CN114394063A - Automobile keyless entry safety protection system based on gait analysis - Google Patents
Automobile keyless entry safety protection system based on gait analysis Download PDFInfo
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- B60—VEHICLES IN GENERAL
- B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
- B60R25/00—Fittings or systems for preventing or indicating unauthorised use or theft of vehicles
- B60R25/10—Fittings or systems for preventing or indicating unauthorised use or theft of vehicles actuating a signalling device
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
- B60R25/00—Fittings or systems for preventing or indicating unauthorised use or theft of vehicles
- B60R25/20—Means to switch the anti-theft system on or off
- B60R25/24—Means to switch the anti-theft system on or off using electronic identifiers containing a code not memorised by the user
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- G06—COMPUTING; CALCULATING OR COUNTING
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C9/00—Individual registration on entry or exit
- G07C9/00174—Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys
- G07C9/00309—Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys operated with bidirectional data transmission between data carrier and locks
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- G—PHYSICS
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- G07C9/00—Individual registration on entry or exit
- G07C9/00174—Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys
- G07C9/00571—Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys operated by interacting with a central unit
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- G07C9/00—Individual registration on entry or exit
- G07C9/00174—Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys
- G07C9/00896—Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys specially adapted for particular uses
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
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- H04W4/02—Services making use of location information
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- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
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Abstract
The invention discloses a car keyless entry safety protection system based on gait analysis, which comprises a Bluetooth module and a gait recognition module, wherein the Bluetooth module is used for connecting a car owner intelligent device through the Bluetooth module to carry out Bluetooth distance verification after finishing the last step of authentication on the basis of the original car keyless entry protocol, and feeding back the judgment result of the gait recognition module to the car; the gait recognition module is installed in the intelligent device and used for the intelligent device to perform gait recognition verification according to the recorded gait information of the intelligent device and the user behavior and make judgment in the intelligent device of the vehicle owner. According to the automobile keyless entry safety protection system, the state behavior information of the automobile owner is judged based on the Unet and ResNet processing sensor data mode, and the automobile entrance guard safety is protected and the safety protection performance of the automobile networking is improved through the double verification technology of the intelligent equipment, namely Bluetooth connection distance limitation and owner behavior state verification in the intelligent equipment.
Description
Technical Field
The invention relates to the technical field of automobile safety, in particular to an automobile keyless entry safety protection system based on gait analysis.
Background
With the development of the automobile industry and the improvement of social science and technology and productivity, a keyless entry system is more and more popular among automobiles of various manufacturers, and the system almost becomes a standard matching system of current factory automobile models. But the keyless entry system of the automobile has the potential safety hazard that an attacker can open the automobile door and even steal the automobile by using the system under the condition that an owner does not know the system.
When a vehicle is equipped with a Passive Key Entry and Start (PKES) system, the driver can unlock the doors as long as he/she is in the vicinity of the vehicle. In some cases, the driver must also press a button on the vehicle. Mutual communication between the key fob and the vehicle is required to verify that the driver is actually nearby. The vehicle then communicates with the key fob via a Low Frequency (LF) band (125khz), and the corresponding key fob responds to the request via an Ultra High Frequency (UHF) band communication. Fig. 1 shows an example of a message flow for authentication in a PKES system. The vehicle broadcasts a low frequency signal periodically to check for the presence of a suitable key fob, such as a beacon signal, in the vicinity. If the key fob is within a communication range of a low frequency band (e.g., 1-2 m), it receives a periodic low frequency band signal from the vehicle, enabling it to transmit a response signal in the UHF band. For security reasons, the data packets are encrypted using a long-term key that is pre-shared between the remote key fob and its respective vehicle. It is noted that in the PKES system, the driver can start the engine even without inserting a physical key into the ignition switch.
In consideration of cost control and convenient manufacturing, automobile manufacturers usually pay less attention to safety of middle-end automobile models equipped with keyless entry systems, and a large number of automobile models with potential safety hazards exist in the current market.
Because the keyless entry system used by most current automobiles has a relay attack hole, but most safety protection measures aiming at the hole at present need to carry out large-scale transformation on automobile keys and automobile hardware systems, for example, machine learning equipment is additionally arranged in the automobile through additional transmission position information of the automobile keys, and the function setting of an Electronic Control Unit (ECU) is changed, which means that the defense mode can not be widely popularized, and the practicability can be greatly reduced.
In addition, a method for judging the strength difference of the signals received by the annunciators by arranging a plurality of key signal receivers on the automobile is also provided, the method has a better protection effect on the one-way relay, and the judgment on the relay attack in response to the two-way relay still has a larger problem and is not high in accuracy.
Disclosure of Invention
Aiming at the problems, the invention designs and develops a new safety detection scheme, namely an automobile keyless entry safety protection system based on gait analysis, in order to change the original system less and protect the automobile keyless entry system better in consideration of the complexity of an automobile wireless communication system.
In order to achieve the above purpose, the invention provides the following technical scheme:
the invention provides a car keyless entry safety protection system based on gait analysis, which comprises a Bluetooth module and a gait recognition module, wherein the Bluetooth module is used for connecting a car owner intelligent device through the Bluetooth module to carry out Bluetooth distance verification after finishing the last step of authentication on the basis of the original car keyless entry protocol, and feeding back the judgment result of the gait recognition module to the car; the gait recognition module is installed in the intelligent device and used for the intelligent device to perform gait recognition verification according to the recorded gait information of the intelligent device and the user behavior and make judgment in the intelligent device of the vehicle owner.
Further, the gait recognition module adopts a deep learning network model.
Further, the training process of the deep learning network model comprises the following steps: the method comprises the steps of collecting data by using a sensor of intelligent equipment, carrying out uniform formatting treatment on training data, then entering into the encoding process of a Unet structure, carrying out data normalization operation after the Unet structure processes the data, and linking a full connection layer storage model result.
Further, the sensor comprises an acceleration sensor and a gyroscope.
Further, the step of the gait recognition module comprises: firstly, a residual error network is utilized to carry out feature extraction for coding, then decoding is carried out, classification of pixel points with the same size as that of an original image is returned, and after classification is obtained, a full-link layer is used for outputting.
Furthermore, the intelligent device is a smart phone.
Further, the automobile keyless entry safety protection system based on gait analysis comprises the following processing steps:
s1, the vehicle owner carries the intelligent key to get close to the vehicle door, and the keyless entry system is triggered;
s2, the automobile normally communicates with the intelligent key, the awakening and challenge codes are transmitted to the automobile key, and the key receives and verifies the encrypted unlocking instruction sent by using the universal high-frequency band after passing the verification;
s3, Bluetooth distance verification: after the automobile receives the instruction and passes the verification, the Bluetooth is started, and the Bluetooth equipment bound by the automobile owner is searched and connected within the communication distance of the Bluetooth;
s4, gait recognition and verification: after the intelligent device is automatically connected with the automobile, the automobile sends a verification request to the intelligent device, the intelligent device judges whether an action gait segment exists or not through sensor information, if the action gait segment exists, the whole gait action of a default owner accords with logic to judge that the unlocking is safe, a safe automatic unlocking signal is returned to the automobile through Bluetooth, otherwise, the intelligent device prompts the owner of the automobile, and the owner of the automobile needs to manually confirm whether the automobile is unlocked or the automobile is unlocked but pops up the automobile unlocking warning for the owner of the automobile.
Furthermore, in the gait recognition and verification process, firstly, formatting operation is carried out on data of the sensor, the transmitted data are subjected to standardization conversion according to dimensionality, meanwhile, data format reforming is carried out, the data meet the model agreement input standard, and then the data are transmitted into an analysis engine, and whether behavior gait segments exist in the data characteristic analysis sequence of the sensor or not is analyzed.
Further, the data of the sensors is set in six dimensions, including three dimensions of acceleration sensors and three dimensions of gyroscope angular velocity amounts.
And further, the unlocking behavior data of the vehicle owner is used as training data to train the gait recognition model again every time.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides an implementation scheme of an in-vehicle bus gateway safety detection method which is simple and convenient to operate, clear in flow and high in efficiency, the system does not depend on certain specific software and hardware, has the capabilities of identifying different bus domains and detecting message forwarding rules among the different bus domains, has the capability of detecting illegal routes, has the capability of automatically adapting bus interfaces, judges vehicle owner state behavior information based on a Unet and ResNet processing sensor data mode, and protects the door access safety of an automobile and improves the safety protection performance of the internet of vehicles by the double verification technology of intelligent equipment, namely Bluetooth connection distance limitation and vehicle owner behavior state verification in the intelligent equipment.
Drawings
In order to more clearly illustrate the embodiments of the present application or technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings can be obtained by those skilled in the art according to the drawings.
Fig. 1 is a flow chart of a car keyless entry security system based on gait analysis according to an embodiment of the invention.
Fig. 2 is an overall structure of a gait recognition module according to an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The automobile keyless entry safety protection system based on gait analysis comprises a Bluetooth module and a gait recognition module, wherein as shown in figure 1, the Bluetooth module is used for connecting an automobile with an intelligent device of an automobile owner through the Bluetooth module to carry out Bluetooth distance verification after finishing the last step of authentication on the basis of the original automobile keyless entry protocol, and feeding back the judgment result of the gait recognition module to the automobile; the gait recognition module is installed in the intelligent device and used for the intelligent device to perform gait recognition verification according to the recorded gait information of the intelligent device and the user behavior and make judgment in the intelligent device of the vehicle owner.
The intelligent device is an intelligent mobile phone.
After the last step of authentication is completed on the basis of the original automobile keyless entry protocol, the automobile tries to be connected with an intelligent device of an automobile owner through Bluetooth, such as a mobile phone, in the communication distance of the Bluetooth, the first layer of safety protection detection is acquiescent, then the intelligent device analyzes according to recorded gait information, in a common situation, walking-static state conversion often exists when the automobile owner uses a keyless entry system, and the automobile owner is not necessarily in a walking state when the automobile owner operates a door. Through the process, a warning can be given in the app bound by the owner in combination with the user behavior. Thereby providing a second layer of protection for the keyless entry system.
Regarding the gait recognition module:
the gait recognition module adopts a deep learning network model. The model structure is shown in fig. 2. The core part is formed by improving Unet and ResNet, and the data part is acquired by using an acceleration sensor and a gyroscope of the smart phone.
The training process of the deep learning network model comprises the following steps: and acquiring data by using a sensor of the intelligent equipment, wherein the sensor comprises an acceleration sensor and a gyroscope. The training data is subjected to unified formatting processing, then enters the encoding (down-sampling) process of the Unet structure, is subjected to data normalization operation after the data is processed by the Unet structure, and is linked with a full connection layer storage model result.
The steps of the gait recognition module include: firstly, a residual error network is utilized to carry out feature extraction for coding (down sampling), then decoding (up sampling) is carried out, then classification of pixel points with the same size as an original image is returned, and after classification is obtained, a full connection layer is used for outputting.
The automobile keyless entry safety protection system based on gait analysis comprises the following processing steps:
s1, firstly, normally completing a normal keyless entry system protocol flow through a vehicle owner, namely, triggering a keyless entry system when the vehicle owner carries an intelligent key to be close to a vehicle door (1-2 m);
s2, the automobile normally communicates with the intelligent key, the awakening and challenge codes are transmitted to the automobile key, and the key receives and verifies the encrypted unlocking instruction sent by using the universal high-frequency band after passing the verification;
s3, Bluetooth distance verification: after the automobile receives the instruction and passes the verification, the multiple verification mechanisms in the invention are awakened, the Bluetooth is started, and the Bluetooth equipment bound by the automobile owner is searched and connected within the communication distance of the Bluetooth;
s4, gait recognition and verification: after the intelligent device is automatically connected with the automobile, the automobile sends a verification request to the intelligent device (such as a mobile phone), the intelligent device judges whether the intelligent device is in a non-motion state or not through sensor (acceleration sensor and gyroscope) information, and if the intelligent device is in the non-motion state, the intelligent device automatically calls pre-recorded data with a proper time length preset in the previous section;
the method comprises the steps that data are firstly formatted, incoming data are subjected to standardized conversion according to dimensionality, data format reforming is carried out simultaneously, the data conform to a model agreement input standard, then the data conform to an analysis engine, whether a behavior gait segment exists in a data characteristic analysis sequence of a sensor or not is judged, if the behavior gait segment exists, the whole gait behavior of a vehicle owner is acquiescent to accord with logic and the unlocking is safe, a safe automatic unlocking signal is returned to the vehicle through Bluetooth, otherwise, the vehicle owner is prompted in intelligent equipment, and the vehicle owner needs to manually confirm whether the vehicle is unlocked or not or the vehicle is unlocked but pops up a vehicle unlocking warning for the vehicle owner.
In the present embodiment, the data of the sensors is set in six dimensions, including three dimensions of the acceleration sensor and three dimensions of the amount of angular velocity of the gyroscope. The specific behavior logic owner can freely set, and the subsequent model can be expanded, so that the method is not limited to the six-dimensional data information.
And S5, training the gait recognition model again by taking the unlocking behavior data of the car owner as training data each time, and improving the model, so that the model is continuously evolved, and the judgment speed and accuracy are continuously improved.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, apparatus embodiments, electronic device embodiments, computer-readable storage medium embodiments, and computer program product embodiments are described with relative simplicity as they are substantially similar to method embodiments, where relevant only as described in portions of the method embodiments.
The above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: those skilled in the art can still make modifications or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalents to some of them, within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the present disclosure, which should be construed in light of the above teachings. Are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A car keyless entry safety protection system based on gait analysis is characterized by comprising a Bluetooth module and a gait recognition module, wherein the Bluetooth module is used for connecting a car owner intelligent device through the Bluetooth module to carry out Bluetooth distance verification after finishing the last step of authentication on the basis of the original car keyless entry protocol, and feeding back the judgment result of the gait recognition module to the car; the gait recognition module is installed in the intelligent device and used for the intelligent device to perform gait recognition verification according to the recorded gait information of the intelligent device and the user behavior and make judgment in the intelligent device of the vehicle owner.
2. The system of claim 1, wherein the gait recognition module employs a deep learning network model.
3. The system of claim 2, wherein the training process of the deep learning network model comprises: the method comprises the steps of collecting data by using a sensor of intelligent equipment, carrying out uniform formatting treatment on training data, then entering into the encoding process of a Unet structure, carrying out data normalization operation after the Unet structure processes the data, and linking a full connection layer storage model result.
4. The system of claim 3, wherein the sensors include acceleration sensors and gyroscopes.
5. The system of claim 1, wherein the step of the gait recognition module comprises: firstly, a residual error network is utilized to carry out feature extraction for coding, then decoding is carried out, classification of pixel points with the same size as that of an original image is returned, and after classification is obtained, a full-link layer is used for outputting.
6. The system of claim 1, wherein the smart device is a smart phone.
7. The automobile keyless entry safety system based on gait analysis as claimed in claim 1, characterized by comprising the following processing steps:
s1, the vehicle owner carries the intelligent key to get close to the vehicle door, and the keyless entry system is triggered;
s2, the automobile normally communicates with the intelligent key, the awakening and challenge codes are transmitted to the automobile key, and the key receives and verifies the encrypted unlocking instruction sent by using the universal high-frequency band after passing the verification;
s3, Bluetooth distance verification: after the automobile receives the instruction and passes the verification, the Bluetooth is started, and the Bluetooth equipment bound by the automobile owner is searched and connected within the communication distance of the Bluetooth;
s4, gait recognition and verification: after the intelligent device is automatically connected with the automobile, the automobile sends a verification request to the intelligent device, the intelligent device judges whether an action gait segment exists or not through sensor information, if the action gait segment exists, the whole gait action of a default owner accords with logic to judge that the unlocking is safe, a safe automatic unlocking signal is returned to the automobile through Bluetooth, otherwise, the intelligent device prompts the owner of the automobile, and the owner of the automobile needs to manually confirm whether the automobile is unlocked or the automobile is unlocked but pops up the automobile unlocking warning for the owner of the automobile.
8. The automobile keyless entry safety protection system based on gait analysis as claimed in claim 7, wherein in the gait recognition and verification process, the formatting operation is firstly carried out on the data of the sensor, the input data is subjected to standardized conversion according to the dimensionality, meanwhile, the data format is reformed and accords with the model consent input standard, and then the input data is transmitted into the analysis engine, and whether a behavior gait segment exists in the data characteristic analysis sequence of the sensor is analyzed.
9. The system of claim 8, wherein the sensor data is arranged in six dimensions, including three dimensions for acceleration sensors and three dimensions for gyroscope angular rate.
10. The automobile keyless entry safety protection system according to claim 7 wherein each time the owner unlocking behavior data is used as training data to retrain the gait recognition model.
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| CN109271888A (en) * | 2018-08-29 | 2019-01-25 | 汉王科技股份有限公司 | Personal identification method, device, electronic equipment based on gait |
| CN111291865A (en) * | 2020-01-21 | 2020-06-16 | 北京工商大学 | Gait recognition method based on convolutional neural network and isolated forest |
| WO2021261267A1 (en) * | 2020-06-26 | 2021-12-30 | ソニーグループ株式会社 | Information processing device, information processing method, information processing program, and information processing system |
| CN112061077A (en) * | 2020-08-10 | 2020-12-11 | 浙江零跑科技有限公司 | Control system and method supporting mobile phone Bluetooth key function |
| CN112043280A (en) * | 2020-09-03 | 2020-12-08 | 南京邮电大学 | Dangerous driving behavior detection method and system based on smart phone gait recognition |
| CN112257559A (en) * | 2020-10-20 | 2021-01-22 | 江南大学 | Identity recognition method based on gait information of biological individual |
| CN112660067A (en) * | 2020-12-31 | 2021-04-16 | 上汽通用五菱汽车股份有限公司 | Non-time limit control method and system for automobile Bluetooth key |
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