Vulnerability mining method, system and device for cognitive map and storage medium
By constructing prior knowledge graphs and attack vector graphs, and combining natural language processing and information extraction techniques, the barriers between traditional vulnerability scanning and manual discovery are broken, achieving intelligent vulnerability detection and improving the accuracy and efficiency of vulnerability discovery.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-10-12
- Publication Date
- 2026-04-14
AI Technical Summary
In existing technologies, traditional vulnerability scanning and manual discovery are hampered by limitations imposed by rules or varying skill levels among personnel, resulting in the inability to achieve comprehensive and accurate vulnerability detection.
A prior knowledge graph is constructed, and publicly available vulnerability data and human experience data in cyberspace are used to process the data through natural language processing and information extraction techniques to form structured data. This data is then combined with attack vector graphs for intelligent vulnerability mining, and deep detection and script execution are performed using preset rules.
It achieves comprehensive and accurate vulnerability detection, reduces the false negative rate, improves the intelligence level of vulnerability discovery, and approaches the effect of manual penetration testing.
Smart Images
Figure CN121864335A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of vulnerability discovery technology, and in particular to a vulnerability discovery method, system, device and storage medium for cognitive mapping. Background Technology
[0002] Cybersecurity vulnerabilities can generally be understood as flaws in the specific implementation of hardware, software, and protocols, or in system security policies, that allow attackers to access or damage the system without authorization. A more colloquial definition is any factor existing in a computer network system that could potentially harm the system's components and data. For example, logical errors in Intel Pentium chips, programming errors in early versions of Sendmail, weaknesses in the authentication methods of the NFS protocol, and misconfigurations when Unix system administrators set up anonymous FTP services could all be exploited by attackers to threaten system security. These can all be considered security vulnerabilities in the system.
[0003] Currently, the internet is gradually shedding its initial attributes as a tool, channel, and platform, transforming into an exceptionally complex cyberspace. The discovery and exploitation of cybersecurity vulnerabilities, and the acquisition of different types of vulnerabilities, imply the acquisition of varying levels of system control and the seizure of risky data. Vulnerability discovery primarily relies on two methods: traditional vulnerability scanning and manual discovery. Both methods have significant barriers to entry. Traditional vulnerability scanning has limited functionality and is constrained by rules or the size of vulnerability databases, while manual vulnerability discovery suffers from inconsistent skill levels among personnel.
[0004] In other words, existing technologies, such as traditional vulnerability scanning and manual discovery, have significant barriers, which are limited by rules or varying levels of personnel skill, resulting in the inability to achieve comprehensive and accurate vulnerability detection. Summary of the Invention
[0005] To overcome, to some extent, the significant barriers in traditional vulnerability scanning and manual discovery in related technologies, which are limited by rules or varying levels of personnel skill, resulting in the inability to achieve comprehensive and accurate vulnerability detection, this application provides a vulnerability discovery method, system, device, and storage medium based on cognitive graphs.
[0006] The proposed solution is as follows:
[0007] Firstly, this application provides a method for vulnerability discovery in cognitive graphs, the method comprising:
[0008] Collect publicly available vulnerability data in the cybersecurity field;
[0009] A priori knowledge graph is constructed using publicly available vulnerability data in the cyberspace security field and pre-obtained human experience data. The priori knowledge graph is used to collect and store publicly available vulnerability data and human experience data in the cyberspace security field. Among them, the human experience data includes: experience data transformed through steps in the process of human vulnerability discovery.
[0010] The prior knowledge graph is used to help construct an attack vector graph; wherein, the attack vector graph is used for deep detection of vulnerability information;
[0011] The attack vector map is used for intelligent vulnerability discovery.
[0012] Furthermore, the construction of a priori knowledge graph using publicly available vulnerability data in the cyberspace security field and pre-obtained human experience data includes:
[0013] Natural language processing and information extraction techniques are used to process publicly available vulnerability data and human experience data in the cyberspace security field to obtain structured data; the structured data includes execution action information and attribute and entity information corresponding to the execution action information;
[0014] The structured data is stored in a pre-obtained knowledge graph to obtain a prior knowledge graph that stores the attributes, entities, and relationships within the structured data.
[0015] Furthermore, the method also includes:
[0016] The execution action information in the structured data is converted into execution commands or execution scripts, and the execution commands or execution scripts are stored.
[0017] Furthermore, the step of utilizing the prior knowledge graph to help construct the attack vector graph includes:
[0018] Using a first preset rule, a processing logic is constructed between the prior knowledge graph and the relevant detection tools; wherein, the processing logic includes: executing the relevant detection tools corresponding to different monitoring points;
[0019] Using a second preset rule, a processing logic is constructed between the returned information and the execution command or execution script; wherein, the returned information includes: the scan results obtained in advance or the return information of the monitoring point, and the processing logic includes: using the returned information to obtain the next execution action;
[0020] The next step is used to obtain the execution command or execution script.
[0021] Furthermore, the intelligent vulnerability discovery using the attack vector graph includes:
[0022] Using prior knowledge graphs, at least one monitoring point is found through penetration testing logic;
[0023] Using at least one monitoring point obtained, the corresponding detection tool is invoked according to the first preset rule to obtain the feedback information detected by the detection tool;
[0024] Determine whether the returned information contains vulnerability detection data; if so, end the detection process.
[0025] If it is not included, the second preset rule is used to obtain the next action to be performed;
[0026] The next execution action is used to obtain the execution command or execution script;
[0027] Execute the execution command or script to obtain feedback information until the feedback information detects vulnerability detection data or reaches the preset first termination condition; otherwise, continue detection using the second preset rule.
[0028] The first termination condition includes: a pre-set time or number of executions.
[0029] Secondly, this application provides a vulnerability discovery system for cognitive graphs, the system comprising:
[0030] The data collection module is used to collect publicly available vulnerability data in the cyberspace security field.
[0031] The first data processing module is used to construct a priori knowledge graph by utilizing publicly available vulnerability data in the security field of cyberspace and pre-obtained human experience data; the priori knowledge graph is used to collect and store publicly available vulnerability data and human experience data in the security field of cyberspace; wherein, the human experience data includes: experience data transformed through steps in the process of human vulnerability discovery.
[0032] The second data processing module is used to utilize the prior knowledge graph to help construct an attack vector graph; wherein, the attack vector graph is used for deep detection of vulnerability information;
[0033] The third data processing module is used to perform intelligent vulnerability mining using the attack vector graph.
[0034] Thirdly, this application provides a vulnerability discovery device for cognitive graphs, the device comprising:
[0035] Memory, on which executable programs are stored;
[0036] A processor for executing the executable program in the memory to implement the steps of any of the methods described above.
[0037] Fourthly, this application provides a computer-readable storage medium storing computer instructions for causing a computer to perform the steps of any of the methods described above.
[0038] The technical solution provided in this application may include the following beneficial effects:
[0039] This application includes: collecting publicly available vulnerability data and human experience data in the security field within cyberspace to construct a priori knowledge graph; the priori knowledge graph is used to collect and store publicly available vulnerability data in the security field within cyberspace; the priori knowledge graph is used to help construct an attack vector graph; wherein the attack vector graph is used for deep detection of vulnerability information; and the attack vector graph is used for intelligent vulnerability mining. This application, by utilizing a priori knowledge graph, transforms successful experiences into operable processes using technical means, and achieves precise problem-solving and action execution through the attack vector graph, thus helping to achieve comprehensive and accurate vulnerability mining.
[0040] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description
[0041] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0042] Figure 1 This is a schematic diagram of a vulnerability discovery method for cognitive graphs provided in one embodiment of this application;
[0043] Figure 2 This is a schematic diagram of the components of a cognitive graph vulnerability discovery system provided in another embodiment of this application;
[0044] Figure 3 This is a schematic diagram of a vulnerability discovery device for cognitive mapping, provided in another embodiment of this application. Detailed Implementation
[0045] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.
[0046] Example
[0047] Please see Figure 1 , Figure 1 This is a schematic flowchart of a vulnerability discovery method for cognitive graphs according to an embodiment of this application. The method includes:
[0048] S1. Collect publicly available vulnerability data in the security field within cyberspace;
[0049] S2. Construct a priori knowledge graph using publicly available vulnerability data in the cyberspace security field and pre-obtained human experience data; the priori knowledge graph is used to collect and store publicly available vulnerability data and human experience data in the cyberspace security field; wherein, human experience data includes: experience data transformed through steps in the process of human vulnerability discovery;
[0050] S3. Utilize the prior knowledge graph to help construct an attack vector graph; wherein, the attack vector graph is used for deep detection of vulnerability information;
[0051] S4. Utilize the attack vector graph to perform intelligent vulnerability mining. In one embodiment, as described in step S2, constructing a priori knowledge graph using publicly available vulnerability data in the cyberspace security field and pre-obtained human experience data includes:
[0052] Natural language processing and information extraction techniques are used to process publicly available vulnerability data and human experience data in the cyberspace security field to obtain structured data; the structured data includes execution action information and attribute and entity information corresponding to the execution action information;
[0053] The structured data is stored in a pre-obtained knowledge graph to obtain a prior knowledge graph that stores the attributes, entities, and relationships within the structured data.
[0054] Among them, human experience data can be understood as, for example, when performing a password brute-force attack on a login page, based on the returned information, it can be determined whether the account or the password can be brute-forced. This is the experience data transformed through the steps in the process of human vulnerability discovery.
[0055] In practice, natural language processing is used to tag each word in publicly available vulnerability data in the cyberspace security field with part-of-speech tags, such as nouns, verbs, adjectives, etc.
[0056] It identifies specific entities such as names of people, places, and organizations in natural language text; it also identifies predicates and their corresponding semantic roles, such as subject, object, time, and location. Furthermore, it resolves ambiguities such as polysemy and homographs in natural language text by utilizing contextual information.
[0057] Information extraction techniques are used to structure publicly disclosed vulnerability data in the cyberspace security field after natural language processing, transforming it into a tabular-like organizational format. Specifically, pre-defined information to be extracted, including but not limited to attribute information and action information, is extracted from the publicly disclosed vulnerability data to obtain a dataset with a fixed format.
[0058] In one embodiment, a knowledge graph, known in the library and information science community as knowledge domain visualization or knowledge domain mapping map, is a series of different graphics that display the development process and structural relationships of knowledge. It uses visualization technology to describe knowledge resources and their carriers, and to mine, analyze, construct, draw and display knowledge and the interrelationships between them.
[0059] Knowledge graphs describe concepts, entities, and relationships in the objective world in a structured form, expressing internet information in a way that more closely resembles human cognition. They provide a better ability to organize, manage, and understand the massive amounts of information on the internet. Knowledge graphs have revitalized internet semantic search and demonstrated powerful capabilities in intelligent question answering, becoming the infrastructure for knowledge-driven intelligent applications on the internet.
[0060] A knowledge graph is essentially a semantic network, composed of nodes and edges. Nodes represent entities, concepts, or attributes, while edges represent the relationships between them. The main types of nodes found in a knowledge graph include:
[0061] An entity refers to a concrete thing in the objective world, such as a person, a city, a plant, or a commodity. Everything in the world is composed of concrete things; this refers to entities. Entities are the most basic elements in a knowledge graph, and different entities have different relationships.
[0062] Concept: refers to the conceptual representation of objective things formed by people in the process of understanding the world. It is a collection of entities with the same characteristics, such as countries, cities, people, etc.
[0063] Attributes: Features used to distinguish entities, used to describe the intrinsic information of things, such as the area and population of China.
[0064] In specific implementation, the structured data is stored in a pre-obtained knowledge graph to obtain a prior knowledge graph that stores the attributes, entities, and relationships within the structured data.
[0065] In practical implementation, for example, a script might be written to detect host liveness. This script opens the computer, enters the Windows command prompt (cmd), and then pings the IP address to retrieve the command prompt's return information. Based on this information, the host's liveness status is determined. This information is then converted into an executable script to automate the above steps. The script is stored according to an entity-attribute-relationship structure. The entities are: the detected host and the Windows command prompt. The relationships are: the connection between the detected host and the computer, the call relationship between the computer and the Windows command prompt, and the communication relationship between the Windows command prompt and the IP address. The attributes are: liveness status, IP address, and return information. Based on the relationships between entities and attributes, fragments of knowledge are connected step-by-step to form a complete knowledge graph. This transforms the execution action information in the structured data into execution commands or scripts, which are then stored.
[0066] In one embodiment, as described in step S2, a priori knowledge graph is used to help construct an attack vector graph; wherein the attack vector graph is used for deep detection of vulnerability information, including:
[0067] Using a first preset rule, a processing logic is constructed between the prior knowledge graph and related detection tools; wherein, the processing logic includes: executing the relevant detection tools corresponding to different monitoring points; wherein, the first preset rule is used to construct and store the processing logic between the prior knowledge graph and related detection tools.
[0068] In practice, depending on the monitoring point, the corresponding detection tools are executed. The monitoring point can be understood as describing the potential problem areas for vulnerability detection. For example, if SQL injection is found in a URL, the injected URL is the monitoring point. Based on prior knowledge, different tools (sqlmap, BSQLHacker, Safe3 SQL Injector, etc.) are called for the injection. Different tools are used depending on the database. The specific commands are executed by the tool based on the existing injection item, such as blind injection with BSQL Hacker or searching the DBMS database with SQLMAP.
[0069] Using a second preset rule, a processing logic is constructed between the returned information and the execution command or execution script; wherein, the returned information includes: the scan results obtained in advance or the return information of the monitoring point, and the processing logic includes: using the returned information to obtain the next execution action;
[0070] The next step is used to obtain the execution command or execution script.
[0071] In practice, the next step is executed based on the returned information and prior knowledge. For example, when submitting vulnerability detection, if the webpage returns information indicating that the vulnerability has been blocked by a WAF (Web Application Firewall), the WAF interception information corresponds to initiating WAF bypass in the prior knowledge graph. When submitting vulnerability detection data, if the returned WAF interception information proves that the bypass has failed, or if a 403 error page or other code information is displayed, different WAF bypass or 403 vulnerability bypass detection is performed based on the correlation information of the prior knowledge graph.
[0072] The second preset rule is used to construct and store the processing logic between the returned information and the execution command or execution script.
[0073] In practical implementation, an attack vector graph is constructed, encompassing a vulnerability information database, a tool library, and a tool knowledge base. Relationships are established between these databases according to the sequence of reconnaissance, information gathering, asset identification, orchestration and scheduling, testing, escape detection, vulnerability exploitation, maintenance and expansion, and achieving the target. In practice, these relationships are established by associating keywords extracted from the prior knowledge graph, such as vulnerability descriptions, vulnerability tools, tool usage methods, and vulnerability exploitation methods. Relationships are confirmed through vulnerability numbers or vulnerability content. For example, regarding SQL injection vulnerabilities, if an online store has an SQL injection vulnerability, SQLmap is an SQL injection vulnerability detection tool. A series of commands are used to verify the SQL injection vulnerability. The tool knowledge base describes the scripted execution and verification capabilities of all tool commands. To detect an SQL injection vulnerability in the online store, "sqlmap.py -u ur l" needs to be executed based on the tool knowledge base content, and then further detection is performed based on the detection results.
[0074] In practice, the association is established based on vulnerability information. The method is to establish the association based on the scope of impact and vulnerability description of the vulnerability. Based on the vulnerability information, multiple vulnerability tools can be matched for vulnerability detection. Vulnerability detection corresponds to the tools executing different commands to perform detection. In addition, multiple tools can be used to detect the same vulnerability based on the knowledge base.
[0075] In specific implementation, as described in step S3, the intelligent vulnerability discovery using the attack vector graph includes:
[0076] Using prior knowledge graphs, at least one monitoring point is found through penetration testing logic;
[0077] Using at least one monitoring point obtained, the corresponding detection tool is invoked according to the first preset rule to obtain the feedback information detected by the detection tool;
[0078] Determine whether the returned information contains vulnerability detection data; if so, end the detection process.
[0079] If it is not included, the second preset rule is used to obtain the next action to be performed;
[0080] The next execution action is used to obtain the execution command or execution script;
[0081] Execute the execution command or script to obtain feedback information until the feedback information detects vulnerability detection data or reaches the preset first termination condition; otherwise, continue detection using the second preset rule.
[0082] The first termination condition includes: a pre-set time or number of executions.
[0083] In practical implementation, this application forms an attack vector map with practical application, which is more intelligent than traditional vulnerability detection and closer to the level of manual penetration testing; it transforms prior knowledge into automated scripts that can be executed automatically, and combines tool libraries and tool knowledge bases to form automated script compilation or command execution; and it reduces the false negative rate during vulnerability discovery.
[0084] Please see Figure 2 , Figure 2 This is a schematic diagram of a vulnerability discovery system for cognitive graphs provided in another embodiment of this application. The system includes:
[0085] The data collection module 21 is used to collect publicly available vulnerability data in the security field of cyberspace;
[0086] The first data processing module 22 is used to construct a priori knowledge graph by utilizing publicly available vulnerability data in the security field of cyberspace and pre-obtained human experience data; the priori knowledge graph is used to collect and store publicly available vulnerability data and human experience data in the security field of cyberspace; wherein, the human experience data includes: experience data transformed through steps in the process of human vulnerability discovery;
[0087] The second data processing module 23 is used to utilize the prior knowledge graph to help construct an attack vector graph; wherein the attack vector graph is used for deep detection of vulnerability information;
[0088] The third data processing module 24 is used to perform intelligent vulnerability mining using the attack vector map.
[0089] Please see Figure 3 , Figure 3 This is a schematic diagram of a vulnerability discovery device for cognitive mapping, provided in another embodiment of this application. The device includes:
[0090] Memory 31, on which an executable program is stored;
[0091] Processor 32 is configured to execute the executable program in the memory 31 to implement the steps of any of the methods described above.
[0092] Furthermore, this application provides a computer-readable storage medium storing computer instructions for causing a computer to perform the steps of any of the methods described above. The storage medium may be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium may also include combinations of the above types of memory.
[0093] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.
[0094] It should be noted that in the description of this application, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this application, unless otherwise stated, "a plurality of" means at least two.
[0095] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the function involved, as will be understood by those skilled in the art to which embodiments of this application pertain.
[0096] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.
[0097] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.
[0098] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.
[0099] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.
[0100] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.
[0101] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.
Claims
1. A method for vulnerability discovery in cognitive graphs, characterized in that, The method includes: Collect publicly available vulnerability data in the cybersecurity field; A priori knowledge graph is constructed using publicly available vulnerability data in the cyberspace security field and pre-obtained human experience data. The priori knowledge graph is used to collect and store publicly available vulnerability data and human experience data in the cyberspace security field. Among them, the human experience data includes: experience data transformed through steps in the process of human vulnerability discovery. The prior knowledge graph is used to help construct an attack vector graph; wherein, the attack vector graph is used for deep detection of vulnerability information; The attack vector map is used for intelligent vulnerability discovery.
2. The method according to claim 1, characterized in that, The method of constructing a priori knowledge graph by utilizing publicly available vulnerability data in the cyberspace security field and pre-obtained human experience data includes: Natural language processing and information extraction techniques are used to process publicly available vulnerability data and human experience data in the cyberspace security field to obtain structured data; the structured data includes execution action information and attribute and entity information corresponding to the execution action information; The structured data is stored in a pre-obtained knowledge graph to obtain a prior knowledge graph that stores the attributes, entities, and relationships within the structured data.
3. The method according to claim 2, characterized in that, The method further includes: The execution action information in the structured data is converted into execution commands or execution scripts, and the execution commands or execution scripts are stored.
4. The method according to claim 1, characterized in that, The process of utilizing the prior knowledge graph to help construct the attack vector graph includes: Using a first preset rule, a processing logic is constructed between the prior knowledge graph and the relevant detection tools; wherein, the processing logic includes: executing the relevant detection tools corresponding to different monitoring points; Using a second preset rule, a processing logic is constructed between the returned information and the execution command or execution script; wherein, the returned information includes: the scan results obtained in advance or the return information of the monitoring point, and the processing logic includes: using the returned information to obtain the next execution action; The next step is used to obtain the execution command or execution script.
5. The method according to claim 1, characterized in that, The intelligent vulnerability discovery using the attack vector graph includes: Using prior knowledge graphs, at least one monitoring point is found through penetration testing logic; Using at least one monitoring point obtained, the corresponding detection tool is invoked according to the first preset rule to obtain the feedback information detected by the detection tool; Determine whether the returned information contains vulnerability detection data; if so, end the detection process. If it is not included, the second preset rule is used to obtain the next action to be performed; The next execution action is used to obtain the execution command or execution script; Execute the execution command or script to obtain feedback information until the feedback information detects vulnerability detection data or reaches the preset first termination condition; otherwise, continue detection using the second preset rule. The first termination condition includes: a pre-set time or number of executions.
6. A vulnerability discovery system for cognitive graphs, characterized in that, The system includes: The data collection module is used to collect publicly available vulnerability data in the cyberspace security field. The first data processing module is used to construct a priori knowledge graph by utilizing publicly available vulnerability data in the security field of cyberspace and pre-obtained human experience data; the priori knowledge graph is used to collect and store publicly available vulnerability data and human experience data in the security field of cyberspace; wherein, the human experience data includes: experience data transformed through steps in the process of human vulnerability discovery; The second data processing module is used to utilize the prior knowledge graph to help construct an attack vector graph; wherein, the attack vector graph is used for deep detection of vulnerability information; The third data processing module is used to perform intelligent vulnerability mining using the attack vector graph.
7. A vulnerability discovery device for cognitive graphs, characterized in that, The device includes: Memory, on which executable programs are stored; A processor for executing the executable program in the memory to implement the steps of the method according to any one of claims 1-5.
8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to perform the steps of the method according to any one of claims 1-5.