Password space generation system, method, medium, program product, and terminal based on user personal information

Abstract

The application provides a password space generation system, method, medium, program product and terminal based on user personal information. The application can effectively improve the decryption success rate in practical environment by analyzing and extracting personalized user personal information, combining general materials in the existing material library module, and quickly generating targeted password space in batches. Meanwhile, the password generation module of the application can be deployed in multiple instances across nodes, can realize high-speed parallel work, and improve the overall use efficiency of the decryption system.

Description

Password space generation system, method, medium, program product and terminal based on user personal information Technical Field

[0001] This application relates to the field of decryption technology, and in particular to password space generation systems, methods, media, program products and terminals based on user personal information. Background Technology

[0002] In the field of password cracking, the mainstream approaches currently include dictionary attacks and mask-based brute-force attacks. The basic principle of dictionary attacks is to use a pre-compiled dictionary containing a large number of possible password combinations. An automated program tries each password in the dictionary to match the password of the target system (such as computer accounts, network device accounts, encrypted files, etc.). Mask-based brute-force attacks use a specific format mask for the password and then perform brute-force attempts according to the mask rules to find the correct password. The mask can specify information such as the type, length, and position of characters contained in the password. In addition, some researchers have used more advanced techniques, such as Markov chains and contextual probability algorithms, to extend existing passwords and have achieved some success.

[0003] However, these technical measures ultimately generate broad, generalized decryption strategies for the majority of users. In the real world, many real-world codebreaking cases are targeted at specific individuals or groups, requiring decryption methods to be customized based on the characteristics of individuals or groups. Currently, some patents have been published for decryption strategies targeting specific groups, but these still have some limitations.

[0004] For example, the patent with publication number CN115098848A, entitled "A Method for Guessing Password Sets Based on Multi-Task Learning in Small Samples", discloses a password analysis and guessing method based on small samples. By extracting and training the structural features of small samples, the desired target password structure is generated. Then, the password structure is filled with password materials extracted from the small sample training set and the auxiliary training set to generate new passwords. However, this small sample is essentially still a bunch of passwords.

[0005] For example, the patent with publication number CN115549919A, entitled "A method, system, device and storage medium for dictionary and mask attack based on artificial intelligence", discloses a framework description of the overall decryption process. The social engineering database unit in this patent can generate passwords through personal information, but it does not elaborate on the specific method for generating passwords for personal information. Summary of the Invention

[0006] In view of the shortcomings of the prior art, the present invention provides a password space generation system, method, medium, program product and terminal based on user personal information, which is used to solve the problem that the password cracking of the prior art is limited to specific personnel or groups.

[0007] To achieve the above and other related objectives, a first aspect of this application provides a password space generation system based on user personal information, comprising: a material library module for providing general materials for password generation; a personal information input module for inputting user personal information, wherein the user personal information includes password-related information and non-password-related information; a password structure analysis module connected to the personal information input module for analyzing the password-related information to extract structural expressions and fields; the set of each structural expression constitutes a structural expression dataset; a material decomposition and extraction module connected to both the personal information input module and the password structure analysis module for extracting and classifying materials from the non-password-related information in the personal information input module to extract fields to form a first field dataset; semantically classifying the fields in the password structure analysis module, the set of each semantic type of field constitutes a second field dataset; and combining the first field dataset and the second field dataset. The data is consolidated into a field dataset, and the total number of fields for each structural expression and semantic type in both the structural expression dataset and the field dataset is counted to obtain statistical results. A weighting module, connected to both the material decomposition and extraction module and the material library module, assigns weights to each structural expression and semantic type field based on the statistical results from the material decomposition and extraction module to obtain a user's personal material library. The weight values ​​in the user's personal material library are adjusted based on general materials in the material library module, and the adjusted user's personal material library is integrated with the general materials to obtain the final password generation material library. A password generation module, connected to the weighting module, generates weight values ​​for structural expressions and fields in the material library based on the final password, calculates the password weight value according to the weight calculation formula, and generates new passwords sequentially in descending order of the password weight value to reconstruct the password space; wherein, the new passwords are generated in parallel.

[0008] In some embodiments of the first aspect of this application, the method for adjusting the weight values ​​in the user's personal material library based on the general materials in the material library module is as follows: the maximum weight value of the structural expression or field in the user's personal material library is slightly higher than the maximum weight value of the corresponding structural expression or field in the general materials.

[0009] In some embodiments of the first aspect of this application, the weight calculation formula is as follows: Where Q is the weight value of the password, X is the weight value of the structure expression, N is the sum of the weight values ​​of the semantic types corresponding to each field in the password, and M is the number of fields.

[0010] In some embodiments of the first aspect of this application, the process of generating the new password in parallel includes: dividing the password space into several intervals, and allocating a computing unit to each interval for password generation; wherein, taking the starting point of the first interval as a reference, setting a corresponding offset for the starting point of each interval, and each computing unit skipping its corresponding offset before generating the password within the corresponding interval.

[0011] In some embodiments of the first aspect of this application, the password structure analysis module, the material decomposition and extraction module, and the weight processing module are deployed on a management server; the password generation module is deployed on a decryption platform.

[0012] In some embodiments of the first aspect of this application, the structure expression includes the data type, length, and position of the field.

[0013] To achieve the above and other related objectives, a second aspect of this application provides a method for generating a password space based on user personal information, applied to the aforementioned password space generation system based on user personal information. The method includes: providing general materials for password generation; inputting user personal information; the user personal information includes: password-related information and non-password-related information; analyzing the password-related information to extract structural expressions and fields; the set of each structural expression constitutes a structural expression dataset; extracting and classifying materials from the non-password-related information in the personal information input module to extract fields that constitute a first field dataset; semantically classifying the fields in the password structure analysis module, the set of each semantic type of field constitutes a second field dataset; and combining the first field dataset and the second field dataset. The data is integrated into a field dataset, and the total number of fields for each structural expression and semantic type in the structured expression dataset and field dataset is counted to obtain statistical results. Based on the statistical results from the material decomposition and extraction module, weights are assigned to fields for each structural expression and semantic type to obtain a user's personal material library. The weight values ​​in the user's personal material library are adjusted based on the general materials in the material library module, and the adjusted user's personal material library is integrated with the general materials to obtain the final password generation material library. Based on the weight values ​​of the structural expressions and fields in the final password generation material library, the weight values ​​of the passwords are calculated according to the weight calculation formula, and new passwords are generated sequentially in descending order of the password weight values ​​to reconstruct the password space; wherein, the new passwords are generated in parallel.

[0014] To achieve the above and other related objectives, a third aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the password space generation method based on user personal information.

[0015] To achieve the above and other related objectives, a fourth aspect of this application provides a computer program product comprising computer program code, which, when executed on a computer, enables the computer to implement the password space generation method based on user personal information.

[0016] To achieve the above and other related objectives, a fifth aspect of this application provides an electronic terminal, including a memory, a processor, and a computer program stored in the memory; the processor executes the computer program to implement the password space generation method based on user personal information.

[0017] As described above, the password space generation system, method, medium, program product, and terminal based on user personal information provided in this application have the following beneficial effects:

[0018] This application analyzes and extracts personalized user information, combining it with general materials from an existing material library module to generate targeted password spaces in batches quickly, effectively improving the success rate of decryption in real-world environments. Furthermore, the password generation module of this invention can be deployed across multiple nodes, enabling high-speed parallel operation and improving the overall efficiency of the decryption system. Attached Figure Description

[0019] Figure 1 shows a schematic diagram of a password space generation system based on user personal information in one embodiment of this application.

[0020] Figure 2 shows a flowchart of a password space generation method based on user personal information in one embodiment of this application.

[0021] Figure 3 shows a schematic diagram of the structure of an electronic terminal in one embodiment of this application. Detailed Implementation

[0022] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0023] To facilitate understanding of the embodiments of this application, a detailed description will first be provided with reference to Figure 1. Figure 1 shows a schematic diagram of the structure of a password space generation system based on user personal information according to an embodiment of the present invention. The system includes: a material library module 110, a personal information input module 120, a password structure analysis module 130, a material decomposition and extraction module 140, a weight processing module 150, and a password generation module 160.

[0024] In one embodiment, as shown in FIG1, the material library module 110 is used to provide general materials for password generation.

[0025] It should be understood that the material library module 110 includes general materials such as structured expressions and fields for password generation. These general materials include names of people, places, items, common phrases, poems, lyrics, mobile phone numbers, ID numbers, numbers, phrases, and special characters. The general materials are obtained through methods such as collecting and organizing from the internet and analyzing and extracting from existing password dictionaries. In this embodiment, the general materials in the material library module undergo priority weighting, meaning that different weight values ​​are assigned to each material based on its probability of appearing in known passwords. Higher weight values ​​result in higher priority for using the material for password generation, increasing the effectiveness or security of the generated password.

[0026] In one embodiment, as shown in FIG1, the personal information input module 120 is used to input user personal information; the user personal information includes: password-type information and non-password-type information.

[0027] Specifically, the personal information input module 120 allows users to input personal information via the user interface, read from a database, pass through an API interface, or import it via command line. This personal information refers to the user's personalized identity information, which is divided into two types: password-based information and non-password-based information. Password-based information includes the user's mobile phone password, bank card password, email password, social media password, etc., while non-password-based information includes the user's name, nickname, birthday, mobile phone number, anniversary, etc. By analyzing the user's personal information, the system obtains the user's password habits and preferences. Based on the user's different needs and preferences, the system generates passwords that are more in line with the user's habits and can flexibly adjust the password generation strategy, making it more personalized and targeted, and improving the decryption efficiency.

[0028] In one embodiment, as shown in FIG1, the password structure analysis module 130 is connected to the personal information input module 120 and is used to analyze the password information to extract structure expressions and fields; the set of each structure expression is a structure expression dataset.

[0029] In some examples, the structure expression includes the data type, length, and position of the fields.

[0030] It should be noted that the password structure analysis module 130 receives password-type information from the personal information input module 120, analyzes it, and extracts the password's structural expression and fields. The structural expression defines the password's structure, including the data type, length, and position of the fields. The data types of the fields include letter fields, number fields, and special symbol fields.

[0031] For example, password-related information includes the following passwords:

[0032] (1) password, letmein, qwerty (the structure expression is: pure letter field structure);

[0033] (2) welcome123, secure789, hello456 (the structure expression is: letter field + number field structure);

[0034] (3) 123@!, 456#$, 789&* (The structure expression is: numeric field + special symbol field structure);

[0035] (4) hello@, secure#, admin! (The structure expression is: letter field + special symbol field structure);

[0036] (5) John123! 、 Alice456@ 、 admin789# (The structure expression is: letter field + number field + special symbol field structure).

[0037] It should be understood that field length refers to the number of characters in a field. For example, in the password "admin789#", the field length of "admin" is 5, the field length of "789" is 3, and the field length of "#" is 1. Field position refers to the order in which the fields are arranged in the password. For example, in the password "admin789#", "admin" is the first field, "789" is the second field, and "#" is the third field. The structure expression extracted from the above password information is:

[0038] (1) <letter field>;

[0039] (2) <alphabetic field> <numeric field>;

[0040] (3) <Number Field> <Special Symbol Field>;

[0041] (4) <Letter Field> <Special Symbol Field>;

[0042] (5) <Letter field> <Number field> <Special symbol field>.

[0043] Furthermore, the structured expressions extracted from password-related information are organized into a structured expression dataset.

[0044] In one embodiment, as shown in FIG1, the material decomposition and extraction module 140 is connected to the personal information input module 120 and the password structure analysis module 130 respectively. It is used to extract and classify non-password information in the personal information input module 120 to extract fields to form a first field dataset; to perform semantic type division on the fields in the password structure analysis module 130, and the set of fields of each semantic type is a second field dataset; to integrate the first field dataset and the second field dataset into a field dataset, and to count the total number of fields of each structural expression and each semantic type in the structure expression dataset and the field dataset respectively to obtain statistical results.

[0045] It should be noted that the personal information input module 120 also includes non-password information. The material generated by the password is extracted from the non-password information and classified, that is, the fields are subjected to semantic type classification processing. The processing steps include: further classifying the letter field, number field and special symbol field into semantic types based on semantic attributes; wherein, the semantic type of the letter field includes one or more combinations of personal name, place name, item name, common phrase, poem, and song lyric, and the semantic type of the number field includes one or more combinations of telephone number, ID number, date, and postal code.

[0046] After classifying each field in the non-password information, the data is organized into a first-field dataset. For example, the classification results of the fields in the non-password information are as follows:

[0047] (1) Letter field:

[0048] Names: Bob, Charlie;

[0049] Place names: Paris, London, Tokyo.

[0050] (2) Numeric fields:

[0051] Common years: 1990, 2000, 2023;

[0052] Telephone prefixes: 400, 800.

[0053] The fields extracted from the password structure analysis module 130 are subjected to the same semantic type partitioning, and the resulting dataset is organized into the second field dataset. For example, the classification results obtained by semantically partitioning the password-related information fields in the example above are as follows:

[0054] (1) Letter field:

[0055] Words: password, letmein, qwerty, welcome, secure, hello, admin;

[0056] Names: John, Alice;

[0057] (2) Numeric fields:

[0058] Common numbers: 123, 456, 789;

[0059] (3) Special symbol field:

[0060] Common symbol combinations: @, #, !, @!, #$, &*;

[0061] Semantic special symbols: ##, ! ! (emphasis type), ￥￥ (currency type).

[0062] Furthermore, the first field dataset and the second field dataset are merged into a field dataset, and the total number of fields of each structural expression and each semantic type in the structure expression dataset and the field dataset are counted respectively. For example, in the example above, the structural expressions are: (1) <letter field> (2) <letter field> <number field> (3) <number field> <special symbol field> (4) <letter field> <special symbol field> (5) <letter field> <number field> <special symbol field>, a total of five types.

[0063] The statistical results for each semantic type of the above data set are as follows:

[0064] (1) Letter field:

[0065] Words: password, letmein, qwerty, welcome, secure, hello, admin (7 in total);

[0066] Names: Bob, Charlie, John, Alice (4 in total);

[0067] Place names: Paris, London, Tokyo (3 in total);

[0068] (2) Numeric fields:

[0069] Common years: 1990, 2000, 2023 (3 in total);

[0070] Telephone prefixes: 400, 800 (2 in total);

[0071] Common numbers: 123, 456, 789 (3 in total);

[0072] (3) Special symbol field:

[0073] Common symbol combinations: @, #, !, @!, #$, &* (6 in total);

[0074] Semantic special symbols: ##, ! ! (emphasis type), ￥￥ (currency type) (3 in total).

[0075] In one embodiment, as shown in FIG1, the weight processing module 150 is connected to the material decomposition and extraction module 140 and the material library module 110 respectively. It is used to assign weight values ​​to fields of each structural expression and each semantic type according to the statistical results in the material decomposition and extraction module 140 to obtain a user personal material library; adjust the weight values ​​in the user personal material library according to the general materials in the material library module 110; and integrate the adjusted user personal material library with the general materials to obtain the final password generation material library.

[0076] It should be noted that after analyzing and processing password-related and non-password-related information in user personal information, a structured expression dataset, a field dataset, and corresponding statistical results are obtained. Weights are then assigned based on these statistical results. Weighting is based on the frequency of structured expressions appearing in the statistical results; structured expressions with higher frequency (more occurrences) have a higher priority in password generation and are therefore assigned a greater weight. Similar to structured expressions, field weighting is also based on their frequency of appearance in the statistical results; frequently occurring fields are assigned higher weights to reflect their importance in password generation.

[0077] For example, the weight values ​​of structured expressions can be divided into 1-32 levels, and the weight values ​​of fields can also be divided into 1-32 levels, where level 32 represents the highest priority and level 1 represents the lowest priority. This hierarchical method helps to quickly identify and use high-priority materials when calculating the weight value of passwords later.

[0078] It should be understood that excessive weighting can amplify invalid information, reducing the comprehensiveness and coverage of passwords, making the generated passwords too concentrated and easy to crack. Insufficient weighting may lead to insufficient integration of personalized information, missing some possible passwords and reducing the personalization and security of the passwords. Therefore, the specific value of the weight assignment should be determined based on the actual situation. The weight assignment strategy should be flexibly adjusted according to different application scenarios and security requirements to achieve the best password generation effect. This embodiment of the invention does not impose any limitations on this.

[0079] The structure expression and fields after weight assignment are organized to obtain the user's personal material library. The materials in the user's personal material library highlight the user's password habits and preferences, making the passwords generated by the system based on the materials in the user's personal material library more in line with the user's habits, thereby improving the success rate of decryption.

[0080] Furthermore, the user's personal material library is merged with the general materials in the existing material library. The merged material can increase the combinations of password generation, making the generated passwords more diverse and increasing the probability of successful decryption. Before merging, in order to highlight the special nature of the user's personal material library and make password generation more targeted, the weight values ​​in the user's personal material library are adjusted based on the weight values ​​of the general materials.

[0081] Specifically, the method for adjusting the weight values ​​in the user's personal material library based on the general materials in the material library module 110 is as follows: the maximum weight value of the structural expressions or fields in the user's personal material library is slightly higher than the maximum weight value of the corresponding structural expressions or fields in the general materials. For example, the maximum weight value of any structural expression in the user's personal material library is greater than the maximum weight value of the corresponding structural expression in the general materials, and the weight value of each semantic type field in the user's personal material library is greater than the maximum weight value of the corresponding semantic type field in the general materials. This ensures that the materials (structural expressions or fields) in the personal material library are used preferentially when generating passwords after merging. Preferably, the maximum weight value of the materials in the user's personal material library = the maximum weight value of the corresponding material in the general materials + 2. Tests have shown that this assignment mode can achieve a good cracking hit rate. The user's personal material library after adjusting the weight values ​​is integrated with the general materials to obtain the final password generation material library, that is, merging materials with the same weight and type. The final password generation material library includes analysis result files and analysis result index files.

[0082] In one embodiment, as shown in FIG1, the password generation module 160 is connected to the weight processing module 150. It is used to generate the weight value of the password based on the structural expression and field weight value in the final password generation material library, calculate the weight value of the password according to the weight calculation formula, and generate new passwords in order from high to low according to the weight value of the password to reconstruct the password space; wherein, the new passwords are generated in parallel.

[0083] In some examples, the weight calculation formula is as follows:

[0084]

[0085] Where Q is the weight value of the password, X is the weight value of the structure expression, N is the sum of the weight values ​​of the semantic types corresponding to each field in the password, and M is the number of fields.

[0086] It should be understood that the weight calculation formula does not constitute a limitation on the scope of protection of this invention. Other formulas can also be used to calculate the corresponding weight values. Other calculation formulas that can convert frequency data into corresponding weight values ​​all fall within the scope of protection of this invention.

[0087] In some examples, the process of generating the new password in parallel includes: dividing the password space into several intervals, allocating a computing unit to each interval for password generation; wherein, taking the starting point of the first interval as a reference, setting a corresponding offset for the starting point of each interval, and each computing unit skipping its corresponding offset before generating the password within the corresponding interval.

[0088] For example, consider a weighted password space containing 12 passwords: a1#, a1&, a2#, a2&, b1#, b1&, b2#, b2&, c1#, c1&, c2#, c2&. This password space can be divided into 3 intervals, with each interval generating 4 passwords.

[0089] Interval 1: a1#, a1&, a2#, a2&.

[0090] Interval 2: b1#, b1&, b2#, b2&.

[0091] Interval 3: c1#, c1&, c2#, c2&.

[0092] Different computing units are assigned to intervals 1 to 3 for generation. Each computing unit only needs to load the data of its own subspace and does not need to process the entire password space. Furthermore, by having multiple computing units process different parts of the password space simultaneously, the generation speed is accelerated. In addition, by using offset and password quantity parameters, the task scope of each subspace is clearly defined, and the generated passwords do not overlap or omit any.

[0093] Preferably, an analysis result file is assigned to the weight value of each semantic type field in the final password generation material library. Simultaneously, an analysis result index file is generated by aggregating the weight values ​​of all semantic types. This index file contains information such as material type, corresponding weight, and material quantity. Compared to dictionary files and material library files (typically in the tens of gigabytes range), the analysis result file is much smaller (in the several megabytes range). Therefore, the analysis result file can be sent to the password generation module on the computing node via a network interface call, or it can be accessed by the password generation module on the computing node as a shared file via shared storage. The access method is chosen based on the actual situation and is not limited in this embodiment. When generating passwords, based on the analysis result index file, the number of passwords specified by the offset parameter can be quickly skipped through a simple calculation, and then the contents of the specified analysis result file are read. The specified number of passwords is then quickly generated based on the password quantity parameter.

[0094] In some examples, the password structure analysis module, material decomposition and extraction module, and weight processing module are deployed on a management server; the password generation module is deployed on a decryption platform.

[0095] Referring to Figure 1, each computing node (computing unit) deploys a password generator program. Analysis result files and analysis result index files are stored in a shared storage area, accessible simultaneously to the weight processing module in the management server and the password generation module in the computing node. The password generation module is deployed in a distributed, multi-instance configuration, forming a decryption cluster. Each deployment instance can independently use one or more sets of offset and password quantity parameters to generate passwords within the specified parameter range. Different deployment instances run independently and concurrently. The generated passwords are directly transferred to the decryption program via shared memory or pipes, without requiring a file storage system as an intermediary.

[0096] Furthermore, configuring password intervals (offset and number of passwords) and invoking the password generation module can also be autonomously completed by a third-party scheduler, thereby automating the entire decryption process. Once a computing unit (computing node) completes password calculation within one interval, it can quickly obtain the next interval from the third-party scheduler for password calculation, until all planned intervals have been decrypted. This method enables the concurrent operation of multiple computing units, maximizing the utilization of computing resources and reducing decryption time.

[0097] It should be emphasized that, in the system operation of this application embodiment, the material library (general material) is first initialized through manual import mode, and then the user's personal information is analyzed through the password structure analysis module and the material decomposition and extraction module. The analysis result file and analysis result index component are generated in combination with the general material. Finally, different password ranges are assigned to each computing node (computing unit) by configuring the offset and the number of passwords. The password generator in the computing node generates the password for the corresponding range and sends it to the decryption program for subsequent processing.

[0098] This application analyzes and extracts personalized user information, combining it with general materials from an existing material library module to generate targeted password spaces in batches quickly, effectively improving the success rate of decryption in real-world environments. Furthermore, the password generation module of this invention can be deployed across multiple nodes, enabling high-speed parallel operation and improving the overall efficiency of the decryption system.

[0099] In the embodiments of this application, the terms "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect, without limiting their order. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that the terms "first" and "second" do not necessarily imply that they are different.

[0100] It should be noted that, in the embodiments of this application, the words "exemplary" or "for example" indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0101] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0102] Figure 2 is a schematic block diagram of a password space generation method based on user personal information provided in an embodiment of this application. As shown in Figure 2, the method includes:

[0103] Step S21: Provide general materials for password generation;

[0104] Step S22: Input user personal information; the user personal information includes: password-related information and non-password-related information;

[0105] Step S23: Analyze the password information to extract the structure expressions and fields; the set of each structure expression is called the structure expression dataset;

[0106] Step S24: Extract and classify the non-password information in the personal information input module to form a first field dataset; perform semantic type division on the fields in the password structure analysis module, and the set of fields of each semantic type is the second field dataset; integrate the first field dataset and the second field dataset into a field dataset, and count the total number of fields of each structural expression and each semantic type in the structure expression dataset and the field dataset respectively to obtain statistical results;

[0107] Step S25: Assign weights to fields of each structural expression and each semantic type based on the statistical results in the material decomposition and extraction module to obtain a user's personal material library; adjust the weight values ​​in the user's personal material library based on the general materials in the material library module; integrate the adjusted user's personal material library with the general materials to obtain the final password generation material library;

[0108] Step S26: Based on the final password, generate the structural expression and field weight values ​​in the material library, calculate the password weight value according to the weight calculation formula, and generate new passwords in descending order of the password weight values ​​to reconstruct the password space; wherein, the new passwords are generated in parallel.

[0109] It should be noted that the password space generation method based on user personal information provided in this application can be specifically applied to controllers such as ARM (Advanced RISC Machines), FPGA (Field Programmable Gate Array), SoC (System on Chip), DSP (Digital Signal Processing), or MCU (Microcontroller Unit); or it can be applied to computers including components such as memory, memory controller, one or more processing units (CPU), peripheral interfaces, RF circuits, audio circuits, speakers, microphones, input / output (I / O) subsystems, displays, other output or control devices, and external ports; the computers include but are not limited to personal computers such as desktop computers, laptops, tablets, smartphones, smart TVs, and personal digital assistants (PDAs); it can also be applied to servers, which can be deployed on one or more physical servers according to various factors such as function and load, or can be composed of distributed or centralized server clusters.

[0110] It should be understood that the specific process of performing the above-mentioned steps has been described in detail in the above module embodiments, and will not be repeated here for the sake of brevity.

[0111] It should also be understood that the module division in the embodiments of this application is illustrative and only represents a logical functional division; in actual implementation, there may be other division methods. Furthermore, the functional modules in the various embodiments of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0112] Figure 3 is a schematic block diagram of an electronic terminal provided in an embodiment of this application. As shown in Figure 3, the electronic terminal includes at least one processor 301, a memory 302, at least one network interface 303, and a user interface 305. The various components in the device are coupled together via a bus system 304. It is understood that the bus system 304 is used to implement communication between these components. In addition to a data bus, the bus system 304 also includes a power bus, a control bus, and a status signal bus. However, for clarity, all buses are labeled as a bus system in Figure 3.

[0113] The user interface 305 may include a monitor, keyboard, mouse, trackball, clicker, button, touchpad, or touch screen.

[0114] It is understood that memory 302 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM) or programmable read-only memory (PROM), used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memories described in the embodiments of this invention are intended to include, but are not limited to, these and any other suitable categories of memory.

[0115] In this embodiment of the invention, the memory 302 is used to store various types of data to support the operation of the electronic terminal 300. Examples of this data include: any executable program for operation on the electronic terminal 300, such as the operating system 3021 and application programs 3022; the operating system 3021 contains various system programs, such as the framework layer, core library layer, driver layer, etc., for implementing various basic services and handling hardware-based tasks. The application program 3022 may contain various applications, such as media players, browsers, etc., for implementing various application services. The password space generation method based on user personal information provided in this embodiment of the invention can be included in the application program 3022.

[0116] The methods disclosed in the above embodiments of the present invention can be applied to processor 301, or implemented by processor 301. Processor 301 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 301 or by instructions in the form of software. The processor 301 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 301 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. General-purpose processor 301 may be a microprocessor or any conventional processor, etc. The steps of the accessory optimization method provided in the embodiments of the present invention can be directly reflected as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium, which is located in memory. The processor reads the information in the memory and combines it with its hardware to complete the steps of the aforementioned method.

[0117] In an exemplary embodiment, the electronic terminal 300 may be used by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), or complex programmable logic devices (CPLDs) to execute the aforementioned method.

[0118] According to the method provided in the embodiments of this application, this application also provides a computer program product, which includes: computer program code, which, when run on a computer, causes the computer to execute the password space generation method based on user personal information in any of the embodiments shown.

[0119] According to the method provided in the embodiments of this application, this application also provides a computer-readable storage medium storing program code, which, when run on a computer, causes the computer to execute the password space generation method based on user personal information in any of the embodiments shown.

[0120] As used in this specification, the terms "component," "module," "system," etc., are used to refer to computer-related entities, hardware, firmware, combinations of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or a computer. As illustrated, applications running on computing devices and computing devices can both be components. One or more components may reside in a process and / or an execution thread, and components may be located on a single computer and / or distributed among two or more computers. Furthermore, these components can be executed from various computer-readable media on which various data structures are stored. Components can communicate, for example, via local and / or remote processes based on signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system, and / or a network, such as the Internet interacting with other systems via signals).

[0121] Those skilled in the art will recognize that the various illustrative logical blocks and steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0122] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0123] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0124] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0125] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0126] In the above embodiments, the functions of each functional unit can be implemented entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. A computer program product includes one or more computer instructions (programs). When the computer program instructions (programs) are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs, DVDs), or semiconductor media (e.g., solid-state disks, SSDs, etc.).

[0127] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0128] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0129] In summary, this application provides a password space generation system, method, medium, program product, and terminal based on user personal information. This application analyzes and extracts personalized user personal information and combines it with general materials from an existing material library module to generate targeted password spaces in batches quickly, effectively improving the success rate of decryption in real-world environments. Furthermore, the password generation module of this invention can be deployed across multiple instances and nodes, enabling high-speed parallel operation and improving the overall efficiency of the decryption system. Therefore, this application effectively overcomes the various shortcomings of existing technologies and has high industrial applicability.

[0130] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A password space generation system based on user personal information, characterized in that, include: The resource library module provides general resources for password generation; The personal information input module is used to input user personal information; The user's personal information includes: password-related information and non-password-related information; a password structure analysis module, connected to the personal information input module, is used to analyze the password-related information to extract structural expressions and fields; the set of each structural expression constitutes a structural expression dataset; a material decomposition and extraction module, connected to both the personal information input module and the password structure analysis module, is used to extract and classify the non-password-related information in the personal information input module to extract fields that constitute a first field dataset; the fields in the password structure analysis module are semantically categorized, and the set of fields of each semantic type constitutes a second field dataset; the first field dataset and the second field dataset are integrated into a field dataset, and each structural expression and each semantic type in the structural expression dataset and field dataset are statistically analyzed respectively. The system calculates the total number of fields of each type to obtain statistical results. A weighting module, connected to both the material decomposition and extraction module and the material library module, assigns weights to fields of each structural expression and semantic type based on the statistical results from the material decomposition and extraction module to obtain a user-specific material library. It then adjusts the weight values ​​in the user-specific material library based on general materials from the material library module, integrating the adjusted user-specific material library with the general materials to obtain the final password generation material library. A password generation module, connected to the weighting module, assigns weight values ​​to structural expressions and fields in the final password generation material library based on the weight values. It calculates the password weight value according to the weight calculation formula and generates new passwords sequentially in descending order of the password weight values ​​to reconstruct the password space. The new passwords are generated in parallel.

2. The password space generation system based on user personal information according to claim 1, characterized in that, The method for adjusting the weight values ​​in the user's personal material library based on the general materials in the material library module is as follows: the maximum weight value of the structural expression or field in the user's personal material library is slightly higher than the maximum weight value of the corresponding structural expression or field in the general materials.

3. The password space generation system based on user personal information according to claim 1, characterized in that, The weight calculation formula is as follows: Where Q is the weight value of the password, X is the weight value of the structure expression, N is the sum of the weight values ​​of the semantic types corresponding to each field in the password, and M is the number of fields.

4. The password space generation system based on user personal information according to claim 1, characterized in that, The process of generating the new password in parallel includes: dividing the password space into several intervals, and assigning a computing unit to each interval for password generation; wherein, taking the starting point of the first interval as a reference, setting a corresponding offset for the starting point of each interval, and each computing unit skipping its corresponding offset before generating the password within the corresponding interval.

5. The password space generation system based on user personal information according to claim 1, characterized in that, The password structure analysis module, material decomposition and extraction module, and weight processing module are deployed on the management server; the password generation module is deployed on the decryption platform.

6. The password space generation system based on user personal information according to claim 1, characterized in that, The structure expression includes the data type, length, and position of the fields.

7. A method for generating a password space based on user personal information, characterized in that, A password space generation system based on user personal information, as described in any one of claims 1 to 6, is provided by the method as follows: providing general materials for password generation; inputting user personal information; the user personal information includes: password-related information and non-password-related information; analyzing the password-related information to extract structural expressions and fields; the set of each structural expression constitutes a structural expression dataset; extracting and classifying materials from the non-password-related information in the personal information input module to extract fields that constitute a first field dataset; semantically classifying the fields in the password structure analysis module, the set of each semantic type of field constitutes a second field dataset; integrating the first field dataset and the second field dataset into a field dataset, and statistically analyzing the results of each dataset. The total number of each structural expression and each semantic type field in the structured expression dataset and field dataset is calculated to obtain statistical results. Based on the statistical results from the material decomposition and extraction module, weights are assigned to each structural expression and each semantic type field to obtain a user's personal material library. The weight values ​​in the user's personal material library are adjusted based on the general materials in the material library module. The adjusted user's personal material library is then integrated with the general materials to obtain the final password generation material library. Based on the weight values ​​of the structural expressions and fields in the final password generation material library, the weight values ​​of the passwords are calculated according to the weight calculation formula. New passwords are generated sequentially in descending order of the password weight values ​​to reconstruct the password space. The new passwords are generated in parallel.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the password space generation method based on user personal information as described in claim 7.

9. A computer program product, characterized in that, The computer program product includes computer program code, which, when run on a computer, enables the computer to implement the password space generation method based on user personal information as described in claim 7.

10. An electronic terminal, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the password space generation method based on user personal information as described in claim 7.

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