CN113807213A - Behavior characteristic-based vehicle owner identity pre-authentication method - Google Patents
Behavior characteristic-based vehicle owner identity pre-authentication method Download PDFInfo
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Abstract
The invention discloses a vehicle owner identity pre-authentication method based on behavior characteristics, which specifically comprises the following steps: collecting pressure data of different volunteers when opening the vehicle door by using a pressure sensor, and carrying out manual marking; preprocessing the pressure data, and training the authentication model by utilizing the preprocessed pressure data to obtain a trained authentication model; collecting pressure data when a legal user of a vehicle opens a vehicle door for multiple times, and preprocessing the pressure data to be used as a reference sample set of the legal user; collecting pressure data of an unknown user when the door of the vehicle is opened, and preprocessing the pressure data to be used as an authentication sample; and performing similarity comparison on the authentication sample and each reference sample in the reference sample set by using the trained authentication model, performing pre-authentication, opening the vehicle door if the pre-authentication is passed, and otherwise, requiring the user to unlock by using a key. The method and the device pre-authenticate the legality of the user identity by utilizing the action characteristics of the user in the process of opening the vehicle door, thereby improving the user experience and the safety.
Description
Technical Field
The invention relates to a vehicle owner identity pre-authentication method based on behavior characteristics, and belongs to the technical field of identity authentication.
Background
In the traffic field, the identity authentication method for the owner mainly uses a key to unlock the vehicle, uses a wireless vehicle key to transmit radio waves to unlock the vehicle remotely, uses a keyless entry technology to unlock the vehicle, and the like. However, these techniques have drawbacks: basically, all unlocking modes need to carry extra unlocking equipment, a car key is easily imitated by an attacker, remote unlocking and keyless entry both have the threat of replay attack, and meanwhile, a keyless entry system has the defects in signature and certificate and is easily attacked. Therefore, in order to effectively authenticate the identity of the owner of the vehicle and improve the user experience and the security, a new identity authentication method needs to be provided.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method for pre-authenticating the identity of the vehicle owner based on the behavior characteristics is provided, and the identity of the vehicle owner can be effectively pre-authenticated to improve user experience and safety.
The invention adopts the following technical scheme for solving the technical problems:
a pre-authentication method for the identity of car owner based on behavior characteristics includes a model training phase, a user registration phase and an authentication phase,
the model training phase comprises the following steps:
the user registration phase comprises the following steps:
the authentication phase comprises the following steps:
step 4, collecting pressure data of an unknown user when the door of the vehicle is opened by using a pressure sensor, and preprocessing the pressure data to be used as an authentication sample;
and 5, performing similarity comparison on the authentication sample and each reference sample in the reference sample set by using the trained authentication model, performing pre-authentication, opening the vehicle door if the pre-authentication is passed, and unlocking by using a key if the pre-authentication is failed.
In a preferred embodiment of the present invention, the pressure sensor is a film-type pressure sensor and is attached to an inner side of the door handle.
As a preferable embodiment of the present invention, the preprocessing is specifically to perform 0-1 standard normalization on the pressure data.
As a preferred scheme of the present invention, the authentication model is a twin neural network, and is composed of two feature extraction networks with the same structure and shared parameters, each feature extraction network is formed by sequentially connecting a convolutional neural network and a pyramid pooling layer, each convolutional neural network includes a first convolutional layer, a first activation layer, a second convolutional layer and a second activation layer, which are sequentially connected, and the number of layers of the pyramid pooling layer is 8;
in the process of training the certification model, a contrast loss function is used for measuring the training of the model, wherein the contrast loss function is as follows:
wherein, L (w, (Y, S)i，Sj) Denotes a contrast loss function, w is a model parameter, Ew(Si，Sj) When the two samples are samples of the same user, Y is 0; when two samples are samples of different users, Y is 1, m is a distance interval, Si，SjFor any two samples.
As a preferred embodiment of the present invention, the specific process of step 5 is as follows:
step 51, calculating the similarity between the authentication sample and each reference sample in the reference sample set by using the trained authentication model to obtain a similarity set;
step 52, pre-authenticating the authentication sample of the unknown user by using the authentication method based on the voting idea, and collecting the similarityEach similarity in the sum is compared with a threshold value TDCompared, greater than a threshold value TDConsidering that the reference sample vote corresponding to the similarity is authentication failure, considering that the vote is successful when the reference sample vote is less than or equal to a threshold value, and obtaining a vote set C, wherein TDIs 1.2;
step 53, counting the votes in the voting set C, if the proportion of the votes successfully authenticated occupying the total votes is larger than the threshold value TCIf the sample is successfully pre-authenticated, opening the vehicle door; otherwise, the pre-authentication fails and requires the user to unlock with a key, wherein TCIs 0.8.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
the invention provides a pre-authentication method for vehicle owner identity based on behavior characteristics, which extracts behavior characteristics of a user by analyzing pressure data of a sensor when the user pulls a vehicle door, and compares the behavior characteristics with a stored reference set of legal users to realize pre-authentication of the vehicle owner. Compared with the traditional vehicle owner authentication means, the user experience and the safety are improved.
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Fig. 1 is an overall architecture diagram of a vehicle owner identity pre-authentication method based on behavior characteristics according to the present invention.
FIG. 2 is a schematic diagram of an authentication model based on a twin neural network in the present invention.
FIG. 3 is a schematic diagram of a twin neural network convolutional layer.
FIG. 4 is a schematic diagram of a twin neural network pyramid pooling layer.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
The invention utilizes the film type pressure sensor to collect pressure data when a user pulls the door handle, analyzes the behavior habit of the user and carries out pre-authentication, if the user passes the authentication, the door is opened, otherwise, the door is unlocked by using a traditional key.
As shown in fig. 1, the present invention is an overall architecture diagram of a vehicle owner identity pre-authentication method based on behavior characteristics, and the method is divided into three stages: the method comprises a model training phase, a user registration phase and an authentication phase, wherein the three phases comprise two common steps: a data acquisition step and a data preprocessing step; meanwhile, the model training stage also comprises the training of the authentication model, the user registration stage also comprises the storage of an authentication reference set of a legal user, and the authentication stage also comprises the authentication of an unknown user by using the trained model.
A data acquisition step: the method comprises the steps that a film type pressure sensor is additionally arranged on the inner side of a vehicle door handle, the pressure change of the vehicle door handle when a user pulls the vehicle door handle is collected in real time, manual data labeling is carried out, a data source is marked, and the collected data format is time sequence data;
a data preprocessing step: performing 0-1 standard normalization on the pressure data in the data preprocessing step;
and (3) certification model training: before leaving a factory, pressure data of a plurality of volunteers when opening the vehicle door for a plurality of times is collected in advance to serve as a training set, and the twin neural network is trained to obtain the capability of performing similarity calculation on time series data.
The schematic structure of the twin neural network is shown in fig. 2, and the twin neural network is composed of two feature extraction networks with the same structure and shared parameters, wherein the feature extraction network is composed of a one-dimensional convolution neural network (fig. 3) and a pyramid pooling layer (fig. 4). The former is composed of two one-dimensional convolution layers and an active layer, and the latter has 8 layers for generating feature vectors of the same length for similarity calculation. Similarity calculation is performed on two feature vectors of the same length using the L2 norm. In the training process, the training of the network is measured by using the contrast loss, and the contrast loss function is as follows:
wherein Ew(Si，Sj) When the two samples are samples of the same user, Y is 0; when two samples are samples of different users, Y is 1, w is a model parameter, m is a distance interval, and is a hyper-parameter, and S is set to be 2i，SjFor any two samples.
A registration step: in the registration stage, a legal user pulls the vehicle door for many times, the pressure sensor collects pressure data of the user, and a reference sample set of the legal user is formed after preprocessing and storage.
Identity authentication: in the authentication stage, pressure data are collected when a user pulls a vehicle door, the pressure data are preprocessed to obtain an authentication sample of the user, and the trained authentication model is used for calculating the similarity between the authentication sample and all reference samples in a reference sample set to obtain a similarity set. An authentication sample of the user is authenticated using an authentication method based on a voting idea. For each similarity coefficient in the similarity set and the threshold value TDComparing, if the reference sample votes are judged to be authentication failure, if the reference sample votes are larger than the threshold, the reference sample votes are judged to be authentication success, and a voting set C is obtained, wherein T is greater than the thresholdDIs 1.2. Counting the votes in the voting set C, if the proportion of votes occupying the total votes is larger than the threshold value TCThe authentication sample is successfully authenticated, otherwise the authentication fails, where TCIs 0.8.
The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modifications made on the basis of the technical scheme according to the technical idea of the present invention fall within the protection scope of the present invention.
Claims (5)
1. A pre-authentication method for the identity of an owner based on behavior characteristics comprises a model training phase, a user registration phase and an authentication phase,
the model training phase comprises the following steps:
step 1, collecting pressure data of different volunteers when opening a vehicle door by using a pressure sensor, and carrying out manual marking to mark a pressure data source;
step 2, preprocessing the pressure data, and training the authentication model by using the preprocessed pressure data to obtain a trained authentication model;
the user registration phase comprises the following steps:
step 3, collecting pressure data of a legal user of the vehicle when the door of the vehicle is opened for multiple times by using a pressure sensor, and preprocessing the pressure data to be used as a reference sample set of the legal user;
the authentication phase comprises the following steps:
step 4, collecting pressure data of an unknown user when the door of the vehicle is opened by using a pressure sensor, and preprocessing the pressure data to be used as an authentication sample;
and 5, performing similarity comparison on the authentication sample and each reference sample in the reference sample set by using the trained authentication model, performing pre-authentication, opening the vehicle door if the pre-authentication is passed, and unlocking by using a key if the pre-authentication is failed.
2. The pre-authentication method for vehicle owner identity based on behavioral characteristics according to claim 1, wherein the pressure sensor is a thin film pressure sensor and is installed inside a door handle.
3. The method for pre-authenticating the identity of the vehicle owner based on the behavioral characteristics according to claim 1, wherein the pre-processing is specifically to perform 0-1 standard normalization on the pressure data.
4. The pre-authentication method for the identity of the vehicle owner based on the behavioral characteristics according to claim 1, wherein the authentication model is a twin neural network and is composed of two feature extraction networks with the same structure and shared parameters, the feature extraction networks are formed by sequentially connecting a convolutional neural network and a pyramid pooling layer, the convolutional neural network comprises a first convolutional layer, a first activation layer, a second convolutional layer and a second activation layer which are sequentially connected, and the number of layers of the pyramid pooling layer is 8;
in the process of training the certification model, a contrast loss function is used for measuring the training of the model, wherein the contrast loss function is as follows:
wherein, L (w, (Y, S)i，Sj) Denotes a contrast loss function, w is a model parameter, Ew(Si，Sj) When the two samples are samples of the same user, Y is 0; when two samples are samples of different users, Y is 1, m is a distance interval, Si，SjFor any two samples.
5. The pre-authentication method for the identity of the vehicle owner based on the behavioral characteristics according to claim 1, wherein the specific process of the step 5 is as follows:
step 51, calculating the similarity between the authentication sample and each reference sample in the reference sample set by using the trained authentication model to obtain a similarity set;
step 52, using the authentication method based on the voting idea to pre-authenticate the authentication sample of the unknown user, and comparing each similarity in the similarity set with the threshold TDCompared, greater than a threshold value TDConsidering that the reference sample vote corresponding to the similarity is authentication failure, considering that the vote is successful when the reference sample vote is less than or equal to a threshold value, and obtaining a vote set C, wherein TDIs 1.2;
step 53, counting the votes in the voting set C, if the proportion of the votes successfully authenticated occupying the total votes is larger than the threshold value TCIf the sample is successfully pre-authenticated, opening the vehicle door; otherwise, the pre-authentication fails and requires the user to unlock with a key, wherein TCIs 0.8.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110533631A (en) * | 2019-07-15 | 2019-12-03 | 西安电子科技大学 | SAR image change detection based on the twin network of pyramid pondization |
| CN111371951A (en) * | 2020-03-03 | 2020-07-03 | 北京航空航天大学 | Smart phone user authentication method and system based on electromyographic signals and twin neural network |
| CN112487374A (en) * | 2020-12-04 | 2021-03-12 | 重庆邮电大学 | Self-adaptive continuous identity authentication method and system based on touch screen interaction behavior |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110533631A (en) * | 2019-07-15 | 2019-12-03 | 西安电子科技大学 | SAR image change detection based on the twin network of pyramid pondization |
| CN111371951A (en) * | 2020-03-03 | 2020-07-03 | 北京航空航天大学 | Smart phone user authentication method and system based on electromyographic signals and twin neural network |
| CN112487374A (en) * | 2020-12-04 | 2021-03-12 | 重庆邮电大学 | Self-adaptive continuous identity authentication method and system based on touch screen interaction behavior |
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