CN111767585A - Object identification method and device, electronic equipment and storage medium - Google Patents
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Abstract
The application discloses an object identification method, an object identification device, electronic equipment and a storage medium, relates to the field of information processing, and can be applied to intelligent transportation or automatic driving scenes. The specific implementation scheme is as follows: acquiring related information of a first object of a target vehicle; determining a category of the first object and determining a security attribute value of the first object based on the related information of the first object; determining an importance level of the first object based on the security attribute value of the first object.
Description
Technical Field
The present application relates to the field of computer technology, and more particularly, to the field of information processing.
Background
With the increase of the degree of intelligence of vehicles, networking with the cloud has become a common capability of vehicles. Meanwhile, the internet of vehicles also faces more and more information security problems. There is therefore a need to assess the safety risk of a vehicle. In the work of evaluating information security risks of vehicles, assets are main action objects of threats, and how to efficiently and accurately identify the assets (or objects) in the vehicles becomes a problem to be solved.
Disclosure of Invention
The disclosure provides an object identification method, an object identification device, an electronic device and a storage medium.
According to a first aspect of the present disclosure, there is provided an object recognition method, the method comprising:
acquiring related information of a first object of a target vehicle;
determining a category of the first object and determining a security attribute value of the first object based on the related information of the first object;
determining an importance level of the first object based on the security attribute value of the first object.
According to a second aspect of the present disclosure, there is provided an object recognition apparatus, the apparatus comprising:
an acquisition module for acquiring information relating to a first object of a target vehicle;
the identification module is used for determining the category of the first object and determining the security attribute value of the first object based on the related information of the first object; and determining an importance level of the first object based on the security attribute value of the first object.
According to a third aspect of the present disclosure, there is provided an electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the above-described method.
According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the above method.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
FIG. 1 is a schematic flow chart of an object recognition method according to an embodiment of the present application;
FIG. 2 is a first schematic diagram of a first structural configuration of an object recognition apparatus according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a second exemplary structure of an object recognition apparatus according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device implementing an object recognition method according to an embodiment of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The present embodiment provides an object identification method, as shown in fig. 1, the method includes:
s101: acquiring related information of a first object of a target vehicle;
s102: determining a category of the first object and determining a security attribute value of the first object based on the related information of the first object;
s103: determining an importance level of the first object based on the security attribute value of the first object.
The scheme of the embodiment of the application can be applied to the server. The scheme of the embodiment of the application can be applied to intelligent transportation or automatic driving scenes.
Before performing S101, an object classification table may be predetermined.
It should be noted first that the object in this application may be an asset of a vehicle. Accordingly, the manner of determining the object classification table may include: according to the expression form of the assets (or objects), the assets of the vehicle are divided into three categories of hardware, software and data; and 9 attributes of a security chip, a debugging interface, a communication interface, firmware, an operating system, an application program, a system configuration file, sensitive data and log information, and finally forming an object classification table. Alternatively, the object classification table may also be referred to as an asset classification table. As shown in the following table:
the preset object classification table comprises: the preset category and the related information of at least one preset object corresponding to the preset category; wherein the preset categories include: the system comprises a security chip, a debugging port, a communication interface, firmware, an operating system, an application program, a system configuration file, sensitive data and log information.
Wherein, at least one object can be all objects or assets contained in the intelligent automobile or the unmanned automobile.
For example, the object classification table includes operating system classes, which may include various operating systems currently used for smart cars or unmanned vehicles, such as Linux operating system, Android operating system, QNX operating system, AutoSar, and so on, which are respectively referred to as operating systems 1, 2, and 3, and are all exhaustive in the object classification table. As another example, the application class may include diagnostic protocols, routing services, application services, port services, and the like.
Further, the preset object classification table further includes: and the preset confidentiality score, the preset integrity score and the preset feasibility score corresponding to each preset object.
After the object classification of the network security of the vehicle is completed, that is, after the object classification table is obtained, a confidentiality score, a preset integrity score, and a preset feasibility score are preset for each of the objects of the respective classes in the preset object classification table from three dimensions of confidentiality, integrity, and availability (hereinafter, referred to as CIA triad), respectively. That is, the preset CIA tri-attribute assignment of each object under each category in the object classification table can be finally obtained.
In one example, the assignment of CIA trilogy may be divided into four different levels, high, medium, and low, respectively, corresponding to different degrees of impact on confidentiality, integrity, and availability of the automobile network object to be protected. For example, confidentiality in the security assets of the automobile network is to ensure that information is enjoyed by authorized persons and not leaked to unauthorized persons, such as control instructions sent by the T _ Box to the CGW, whether there is plaintext leakage, whether it is intercepted by an attacker. If the information leakage of the T-Box affects part of vehicle types or all vehicle types, assigning the C attribute in the CIA to be very high; if the information leakage of the T-Box can affect the vehicle type, the value is assigned as high; if the information leakage of the T-Box only affects the vehicle, the value is assigned as high; if the information leak of the T-Box has no effect on only this asset or vehicle, the value is assigned low. Specifically, each object may be assigned according to the CIA assignment basis table. It should be understood that the above is only one way of presetting the assignment for the security attribute, and in the actual process, the CIA triad is assigned to scores of 1, 2, 3 and 4 respectively for representing different levels of low, medium, high and high. The specific assignment method is not limited in this embodiment, and is not exhaustive.
In S101, the target vehicle may be any one of a plurality of vehicles from which the server can acquire the related information.
The first object is any one of a plurality of objects contained in the target vehicle and can be regarded as an asset to be identified in the target vehicle; that is, a plurality of objects of the target vehicle may each perform the above processes of S101 to S103 as the first object. In this embodiment, only one of the objects will be described, and the processing of all the objects will not be described repeatedly.
The related information of the first object can be regarded as at least one of the name, identification, number and the like of the asset to be identified.
In addition, in S101, the related information of the first object of the target vehicle is obtained, the related information of all the objects of the target vehicle is uploaded to the server (or the cloud server) for the target vehicle, and then when each object (or asset) of the target vehicle is identified by the cloud server, the object or asset currently identified is used as the first object, and the related information of the object or asset is used as the related information of the first object.
In a further S102, the determining the category of the first object based on the related information of the first object includes:
and determining the category of the first object based on a preset object classification table and the related information of the first object.
The content of the preset object classification table is already described in the foregoing embodiments, and is not repeated here.
For example, if the first object is an operating system, the related information of the first object may include: name, or identification or number of the operating system. Accordingly, based on the name or identification of the first object, the category corresponding to the first object may be determined from a preset object classification table.
In S103, the determining the security attribute value of the first object includes:
and calculating the security attribute value of the first object based on the confidentiality score, the integrity score and the feasibility score of the first object.
Ways to determine the confidentiality score, integrity score, and feasibility score of the first object may include:
and determining the confidentiality score, the integrity score and the feasibility score of the first object based on a preset object classification table and the related information of the first object.
In one embodiment, a preset confidentiality score, a preset integrity score and a preset feasibility score corresponding to a first object (or asset) are extracted from a preset object classification table based on related information of the first object; and taking the extracted preset confidentiality score, preset integrity score and preset feasibility score as the confidentiality score, the integrity score and the feasibility score of the first object.
Calculating a security attribute value of the first object based on the confidentiality score, the integrity score and the feasibility score of the first object, which may specifically include: and determining the value of the first object (or the asset) according to the asset (or the object) importance assignment formula and combining the confidentiality score, the integrity score and the feasibility score of the first object.
The asset (or object) importance assignment formula can be expressed as follows:
wherein: v denotes asset value, x denotes confidentiality, y denotes integrity, and z denotes availability. The value 1.4 in the formula is a preset parameter according to an actual situation, and in the actual processing, the parameter may be adjusted, for example, may be set to 1.2, or 0.7, or 1.6, and the like, where the value is not limited, and the specific values are not exhaustive.
Finally, in S103, determining the importance level of the first object based on the security attribute value of the first object may include the following processing manners:
in the mode 1, the importance level of the first object is determined directly according to a preset importance division list and the security attribute value of the first object.
Wherein, the preset importance dividing list may include: at least one preset importance level and a safety attribute range corresponding to each importance level.
For example, the importance levels include 5, the security attribute value is the lowest level in the range of 0-1, the security attribute value is the lower level in the range of 1-2, the security attribute value is the medium level in the range of 2-3, the security attribute value is the higher level in the range of 3-4, and the security attribute value is the highest level in the range of 4-5.
Of course, there may be more setting manners for the preset importance dividing list, for example, 4 importance levels may also be set, and the range of the security attribute value may also be different from the above, but this embodiment does not exhaust the above.
Mode 2, extracting M objects having a safety attribute value from a plurality of objects of a target vehicle; m is an integer; and determining the importance level of the first object in the M objects according to the security attribute value of the first object and the security attribute values of the M objects.
This way, the list may not need to be preset like way 1, and the way of determining the importance level may be to compare with other objects.
The importance level of the first object may be determined according to a preset rule, for example, the preset rule may include: the security attribute values are ranked at the top 20% in M objects of the currently existing security attribute values, so that the importance level is highest, the ranking is 20% -50%, the importance level is medium, and the remaining importance level is low.
In this way, the importance levels of the objects at different times may be different.
Whether the mode 1 or the mode 2 is adopted is the result of combining the security assignment of the assets (or objects), the importance degree of the assets is divided (for example, the importance degree can be divided into 4 levels of high, medium and low), the higher the level is, the more important the assets are, and finally the importance level of the assets is determined.
Based on the above scheme, the present embodiment may further include: determining a safe handling manner of the first object based on the importance level of the first object of the target vehicle.
Wherein the security handling is related to the type of the first object and the importance level of the first object.
Since the first object may be any one of the objects in the target vehicle, the above process may be understood as taking different safeguards for different assets depending on the derived level of importance of the asset (or object). For the assets with high importance level, the all-round protection can be carried out from hardware, software, communication, system, application and data safety, for the assets with low importance level, the basic protection can be carried out, and the specific protection scheme can be further formulated in detail by combining the function of each asset and the result of threat analysis. The processing provided by the embodiment is particularly suitable for intelligent transportation or automatic driving scenes.
For example, it is recognized that Tbox is an asset with a higher importance level through asset identification, and in combination with the specific service function and the result of threat analysis, it is known that the Tbox's own system architecture (such as Linux image, serial port, diagnostic service program, etc.), the communication between Tbox and TSP cloud (such as OTA upgrade, remote control, network configuration file, key certificate, etc.), and the communication between Tbox and gateway (such as CAN network application program, key, etc.) are vulnerable, and the Tbox's own architecture, data storage, upgrade, etc. CAN be protected against the above vulnerable points, such as using ethernet firewall and newer operating system, using HSM hardware secure storage technology to protect the security of sensitive data such as private key, configuration data, etc., and using message authentication and message encryption mechanism to ensure the secure communication between Tbox, TSP and gateway, etc.
Therefore, by adopting the scheme, the type of the object can be identified and obtained based on the related information of the object, and the importance level of the object can be determined; therefore, the object of the target vehicle can be identified more accurately and more finely in the process of identifying the object of the target vehicle, the importance level of the object can be efficiently obtained, the efficiency and the accuracy of object identification are improved, and the subsequent safety processing and other work on the object in the target vehicle are more efficient and accurate.
An embodiment of the present application further provides an object recognition apparatus, as shown in fig. 2, the apparatus includes:
an acquisition module 21 for acquiring information about a first object of a target vehicle;
an identification module 22, configured to determine a category of the first object and determine a security attribute value of the first object based on the related information of the first object; and determining an importance level of the first object based on the security attribute value of the first object.
The identification module 22 is configured to determine a category of the first object based on a preset object classification table and the related information of the first object; wherein, the preset object classification table comprises: and the preset categories and the related information of at least one preset object corresponding to the preset categories.
The identification module 22 is configured to determine a confidentiality score, an integrity score, and a feasibility score of the first object based on a preset object classification table and related information of the first object; wherein, in the preset object classification table, the method further comprises: and the preset confidentiality score, the preset integrity score and the preset feasibility score corresponding to each preset object.
The identification module 22 is configured to calculate a security attribute value of the first object based on the confidentiality score, the integrity score, and the feasibility score of the first object.
On the basis of fig. 2, as shown in fig. 3, the apparatus further includes:
a processing module 23, configured to determine a safe handling manner of the first object based on the importance level of the first object of the target vehicle.
The object recognition device may be disposed in an electronic device, and the electronic device may be a terminal or a server in the cloud.
Therefore, by adopting the scheme, the type of the object can be identified and obtained based on the related information of the object, and the importance level of the object can be determined; therefore, the object of the target vehicle can be identified more accurately and more finely in the process of identifying the object of the target vehicle, the importance level of the object can be efficiently obtained, the efficiency and the accuracy of object identification are improved, and the subsequent safety processing and other work on the object in the target vehicle are more efficient and accurate.
According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.
As shown in fig. 4, the electronic device according to the object recognition method of the embodiment of the present application is a block diagram. The electronic device may be the aforementioned deployment device or proxy device. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 4, the electronic apparatus includes: one or more processors 801, memory 802, and interfaces for connecting the various components, including a high speed interface and a low speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). In fig. 4, a processor 801 is taken as an example.
The memory 802 is a non-transitory computer readable storage medium as provided herein. Wherein the memory stores instructions executable by at least one processor to cause the at least one processor to perform the object recognition methods provided herein. The non-transitory computer-readable storage medium of the present application stores computer instructions for causing a computer to perform the object recognition method provided herein.
The memory 802, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules (e.g., the acquisition module, the recognition module, the processing module shown in fig. 2 or 3) corresponding to the object recognition method in the embodiments of the present application. The processor 801 executes various functional applications of the server and data processing by running non-transitory software programs, instructions, and modules stored in the memory 802, that is, implements the object recognition method in the above-described method embodiments.
The memory 802 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the electronic device, and the like. Further, the memory 802 may include high speed random access memory and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 802 optionally includes memory located remotely from the processor 801, which may be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the object recognition method may further include: an input device 803 and an output device 804. The processor 801, the memory 802, the input device 803, and the output device 804 may be connected by a bus or other means, as exemplified by the bus connection in fig. 4.
The input device 803 may receive input numeric or character information and generate key signal inputs related to user settings and function controls of the electronic device, such as a touch screen, keypad, mouse, track pad, touch pad, pointer stick, one or more mouse buttons, track ball, joystick, or other input device. The output devices 804 may include a display device, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
By adopting the scheme, the category of the object can be identified and obtained based on the related information of the object, and the importance level of the object can be determined; therefore, the object of the target vehicle can be identified more accurately and more finely in the process of identifying the object of the target vehicle, the importance level of the object can be efficiently obtained, the efficiency and the accuracy of object identification are improved, and the subsequent safety processing and other work on the object in the target vehicle are more efficient and accurate.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (12)
1. A method of object recognition, the method comprising:
acquiring related information of a first object of a target vehicle;
determining a category of the first object and determining a security attribute value of the first object based on the related information of the first object;
determining an importance level of the first object based on the security attribute value of the first object.
2. The method of claim 1, wherein the determining the category of the first object based on the related information of the first object comprises:
determining the category of the first object based on a preset object classification table and the related information of the first object; wherein, the preset object classification table comprises: and the preset categories and the related information of at least one preset object corresponding to the preset categories.
3. The method of claim 2, wherein the method further comprises:
determining a confidentiality score, an integrity score and a feasibility score of the first object based on a preset object classification table and related information of the first object;
wherein, in the preset object classification table, the method further comprises: and the preset confidentiality score, the preset integrity score and the preset feasibility score corresponding to each preset object.
4. The method of claim 3, wherein the determining a security attribute value of the first object comprises:
and calculating the security attribute value of the first object based on the confidentiality score, the integrity score and the feasibility score of the first object.
5. The method of any of claims 1-4, wherein the method further comprises:
determining a safe handling manner of the first object based on the importance level of the first object of the target vehicle.
6. An object recognition apparatus, the apparatus comprising:
an acquisition module for acquiring information relating to a first object of a target vehicle;
the identification module is used for determining the category of the first object and determining the security attribute value of the first object based on the related information of the first object; and determining an importance level of the first object based on the security attribute value of the first object.
7. The apparatus of claim 6, wherein the identifying module is configured to determine the category of the first object based on a preset object classification table and related information of the first object; wherein, the preset object classification table comprises: and the preset categories and the related information of at least one preset object corresponding to the preset categories.
8. The apparatus of claim 7, wherein the identification module is configured to determine a confidentiality score, an integrity score, and a feasibility score of the first object based on a preset object classification table and information related to the first object; wherein, in the preset object classification table, the method further comprises: and the preset confidentiality score, the preset integrity score and the preset feasibility score corresponding to each preset object.
9. The apparatus of claim 8, wherein the identification module is configured to calculate a security attribute value for the first object based on the confidentiality score, the integrity score, and the feasibility score of the first object.
10. The apparatus of any of claims 6-9, wherein the apparatus further comprises:
a processing module to determine a safe handling of the first object based on an importance level of the first object of the target vehicle.
11. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.
12. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010607068.1A CN111767585A (en) | 2020-06-29 | 2020-06-29 | Object identification method and device, electronic equipment and storage medium |
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| CN114489897A (en) * | 2022-01-21 | 2022-05-13 | 北京字跳网络技术有限公司 | Object processing method, device, terminal equipment and medium |
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