Security credential management system, method and computer readable medium based on movement behavior prediction

Abstract

This invention discloses a secure credential management system, method, and computer-readable medium based on mobile behavior prediction. The system involves an in-vehicle terminal device broadcasting a security protocol data unit (GPRS) to a roadside terminal device (Roadside Terminal) which receives the GPRS and obtains a pseudonym and a butterfly public key. The system generates first statistics by counting the number of butterfly public keys and GPRS packets within a given time period. Furthermore, an authorization credential center retrieves the cocoon public key corresponding to the butterfly public key from the first statistics and generates second statistics by counting the number of Roadside Terminal devices associated with the cocoon public key and the number of GPRS packets. Additionally, a login center with a mobile behavior prediction mechanism retrieves the caterpillar public key corresponding to the cocoon public key from the second statistics and generates third statistics by counting the number of Roadside Terminal devices associated with the caterpillar public key, the number of GPRS packets, and the number of cocoon public keys used by the caterpillar public key. This data is used to predict the number of cocoon public keys required by the in-vehicle terminal device in the next time period.

Claims

1. A secure credential management system based on mobile behavior prediction, comprising: The vehicle's onboard terminal equipment is a broadcast security protocol data unit; The roadside terminal device is communicatively connected to the vehicle-mounted terminal device. Within the transmission range of the vehicle-mounted terminal device, the roadside terminal device receives the broadcast security protocol data unit (PCA). It then retrieves the pseudonym credential and its associated butterfly public key from the PCA. The roadside terminal device then counts the number of packets containing the butterfly public key and its corresponding PCA within a given time period, generating the first set of statistical data. The authorization credential center is communicatively connected to the roadside terminal device. The terminal device obtains the cocoon public key corresponding to the butterfly public key from the first statistical data collected by the roadside terminal device, and then the authorization credential center counts the number of roadside terminal devices associated with the cocoon public key and the number of packets of the security protocol data unit within the time period to generate the second statistical data collected by the authorization credential center; and the login center with a mobility behavior prediction mechanism is communicatively connected to the authorization credential center and used to predict the mobility behavior of the vehicle, wherein the mobility behavior prediction mechanism... The login center of the detection mechanism obtains the caterpillar public key corresponding to the cocoon public key from the second statistical data collected by the authorization credential center. Then, the login center with the mobility behavior prediction mechanism counts the number of roadside terminal devices associated with the caterpillar public key, the number of packets of the security protocol data unit, and the number of cocoon public keys used by the caterpillar public key within the time period to generate the third statistical data collected by the login center with the mobility behavior prediction mechanism. Based on the collected third statistical data, the login center with the mobility behavior prediction mechanism predicts the number of cocoon public keys required by the vehicle terminal device in the next time period. In addition, the login center with the mobility behavior prediction mechanism uses a machine learning model with the vehicle architecture of the vehicle terminal device as the prediction model to predict the vehicle's mobility behavior. The login center with the mobility behavior prediction mechanism also uses machine learning methods to predict the number of pseudonym credentials required by the vehicle, so that the login center with the mobility behavior prediction mechanism can pre-produce the pseudonym credentials for the vehicle based on the number of pseudonym credentials.

2. The security credential management system as described in claim 1, wherein, The vehicle-mounted terminal device periodically broadcasts the security protocol data unit via a wireless network, so that the roadside terminal device can receive the broadcast security protocol data unit within the transmission range of the vehicle-mounted terminal device. The security protocol data unit carries the pseudonym credentials of the vehicle-mounted terminal device, and the pseudonym credentials of the vehicle-mounted terminal device include the butterfly public key of the vehicle-mounted terminal device.

3. The security credential management system as described in claim 1, wherein, The login center with the mobile behavior prediction mechanism has a mobile behavior prediction module, which adjusts the value of the cocoon public key based on the number of roadside terminal devices associated with the caterpillar public key within a certain time period. The more roadside terminal devices associated with the caterpillar public key within a certain time period, the larger the value of the cocoon public key; and the fewer roadside terminal devices associated with the caterpillar public key within a certain time period, the smaller the value of the cocoon public key.

4. A secure credential management system based on mobile behavior prediction, comprising: The vehicle's onboard terminal equipment is a broadcast security protocol data unit; The roadside terminal device is communicatively connected to the vehicle-mounted terminal device. Within the transmission range of the vehicle-mounted terminal device, the roadside terminal device receives the broadcast security protocol data unit (PCA). It then retrieves the pseudonym credential and its associated butterfly public key from the PCA. The roadside terminal device then counts the number of packets containing the butterfly public key and its corresponding PCA within a given time period, generating a first set of statistics. The authorization credential center is communicatively connected to the roadside terminal device. It retrieves the cocoon public key corresponding to the butterfly public key from the first set of statistics. The authorization credential center then counts the number of roadside terminal devices associated with the cocoon public key and the number of PCA packets within that time period, generating a second set of statistics. Finally, the login center with a mobility behavior prediction mechanism is communicatively connected to the authorization credential center. The login center with the motion prediction mechanism obtains the caterpillar public key corresponding to the cocoon public key from the second statistical data collected by the authorization credential center. Then, the login center with the motion prediction mechanism counts the number of roadside terminal devices associated with the caterpillar public key, the number of packets of the security protocol data unit, and the number of cocoon public keys used by the caterpillar public key within the time period to generate the third statistical data collected by the login center with the motion prediction mechanism. Based on the collected third statistical data, the login center with the motion prediction mechanism predicts the number of cocoon public keys required by the vehicle terminal device in the next time period. The login center with the motion prediction mechanism has a motion prediction module, which sets a maximum value and a minimum value for the number of cocoon public keys, and the value of the number of cocoon public keys is between the maximum value and the minimum value.

5. A secure credential management system based on mobile behavior prediction, comprising: The vehicle's onboard terminal equipment is a broadcast security protocol data unit; The roadside terminal device is communicatively connected to the vehicle-mounted terminal device. Within the transmission range of the vehicle-mounted terminal device, the roadside terminal device receives the broadcast security protocol data unit (PCA). It then retrieves the pseudonym credential and its associated butterfly public key from the PCA. The roadside terminal device then counts the number of packets containing the butterfly public key and its corresponding PCA within a given time period, generating a first set of statistics. The authorization credential center is communicatively connected to the roadside terminal device. It retrieves the cocoon public key corresponding to the butterfly public key from the first set of statistics. The authorization credential center then counts the number of roadside terminal devices associated with the cocoon public key and the number of PCA packets within that time period, generating a second set of statistics. A login center with a mobility behavior prediction mechanism is communicatively connected to the authorization credential center. The system is used to predict the vehicle's movement behavior. The login center with the movement behavior prediction mechanism obtains the caterpillar public key corresponding to the cocoon public key from the second statistical data collected by the authorization credential center. It then counts the number of roadside terminal devices associated with the caterpillar public key, the number of packets of the security protocol data unit, and the number of cocoon public keys used by the caterpillar public key within the given time period. This generates a third statistical data set by the login center, which then predicts the number of cocoon public keys required for the vehicle-mounted terminal device in the next time period based on this third statistical data. The login center with the movement behavior prediction mechanism has a movement behavior prediction module that adjusts the number of cocoon public keys based on the number of roadside terminal devices associated with the caterpillar public key within the given time period, or based on the number of packets of the security protocol data unit.

6. A secure credential management system based on mobile behavior prediction, comprising: The vehicle's onboard terminal equipment is a broadcast security protocol data unit; The roadside terminal device is communicatively connected to the vehicle-mounted terminal device. Within the transmission range of the vehicle-mounted terminal device, the roadside terminal device receives the broadcast security protocol data unit (PCA). It then retrieves the pseudonym credential and its associated butterfly public key from the PCA. The roadside terminal device then counts the number of packets containing the butterfly public key and its corresponding PCA within a given time period, generating a first statistical data set. The authorization credential center is communicatively connected to the roadside terminal device. The authorization credential center retrieves the first statistical data from the roadside terminal device. The system obtains the cocoon public key corresponding to the butterfly public key from the data. Then, the authorization credential center counts the number of roadside terminal devices associated with the cocoon public key and the number of packets of the security protocol data unit within a given time period to generate the second statistical data collected by the authorization credential center. A login center with a mobility behavior prediction mechanism is connected to the authorization credential center and used to predict the vehicle's mobility behavior. The login center with the mobility behavior prediction mechanism obtains the caterpillar public key corresponding to the cocoon public key from the second statistical data collected by the authorization credential center, and then counts the number of packets of the cocoon public key associated with the caterpillar public key. The login center with a mobility behavior prediction mechanism generates third statistical data based on the number of roadside terminal devices associated with the caterpillar public key, the number of packets of the security protocol data unit, and the number of cocoon public keys used by the caterpillar public key within a given time period. This third statistical data allows the login center to predict the number of cocoon public keys required by the vehicle-mounted terminal device in the next time period. The login center includes a mobility behavior prediction module that calculates the number of roadside terminal devices associated with the caterpillar public key of any vehicle-mounted terminal device within that time period. The number of backups is ranked from least to most among all vehicle-mounted terminal devices to obtain a first weight. The number of packets of the security protocol data unit of any vehicle-mounted terminal device is ranked from least to most among all vehicle-mounted terminal devices to obtain a second weight. The number of cocoon public keys used corresponding to the caterpillar public key of any vehicle-mounted terminal device is ranked from least to most among all vehicle-mounted terminal devices to obtain a third weight. The mobility behavior prediction module sets the number of cocoon public keys required for the next time period of any vehicle-mounted terminal device based on the first weight, the second weight, the third weight, and the maximum and minimum values ​​of the number of cocoon public keys.

7. A method for managing secure credentials based on mobile behavior prediction, comprising: The vehicle's onboard terminal equipment broadcasts a security protocol data unit; The roadside terminal receives the broadcast security protocol data unit within the transmission range of the vehicle-mounted terminal. The roadside terminal then obtains the pseudonym credential and its associated butterfly public key from the security protocol data unit. The roadside terminal then counts the number of packets containing the butterfly public key and its corresponding security protocol data unit received within a given time period, generating a first statistical data set. The authorization credential center retrieves the cocoon public key corresponding to the butterfly public key from the first statistical data. The authorization credential center then counts the number of roadside terminals associated with the cocoon public key and the number of packets containing the security protocol data unit within that time period, generating a second statistical data set. Finally, a login center with a mobility prediction mechanism predicts the vehicle's mobility behavior, wherein the login center with the mobility prediction mechanism retrieves data from the second statistical data. The system obtains the caterpillar public key corresponding to the cocoon public key. Then, the login center with the mobility prediction mechanism counts the number of roadside terminal devices associated with the caterpillar public key, the number of packets of the security protocol data unit, and the number of cocoon public keys used by the caterpillar public key within the given time period. This generates third statistical data, which the login center uses to predict the number of cocoon public keys required for the vehicle-mounted terminal device in the next time period. Furthermore, the login center constructs a machine learning model for vehicles equipped with the vehicle-mounted terminal device to predict their mobility behavior. It also uses machine learning to predict the number of pseudonym credentials required for the vehicle, allowing the login center to pre-produce pseudonym credentials for the vehicle based on the required number of pseudonym credentials.

8. The security credential management method as described in claim 7, wherein, The vehicle-mounted terminal device periodically broadcasts the security protocol data unit via a wireless network, so that the roadside terminal device can receive the broadcast security protocol data unit within the transmission range of the vehicle-mounted terminal device. The security protocol data unit carries the pseudonym credentials of the vehicle-mounted terminal device, and the pseudonym credentials of the vehicle-mounted terminal device include the butterfly public key of the vehicle-mounted terminal device.

9. The security credential management method as described in claim 7 further includes the mobile behavior prediction module of the login center with the mobile behavior prediction mechanism adjusting the value of the cocoon public key based on the number of roadside terminal devices associated with the caterpillar public key during the time period. The more roadside terminal devices associated with the caterpillar public key during the time period, the larger the value of the cocoon public key; and the fewer roadside terminal devices associated with the caterpillar public key during the time period, the smaller the value of the cocoon public key.

10. A method for managing secure credentials based on mobile behavior prediction, comprising: The vehicle's onboard terminal equipment broadcasts a security protocol data unit; The roadside terminal receives the broadcast security protocol data unit within the transmission range of the vehicle-mounted terminal. The roadside terminal then obtains the pseudonym credential and its associated butterfly public key from the security protocol data unit. The roadside terminal then counts the number of packets containing the butterfly public key and its corresponding security protocol data unit received within a given time period, generating a first statistical data set. The authorization credential center retrieves the cocoon public key corresponding to the butterfly public key from the first statistical data. The authorization credential center then counts the number of roadside terminals associated with the cocoon public key and the number of packets containing the security protocol data unit within that time period, generating a second statistical data set. Finally, a login center with a motion behavior prediction mechanism predicts the vehicle's motion behavior. The login center of the behavior prediction mechanism obtains the caterpillar public key corresponding to the cocoon public key from the second statistical data collected by the authorization credential center. Then, the login center with the mobile behavior prediction mechanism counts the number of roadside terminal devices associated with the caterpillar public key, the number of packets of the security protocol data unit, and the number of cocoon public keys used by the caterpillar public key within the time period to generate the third statistical data collected by the login center with the mobile behavior prediction mechanism. Based on the collected third statistical data, the login center with the mobile behavior prediction mechanism predicts the number of cocoon public keys required by the vehicle terminal device in the next time period. The login center with the mobile behavior prediction mechanism also sets a maximum value and a minimum value for the number of cocoon public keys, and the value of the number of cocoon public keys is between the maximum value and the minimum value.

11. A method for managing secure credentials based on mobile behavior prediction, comprising: The vehicle's onboard terminal equipment broadcasts a security protocol data unit; The roadside terminal receives the broadcast security protocol data unit within the transmission range of the vehicle-mounted terminal. The roadside terminal then obtains the pseudonym credential and its associated butterfly public key from the security protocol data unit. The roadside terminal then counts the number of packets containing the butterfly public key and its corresponding security protocol data unit received within a given time period, generating a first statistical data set. The authorization credential center retrieves the cocoon public key corresponding to the butterfly public key from the first statistical data. The authorization credential center then counts the number of roadside terminals associated with the cocoon public key and the number of packets containing the security protocol data unit within that time period, generating a second statistical data set. Finally, a login center with a motion behavior prediction mechanism predicts the vehicle's motion behavior. The login center of the mechanism obtains the caterpillar public key corresponding to the cocoon public key from the second statistical data collected by the authorization credential center. Then, the login center with the mobility behavior prediction mechanism collects statistics on the number of roadside terminal devices associated with the caterpillar public key, the number of packets of the security protocol data unit, and the number of cocoon public keys used by the caterpillar public key within the time period to generate the third statistical data collected by the login center with the mobility behavior prediction mechanism. Based on the collected third statistical data, the login center with the mobility behavior prediction mechanism predicts the number of cocoon public keys required by the vehicle terminal device in the next time period. The login center with the mobility behavior prediction mechanism adjusts the value of the cocoon public key based on the number of roadside terminal devices associated with the caterpillar public key within the time period, or adjusts the value of the cocoon public key based on the number of packets of the security protocol data unit.

12. A method for managing secure credentials based on mobile behavior prediction, comprising: The vehicle's onboard terminal equipment broadcasts a security protocol data unit; The roadside terminal receives the broadcast security protocol data unit within the transmission range of the vehicle-mounted terminal. The roadside terminal then obtains the pseudonym credential and its contained butterfly public key from the security protocol data unit. The roadside terminal then counts the number of packets containing the butterfly public key and its corresponding security protocol data unit received within a time period, generating the first statistical data. The authorization credential center retrieves the cocoon public key corresponding to the butterfly public key from the first statistical data collected by the roadside terminal, and then counts the cocoon public key. The number of roadside terminal devices associated with the public key and the number of packets of the security protocol data unit within the specified time period are used to generate the second statistical data collected by the authorization credential center. The login center with a mobility prediction mechanism predicts the vehicle's movement behavior. Specifically, the login center with the mobility prediction mechanism obtains the caterpillar public key corresponding to the cocoon public key from the second statistical data collected by the authorization credential center. Then, the login center with the mobility prediction mechanism counts the number of roadside terminal devices associated with the caterpillar public key and the number of packets of the security protocol data unit within the specified time period. The number of packets and the number of cocoon public keys used corresponding to the caterpillar public key are used to generate third statistical data collected by the login center with the mobile behavior prediction mechanism. Based on this third statistical data, the login center predicts the number of cocoon public keys required by the vehicle terminal device in the next time period. Furthermore, one of the mobile behavior prediction modules of the login center calculates the ranking of the number of roadside terminal devices associated with the caterpillar public key of any vehicle terminal device within that time period, from least to most, among all vehicle terminal devices. A first weight is obtained, and the number of packets of the security protocol data unit of any vehicle terminal device is ranked from least to most among all vehicle terminal devices to obtain a second weight. The number of cocoon public keys used by the caterpillar public key of any vehicle terminal device is ranked from least to most among all vehicle terminal devices to obtain a third weight. The login center with the mobile behavior prediction mechanism sets the number of cocoon public keys required for the next time period of any vehicle terminal device based on the first weight, the second weight, the third weight, and the maximum and minimum values ​​of the number of cocoon public keys.

13. A computer-readable medium, applied in a computing device or computer, storing instructions and executing the computer-readable medium via a processor and memory, so as to perform the security credential management method based on mobile behavior prediction as described in any one of claims 7 to 12 when executing the computer-readable medium.

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