A password evaluation work instruction intelligent generation method and device based on fingerprint cross comparison

Abstract

The present application relates to the technical field of password evaluation, and aims to provide a password evaluation job instruction manual intelligent generation method based on fingerprint cross comparison. Specifically, it includes the following steps: (1) obtaining the asset fingerprint of the system to be evaluated based on offline collection and lossless version sniffing; (2) cross matching the asset fingerprint with the keyword of the compatible device information library, and feeding back prompt information if the matching fails, and adjusting the format or supplementing the knowledge base according to the prompt; (3) accurate matching, calling the evaluation implementation content knowledge base to establish a temporary library; (4) based on the index relationship between the temporary library and the report framework, the generation of job instruction manuals for different devices or levels is realized. The present application aims to use automatic technical measures to realize the standardized output of password evaluation job instruction manuals for a large number of objects, improve the accuracy of the job instruction manual, reduce the consumption rate of manual resources, improve the update iteration efficiency, avoid subjective intervention of manual version replacement, and promote the standardization, reliability and convenience of password evaluation work.

Description

TECHNICAL FIELD

[0001] The application relates to a fingerprint cross-comparison-based password evaluation work instruction intelligent generation method and device, and belongs to the technical field of password evaluation. BACKGROUND

[0002] Password technology is the core technology and basic support for network and information security. The Password Law and relevant laws and regulations clearly stipulate the legal requirements for commercial password application and security evaluation, and a basic standard for guiding commercial password application and security evaluation, namely, Information Security Technology Information System Password Application Basic Requirements (GB / T 39786-2021), will be implemented on October 1, 2021. Therefore, the normalization of commercial password application and security evaluation is of great significance to the maintenance of national network and information security.

[0003] At present, the security evaluation of password application is in its infancy, and there is a lack of supporting evaluation tools, texts and other materials. The existing Password Application Security Evaluation Evaluation Work Instruction (Draft for Comments) has the following problems: the applicable scene is single, the evaluation method is general, the operation method is not detailed to the individual index, and the standardization system has not been formed in terms of password collection judgment method, key calculation node evaluation index selection, high-risk judgment guide, targeted security suggestions and the like, which cannot meet the needs of the current diversified technological innovation era, thereby weakening the substantive effect of the on-site evaluation.

[0004] In view of the above situation, most units adopt the mode of fixed template or manual preparation of on-site work instruction to promote the implementation of password application security evaluation; but the current mode still has some shortcomings: (1) the work instruction is general, and the index differentiation selection is not realized, which is not conducive to precise evaluation and the embodiment of different characteristics; (2) manual preparation is influenced by subjective factors, and there are shortcomings in the cross combination of multiple elements, which is not conducive to standardized operation and process implementation. SUMMARY

[0005] The purpose of the present application is to overcome the shortcomings of the prior art, and to provide a fingerprint cross-comparison-based password evaluation work instruction intelligent generation method and device, which can realize the automatic generation of targeted work instruction based on the password product use situation of the evaluated system, and improve the efficiency and accuracy of on-site evaluation.

[0006] To achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0007] In the first aspect, the present application provides a fingerprint cross-comparison-based password evaluation work instruction intelligent generation method, which comprises the following steps:

[0008] The asset fingerprint information is collected in a multiple combination mode;

[0009] According to the asset fingerprint information cross-matching compatible device information library;

[0010] Based on the matching results, call the knowledge base to establish a temporary library;

[0011] Constructing the temporary library and the reporting framework index relationship, realizing the generation of the job instruction book.

[0012] Further, the multiple combination mode includes offline collection and lossless version sniffing to obtain the asset fingerprint information of the evaluated system;

[0013] The offline collection includes: using collection scripts or programs or manual checking methods to obtain the asset fingerprint information;

[0014] The lossless version sniffing includes: using passive methods to collect the asset fingerprint information of the evaluated system, reducing the load of the evaluated system;

[0015] The asset fingerprint information includes asset type, device model, system version, kernel version, application system architecture, and development language;

[0016] The asset type includes access control system, video monitoring system, network and communication device, core computing device, and application system.

[0017] Further, according to the asset fingerprint information cross-matching compatible device information library, including:

[0018] According to the asset fingerprint, perform fingerprint information conversion, cross-match the compatible device information library keywords according to the conversion results, and if the matching fails, prompt error nodes and information or prompt to supplement the knowledge base information, so as to further correct and optimize the data and further secondary comparison, until accurate matching, and call the knowledge base data as needed.

[0019] The fingerprint information conversion includes: Chinese conversion to alphabet full spelling, and filtering spaces in the fingerprint information;

[0020] The information library keywords include asset type, device model, system version, kernel version, application system architecture, and development language.

[0021] Further, the cross-matching includes preferentially matching the asset type, and cross-comparing according to the type, including the following steps:

[0022] When the asset type is an access control system or a video monitoring system, further match the device model;

[0023] When the asset type is a network and communication device, further match the device model, and according to the device model, call out the system version resource pool for matching;

[0024] When the asset type is a core computing device, the system version is further matched, and the kernel version resource pool is retrieved for matching based on the system version.

[0025] When the asset type is an application system, the system architecture and development language are matched sequentially.

[0026] Furthermore, the supplementary knowledge base information includes: optimizing and updating the knowledge base by technical personnel based on the acquired asset fingerprint information;

[0027] The knowledge base includes an instance library created in a relational database and files stored in a specified path;

[0028] The knowledge base includes:

[0029] A cryptographic evaluation metrics library is used to store metrics covering multiple levels of requirements;

[0030] A compatible device information database is used to store database keywords;

[0031] The assessment implementation content library is used to store indicator assessment content that matches different asset fingerprints;

[0032] The evaluation method example library stores detailed evaluation methods for different indicator evaluation contents;

[0033] The expected results library is used to store the expected results that meet different asset fingerprint evaluation indicators;

[0034] The report framework stores the specific path to the report master and the index relationship between the report framework and the knowledge base.

[0035] Furthermore, a temporary library is built based on the matching results and the knowledge base, including:

[0036] Knowledge base data is extracted sequentially based on cross-matching logic, and the extracted data is stored in a temporary database for previewing and building the report framework index.

[0037] The steps involved in extracting knowledge base data according to cross-matching logic are as follows:

[0038] Indicators are selected from the cryptographic evaluation indicator library based on asset type;

[0039] For access control systems and video surveillance systems, data is extracted from the evaluation implementation content library, evaluation method example library, and expected result library based on the equipment model and the selected indicators.

[0040] For network and communication equipment, the system version is further specified according to the equipment model, and data is extracted from the evaluation implementation content library, evaluation method example library, and expected result library for the selected indicators.

[0041] For the core computing device, according to the system version, further confirm the kernel version, and extract data from the evaluation implementation content library, the evaluation method example library and the expected result library according to the selected index;

[0042] For the application system, data is extracted from the evaluation implementation content library, the evaluation method example library and the expected result library according to the system architecture, the development language and the selected index.

[0043] Further, the temporary library and the report framework index relationship are constructed to realize the generation of the job instruction book, including:

[0044] The temporary library and the report framework index relationship are constructed to realize the generation of the job instruction book for different devices or levels.

[0045] Further, the report framework is a preset doc format report master, and a differentiated index calling relationship is established between different asset fingerprints and the report master;

[0046] The index relationship is to establish a relationship between the fields of the temporary library and the report master, and to establish an index relationship by using keys and variables in the Word template.

[0047] In a second aspect, the present application provides a password evaluation job instruction book intelligent generation device based on fingerprint cross comparison, which comprises:

[0048] The acquisition module is used for acquiring asset fingerprint information in a multiple combination mode;

[0049] The matching module is used for cross matching the compatible device information library according to the asset fingerprint information;

[0050] The temporary library module is used for calling the knowledge base to establish a temporary library based on the matching result;

[0051] The generation module is used for constructing the temporary library and the report framework index relationship to realize the generation of the job instruction book.

[0052] In a third aspect, the present application provides a password evaluation job instruction book intelligent generation device based on fingerprint cross comparison, which comprises a processor and a storage medium;

[0053] The storage medium is used for storing instructions;

[0054] The processor is used for operating according to the instructions to execute the steps of the method according to the first aspect.

[0055] Compared with the prior art, the present application has the following beneficial effects:

[0056] The present application uses a multiple combination mode fingerprint acquisition means including a lossless version sniffing technology to realize efficient, lossless and comprehensive fingerprint information acquisition.

[0057] The present application is based on fingerprint features, a mass knowledge base, and through fingerprint cross comparison, can realize the rapid generation of multiple combinations of large batch cryptanalysis operation instruction books in the field fingerprint information collection stage, reduce the man-hour consumption of the pre-cryptanalysis preparation work, and improve the cryptanalysis project capacity;

[0058] The present application can realize the centralized optimization and upgrading and distribution of the mass knowledge base according to the changes of domestic and foreign technology development and password evaluation requirements, and avoid the interference of uncontrollable factors such as personal technical ability and responsibility attitude;

[0059] The present application can ultimately improve the pertinence, accuracy and frontiers of cryptanalysis operation instruction books, further promote the stable development of cryptanalysis work, and effectively build a solid and reliable network security password guarantee. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 It is the architecture diagram of the present application.

[0061] Figure 2 It is the disposal flow chart of the present application. DETAILED DESCRIPTION

[0062] The present application will be further described below in combination with the drawings. The following examples are only used to more clearly illustrate the technical solutions of the present application, and cannot limit the protection scope of the present application.

[0063] Example one:

[0064] As shown in the figure, a password evaluation operation instruction book intelligent generation method based on fingerprint cross comparison includes the following steps: Figure 1 Step one: based on offline collection and lossless version sniffing, etc., the asset fingerprint of the system to be evaluated is obtained;

[0065] Step two: according to the cross matching of asset fingerprint and compatible device information library keywords, feedback prompt information is given if the matching fails, and format adjustment or knowledge base supplement is taken according to the prompt;

[0066] Step three: accurate matching, calling the evaluation implementation content and other knowledge base to establish a temporary library;

[0067] Step four: based on the index relationship between the temporary library and the report framework, the generation of different device or level operation instruction books is realized.

[0068] Referring to

[0069] , the present application provides a password evaluation operation instruction book intelligent generation method based on fingerprint cross comparison, which includes: Figure 2

[0070] ​(1) Asset collection; asset collection includes offline collection and lossless version sniffing;

[0071] Further, the offline collection refers to acquiring asset fingerprint information using a collection script / program or manual checking; the lossless version sniffing includes collecting asset fingerprints of the evaluated system in a passive manner to reduce the load of the evaluated system; the asset fingerprints include asset type, device model, system version, kernel version, application system architecture, and development language; the asset type includes access control system, video monitoring system, network and communication device, core computing device, and application system. The present application uses multiple combination methods including lossless version sniffing technology to realize efficient, lossless, and comprehensive fingerprint information collection.

[0072] (2) Information conversion; according to the asset fingerprint, the fingerprint information is converted. That is, the Chinese of the asset fingerprint information is converted into alphabet full spelling, and the spaces in the fingerprint information are filtered.

[0073] (3) Matching keywords; according to the conversion result, cross-matching compatible device information library keywords is performed, and if the matching fails, an error node and information are prompted or the knowledge base information to be supplemented is prompted, so as to further correct and optimize the data and further secondary comparison until accurate matching, and the knowledge base data is called as needed. Based on the fingerprint characteristics and the massive knowledge base, the present application can realize the rapid generation of a large number of secret evaluation operation instruction books through fingerprint cross comparison in the field fingerprint information collection stage, reduce the man-hour consumption of the secret evaluation preparation work, and improve the secret evaluation project capacity.

[0074] Specifically, the information library keywords include asset type, device model, system version, kernel version, application system architecture, and development language, wherein except for the asset type, the others are English characters, such as "huawei" represented as "huawei".

[0075] The asset type includes access control system, video monitoring system, network and communication device, core computing device, and application system.

[0076] The cross-matching includes preferentially matching the asset type, and cross-comparing according to the type. When the asset type is access control system or video monitoring system, the device model is further matched. When the asset type is network and communication device, the device model is further matched, and according to the device model, the system version resource pool is called out for matching. When the asset type is core computing device, the system version is further matched, and according to the system version, the kernel version resource pool is called out for matching. When the asset type is application system, the system architecture and the development language are matched in sequence.

[0077] The knowledge base supplement includes optimizing and updating the knowledge base by the technical personnel according to the acquired asset fingerprint information.

[0078] The knowledge base includes an instance base created in a relational database and files stored in a specified path.

[0079] (4) The knowledge base data is extracted in sequence according to cross-matching logic, and the extracted data is stored in a temporary base for preview and construction of a report framework index.

[0080] The sequential extraction includes first selecting indicators from a password evaluation indicator base according to asset types; for access control systems and video monitoring systems, data is extracted from an evaluation implementation content base, an evaluation method example base, and an expected result base according to device models and the selected indicators; for network devices and communication devices, the system version is further specified according to the device models, and data is extracted from the evaluation implementation content base, the evaluation method example base, and the expected result base according to the selected indicators; for core computing devices, the kernel version is further confirmed according to the system version, and data is extracted from the evaluation implementation content base, the evaluation method example base, and the expected result base according to the selected indicators; for application systems, data is extracted from the evaluation implementation content base, the evaluation method example base, and the expected result base according to the system architecture, the development language, and the selected indicators.

[0081] (5) A report framework is called to utilize the established temporary base to construct an index relationship between the temporary base and the report framework, and to realize generation of different device or level job instruction sheets.

[0082] The report framework is a preset doc format report master, and a differentiated index calling relationship is established between different asset fingerprints and the report master.

[0083] The index relationship is to establish a relationship between fields of the temporary base and ${valuename} in the report master, such as a variable ${valuename} in a word template, a Map collection with ${valuename} as a key and value as a value is built for the variable, and an index relationship is established by using the key and the variable in the word template.

[0084] The database includes:

[0085] A password evaluation indicator base for storing indicators covering multiple levels of requirements;

[0086] A compatible device information base for storing information base keywords;

[0087] An evaluation implementation content base for storing indicator evaluation contents matching different asset fingerprints;

[0088] An evaluation method example base for storing detailed evaluation methods corresponding to different indicator evaluation contents;

[0089] An expected result base for storing expected results satisfying different asset fingerprint evaluation indicators;

[0090] A report framework is used to store the specific path of the report master and the index relationship between the report framework and the knowledge base.

[0091] The present application realizes the multiple combination of a large number of secret evaluation operation guides based on fingerprint characteristics and a mass knowledge base through fingerprint cross comparison, reduces the man-hour consumption of the pre-preparation work of secret evaluation, synchronously improves the pertinence and accuracy of the guide, and further promotes the stable development of secret evaluation work.

[0092] The present application can realize the centralized optimization upgrade and distribution of the mass knowledge base according to the changes of domestic and foreign technology development and secret evaluation requirements, and avoid the interference of uncontrollable factors such as personal technical ability and responsibility attitude.

[0093] The present application can finally improve the pertinence, accuracy and frontiers of the secret evaluation operation guide, further promote the stable development of secret evaluation work, and effectively build a solid and reliable network security and password guarantee.

[0094] Embodiment two:

[0095] The present embodiment provides a secret evaluation operation guide intelligent generation device based on fingerprint cross comparison, which comprises:

[0096] The acquisition module is used to acquire asset fingerprint information in a multiple combination mode;

[0097] The matching module is used to cross-match the compatible device information base according to the asset fingerprint information;

[0098] The temporary library module is used to call the knowledge base to establish a temporary library based on the matching result;

[0099] The generation module is used to build the index relationship between the temporary library and the report framework, and realize the generation of the operation guide.

[0100] The device of the present embodiment can be used to realize the method described in embodiment one.

[0101] Embodiment three:

[0102] The present embodiment provides a secret evaluation operation guide intelligent generation device based on fingerprint cross comparison, which comprises a processor and a storage medium;

[0103] The storage medium is used to store instructions;

[0104] The processor is used to operate according to the instructions to perform the steps according to the following method:

[0105] Step one: based on offline acquisition and lossless version sniffing, the asset fingerprint of the system to be evaluated is acquired;

[0106] Step two: cross-match the asset fingerprint with the compatible device information library keywords, and if the matching fails, feedback a prompt message, and adjust the format or supplement the knowledge base according to the prompt;

[0107] Step three: accurate matching, call the evaluation implementation content, and establish a temporary library;

[0108] Step four: based on the temporary library, build an index relationship with the report framework, and realize the generation of different device or level job instruction sheets.

[0109] Those skilled in the art will understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0110] The present application is described with reference to flowcharts and / or block diagrams according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in a flow or multiple flows and / or blocks Figure 1 The functions specified in a flow or multiple flows and / or blocks

[0111] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction devices that implement the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in a flow or multiple flows and / or blocks Figure 1 The functions specified in a flow or multiple flows and / or blocks

[0112] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide a process for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in a flow or multiple flows and / or blocks Figure 1 The functions specified in a flow or multiple flows and / or blocks

[0113] The above description is only preferred embodiments of the present application, it should be pointed out that, for those skilled in the art, without departing from the technical principles of the present application, a number of improvements and variations can be made, which should be considered as the protection scope of the present application.

Claims

1. A method for intelligently generating password assessment work instructions based on fingerprint cross-comparison, characterized in that, Includes the following steps: Asset fingerprint information is collected using multiple combined methods; Cross-matching of asset fingerprint information with a compatible device information database; A temporary library is created by calling the knowledge base based on the matching results; Establish the relationship between the temporary library and the report framework index to generate work instructions; The multiple combination methods include offline collection and lossless version sniffing to obtain asset fingerprint information of the evaluated system; The offline collection includes: obtaining asset fingerprint information using collection scripts or programs or manual verification methods; The lossless version sniffing includes: passively collecting asset fingerprint information of the evaluated system to reduce the load on the evaluated system; The asset fingerprint information includes asset type, device model, system version, kernel version, application system architecture, and development language; The asset types include access control systems, video surveillance systems, network and communication equipment, core computing equipment, and application systems; Based on cross-matching of asset fingerprint information with a compatible device information database, including: Based on the asset fingerprint, the fingerprint information is escaped. The escaped results are cross-matched with keywords in the compatible device information database. If the match fails, the error node and information are prompted or the information to be supplemented in the knowledge base is prompted, so as to further correct and optimize the data and further compare it until the match is accurate. The knowledge base data is called as needed. The fingerprint information escaping includes: escaping Chinese characters into full alphabetical spelling, and filtering out spaces in the fingerprint information; The database keywords include asset type, equipment model, system version, kernel version, application system architecture, and development language; The cross-matching includes prioritizing matching asset types and performing cross-comparison based on type, including the following steps: When the asset type is an access control system or a video surveillance system, further match the equipment model; When the asset type is network and communication equipment, the equipment model is further matched, and the system version resource pool is retrieved for matching based on the equipment model; When the asset type is a core computing device, the system version is further matched, and the kernel version resource pool is retrieved for matching based on the system version. When the asset type is an application system, the system architecture and development language are matched sequentially.

2. The intelligent generation method for password evaluation work instructions based on fingerprint cross-comparison according to claim 1, characterized in that, Supplementing the knowledge base information includes: optimizing and updating the knowledge base by technical personnel based on the acquired asset fingerprint information; The knowledge base includes an instance library created in a relational database and files stored in a specified path; The knowledge base includes: A cryptographic evaluation metrics library is used to store metrics covering multiple levels of requirements; A compatible device information database is used to store database keywords; The assessment implementation content library is used to store indicator assessment content that matches different asset fingerprints; The evaluation method example library stores detailed evaluation methods for different indicator evaluation contents; The expected results library is used to store the expected results that meet different asset fingerprint evaluation indicators; The report framework stores the specific path to the report master and the index relationship between the report framework and the knowledge base.

3. The intelligent generation method for password evaluation work instructions based on fingerprint cross-comparison according to claim 1, characterized in that, A temporary library is built based on the matching results and the knowledge base, including: Knowledge base data is extracted sequentially based on cross-matching logic, and the extracted data is stored in a temporary database for previewing and building the report framework index. The steps involved in extracting knowledge base data according to cross-matching logic are as follows: Indicators are selected from the cryptographic evaluation indicator library based on asset type; For access control systems and video surveillance systems, data is extracted from the evaluation implementation content library, evaluation method example library, and expected result library based on the equipment model and the selected indicators. For network and communication equipment, the system version is further specified according to the equipment model, and data is extracted from the evaluation implementation content library, evaluation method example library, and expected result library for the selected indicators. For core computing devices, the kernel version is further confirmed based on the system version, and data is extracted from the evaluation implementation content library, evaluation method example library, and expected result library for the selected indicators. For application systems, data is extracted from the evaluation implementation content library, evaluation method example library, and expected result library based on the system architecture, development language, and selected indicators.

4. The intelligent generation method for password evaluation work instructions based on fingerprint cross-comparison according to claim 1, characterized in that, Establish the relationship between the temporary library and the report framework index to generate work instructions, including: Using the established temporary library, construct the index relationship between the temporary library and the report framework to generate work instructions for different devices or levels.

5. The intelligent generation method for password evaluation work instructions based on fingerprint cross-comparison according to claim 4, characterized in that, The report framework is a preset doc format report master, and different asset fingerprints establish differentiated index calling relationships with the report master; The index relationship is established by relating the fields of the temporary library to the report master, using keys and variables in the Word template to create the index relationship.

6. A smart device for generating cryptographic evaluation work instructions based on fingerprint cross-comparison for performing the method as described in claim 1, characterized in that, The device includes: Data Acquisition Module: Used to collect asset fingerprint information using multiple combined methods; Matching module: Used to cross-match asset fingerprint information with a compatible device information database; Temporary library module: Used to create a temporary library based on the matching results and by calling the knowledge base; Generation module: Used to build the relationship between the temporary library and the report framework index, and to generate the work instructions.

7. A smart generation device for password evaluation work instructions based on fingerprint cross-comparison, characterized in that, Including processor and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1 to 5.

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