cards, and the wireless network cards are arranged in a room at intervals; the COTS equipment transmitting end is used for transmitting the WiFi signal carrying the CSI information, and the COTS equipment receiving end is used for receiving the WiFi signal in the environment and uploading the WiFi signal to the cloud server;
the cloud server comprises a data collection module, a preprocessing module and an authentication module; the data collection module collects CSI data of a user and user actions by using COTS equipment; the preprocessing module uses a low-pass filter to reduce noise of the CSI data collected by the data collection module, screens the antenna and the CSI stream, and generates CSI characteristic data; the authentication module trains, verifies and tests the preprocessed CSI characteristic data by using a machine learning method, and completes identity authentication and action recognition through a trained network model;
the control terminal is a webpage or mobile phone user terminal, is communicated with the COTS equipment and the cloud server, and transmits basic information of a user to the cloud server to monitor and early warn the identity of the user in real time;
the authentication method comprises the following steps:
firstly, a user enters a room for the first time, a WiFi signal is sent by a COTS equipment transmitting end, the WiFi signal is obviously disturbed by the user walking, and at the moment, the COTS equipment receiving end collects and extracts CSI data of four gait of the user and uploads the CSI data to a cloud server; simultaneously, the control end is used for transmitting the basic information of the user to the cloud server;
step two, the cloud server pre-processes the WiFi signals in the step one, and then models the pre-processed data by using a machine learning module;
(1) Denoising data: the linear interpolation algorithm is applied to the passive sensing field, gaps in the CSI data are filled, the gaps are uniformly distributed in a time domain, and a Butterworth filter is utilized to remove high-frequency noise with the frequency exceeding 80 Hz;
(2) And (3) antenna screening: increasing limited data samples by taking the CSI flow in each piece of CSI data as the smallest data unit; the COTS equipment is provided with 3 antennas, the correlation coefficient of the CSI flow between the antennas is calculated to evaluate the correlation of the antennas, 1 antenna with the lowest correlation is removed, and 2 antennas with higher correlation are reserved;
wherein ,is the correlation coefficient between the ith and jth CSI streams,/>For covariance between ith and jth CSI streams, +.> and />Respectively representing standard deviations of an ith CSI stream and a jth CSI stream;
(3) CSI stream screening: respectively calculating the amplitudes and variances of all the CSI streams on each antenna, and screening the CSI streams with low amplitudes and high variances;
the amplitudes of the CSI streams are replaced by the maximum value of the CSI stream data of the corresponding group; taking the maximum value of each group of CSI stream data as an abscissa and the variance as an ordinate, and making a scatter diagram in a two-dimensional coordinate system; then defining an optimal center point P as follows:
wherein, maximum i And Variance j Representing the maximum value and variance of the corresponding CSI streams;
the center point P is a point with the minimum value of the maximum values of all CSI stream data on each antenna as the abscissa and the maximum value of the variance as the ordinate; calculating Euclidean distances between other points and a central point P, and reserving CSI streams corresponding to the first 6 points with the minimum Euclidean distance;
the machine learning algorithm comprises the following specific steps:
(1) Snn+ghostnet network structure: combining the SNN framework and the GhostNet together to form an SNN+GhostNet network structure; the SNN+GhostNet network structure comprises four modules, namely a GhostNet module, a global average pool and Euclidean distance, sigmoid regression and a Cross-entopy function, wherein the GhostNet module is a core part;
the SNN framework groups individual data in the limited data into data pairs to further increase the data sample size, and defines the label Y of the data pairs as:
wherein ,ya and yb For constituent data pairs X a and Xb Is a label of (2);
then pair X data a and Xb The input module is input into a GhostNet module and comprises two CNN units and 1GhostNet unit; the first CNN unit performs data sampling, collates input data, the second CNN unit adjusts data dimension so as to facilitate subsequent processing, and the GhostNet unit is used for rapidly extracting deep action features;
wherein ,G(Xa) and G(Xb ) To obtain a characteristic diagram;
(2) Global average pool to euclidean distance: uniformly receiving and retaining all extracted characteristic information by adopting a global average pooling layer, and reducing the dimension of data into characteristic vectors;
calculated at A (X a) and A(Xb ) Euclidean distance of feature vectors in feature space;
Euc(X a ,X b )＝||A(X a )-A(X b )|| (6)
(3) Sigmoid regression: calculation of normalized Euclidean distance using Sigmoid regression method, will Euc (X a ,X b ) Mapping to (0, 1);
(4) Cross-entropy function: minimizing S (X) from the same class using Cross-entopyloss function a ,X b ) Approaching it to 0, maximizing S (X) from different classes a ,X b ) Making the temperature approach to 1;
L(X a ,X b ,Y)＝(1-Y)log(1-S(X a ,X b ))+Y logS(X a ,X b )+λω (8)
wherein ω is L 2 A regulater, λ representing a regularization coefficient; setting a threshold value of 0.2 to determine whether the input data is from the same category or different categories;
and thirdly, when the user enters the room again, the receiving end of the COTS equipment collects gait CSI data again and uploads the gait CSI data to the cloud server, the cloud server performs person identity matching verification on the CSI data by using the trained model, the person identity is displayed and sent to the control end, and if the person information does not exist in the database, the cloud server sends alarm information to the control end.
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