Intelligent coaching method, device and equipment for financial network security and medium

Abstract

The invention discloses an intelligent coaching method and device for financial network security, equipment and a medium, relates to a big data technology, and can be applied to the field of information security. The method comprises the following steps: constructing a financial network security target range, and obtaining learning data of a student in the financial network security target range; determining a student type to which the student belongs according to the learning data; and pushing subsequent learning content matched with the student type to the student. According to the embodiment of the invention, the software and hardware cost required by financial network security training can be reduced, and the training effect of financial network security is improved.

Description

Technical Field

[0001] The present invention relates to big data technology and can be used in the field of information security, and in particular to an intelligent coaching method, device, equipment and medium for financial network security. Background Art

[0002] In the financial industry, as financial operations become increasingly digital and networked, financial cybersecurity risks are becoming increasingly severe. Financial institutions face threats from various cyberattacks and data breaches, creating an increasingly urgent need for financial cybersecurity talent. However, traditional financial cybersecurity training methods present several difficulties and limitations.

[0003] On the one hand, traditional training methods often face high costs. Establishing a realistic financial network environment and conducting practical exercises requires significant capital investment and equipment support. For example, building a lab environment that simulates a bank's internal trading system or securities trading platform to test its security requires not only the procurement of hardware such as servers, storage devices, and firewalls, but also the configuration of specialized operating systems, database management systems, and financial application software, all of which significantly increase initial construction costs.

[0004] On the other hand, traditional training methods simply require users to watch videos on their computers or follow instructions to complete pre-set tasks, lacking personalized guidance and feedback. Every student's learning level, skill requirements, and progress are different, but traditional training often uses a one-size-fits-all approach that fails to meet students' individual needs. This results in the costly training system being underutilized. Summary of the Invention

[0005] The present invention provides an intelligent coaching method, device, equipment and medium for financial network security, so as to reduce the software and hardware costs required for financial network security training and improve the training effect of financial network security.

[0006] According to one aspect of the present invention, there is provided an intelligent coaching method for financial network security, comprising:

[0007] Constructing a financial cybersecurity training ground and obtaining students' learning data in the training ground;

[0008] determining the student type of the student according to the learning data;

[0009] Push subsequent learning content that matches the student type to the student.

[0010] According to another aspect of the present invention, there is provided an intelligent coaching device for financial network security, comprising:

[0011] An acquisition module is used to build a financial cybersecurity shooting range and obtain the learning data of trainees in the financial cybersecurity shooting range;

[0012] An analysis module, configured to determine the student type of the student based on the learning data;

[0013] The push module is used to push subsequent learning content that matches the student type to the student.

[0014] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the intelligent coaching method for financial network security according to any embodiment of the present invention.

[0015] According to another aspect of the present invention, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the intelligent coaching method for financial network security described in any embodiment of the present invention.

[0016] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the intelligent coaching method for financial network security according to any embodiment of the present invention when executed.

[0017] The embodiment of the present invention builds a financial network security training range to simulate a real financial network environment and attack scenarios, reducing the software and hardware costs required for financial network security training. At the same time, corresponding learning content is pushed to students based on their types, thereby realizing personalized training for students and improving the training effect of financial network security.

[0018] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1This is a flowchart of an intelligent coaching method for financial network security provided according to one embodiment of the present invention;

[0021] Figure 2A is a flow chart of an intelligent coaching method for financial network security provided according to another embodiment of the present invention;

[0022] Figure 2B is a schematic diagram of a workflow of an intelligent coach provided according to another embodiment of the present invention;

[0023] Figure 3 is a schematic structural diagram of an intelligent coaching method for financial network security provided according to another embodiment of the present invention;

[0024] Figure 4 It is a schematic structural diagram of an electronic device implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] Figure 1 This is a flowchart of an intelligent coaching method for financial network security provided by one embodiment of the present invention. This embodiment can be applied to situations where, after students have completed their financial network security studies at a financial network security training range, the intelligent coaching system recommends subsequent learning content to them based on their current learning progress. This method can be executed by an intelligent coaching device for financial network security, which can be implemented in the form of hardware and / or software and can be configured in an electronic device with corresponding data processing capabilities, such as an intelligent coaching system. Figure 1 As shown, the method includes:

[0028] S110: Construct a financial cybersecurity training ground and obtain learning data of trainees in the financial cybersecurity training ground.

[0029] S120. Determine the student type of the student based on the learning data.

[0030] S130: Pushing subsequent learning content that matches the student type to the student.

[0031] Specifically, the topology of the financial network is designed, including the layout of various components and services in the financial system, as well as network connections and communication methods. Advanced routing algorithms and load balancing technologies are considered during the design to optimize network performance and resource allocation. Virtualization technology is used to simulate different financial system components and services. Each virtual machine can play a different role in the financial network, such as a bank server, database server, or trading terminal, simulating a realistic financial network environment. Virtualization also utilizes containerization to further improve resource utilization and deployment flexibility. Each container or virtual machine is streamlined, retaining only the essential operating system kernel and application dependencies, thereby reducing resource usage and accelerating instance startup time. Furthermore, automated configuration management and a continuous integration / continuous deployment (CI / CD) pipeline ensure environmental consistency and rapid iteration. Common vulnerabilities and attack methods in the financial network security field are collected and organized, and then applied to a training range environment to simulate realistic network attack scenarios. This includes common network attack techniques, malware, and other diverse attack scenarios. Simulated financial network data traffic and logs are generated and injected into the training range environment, allowing participants to access, analyze, and process real network data. The generated data is ensured to have a certain degree of authenticity and diversity to enhance the trainees' real-world experience. Furthermore, regarding information security, static application security testing (SAST) and dynamic application security testing (DAST) tools are used to automatically detect potential vulnerabilities in the range code, combined with manual review to ensure comprehensive coverage. Furthermore, strict access control policies, including multi-factor authentication (MFA), role-based access control (RBAC), and regular security audits, are implemented within the range environment to prevent unauthorized access and insider threats.

[0032] In the Financial Cybersecurity Range, students can assume the role of attackers, attempting to exploit vulnerabilities and attack techniques to break into banking systems, obtain sensitive information, or steal funds. Students can conduct common attacks such as SQL injection, cross-site scripting, and DDoS attacks to test the security and vulnerabilities of banking systems. The range also provides practical training in defense. Students can assume the role of security administrator for banking systems, deploying security measures such as firewalls, intrusion detection systems, and access control to prevent attacks and protect banking systems. The range also provides learning resources on financial cybersecurity, including tutorials, case studies, and security vulnerability demonstrations.

[0033] When students are learning financial cybersecurity in the financial cybersecurity training ground, the system collects their learning data in the background. These learning data can reflect their learning progress and skill improvement in the cybersecurity training ground at a detailed level.

[0034] Data analytics technology is leveraged preliminarily to deeply explore students' learning data and build a comprehensive and detailed cybersecurity intelligent coaching model. After students complete their current learning phase in the Financial Cybersecurity Training Range, the intelligent coaching model processes and analyzes their learning data to determine their student type. This student type reflects their current learning progress, and different student types correspond to different subsequent learning content. Once the student type is determined, the appropriate subsequent learning content is pushed to the student.

[0035] The system also uses machine learning algorithms and models to analyze and evaluate students' offensive and defensive behaviors in real time. It identifies student behaviors and provides targeted guidance and feedback to help them improve their offensive techniques and strengthen their defensive strategies. It also provides comprehensive evaluations based on student performance and grades, tracking their learning progress and skill improvement. Students can review their grades and evaluation results through the system, understand their strengths and areas for improvement, and further optimize their learning plans and enhance their abilities.

[0036] It should be noted that the information collected is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data comply with the relevant laws, regulations and standards of relevant countries and regions, take necessary confidentiality measures, do not violate public order and good customs, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0037] The embodiment of the present invention builds a financial network security training range to simulate a real financial network environment and attack scenarios, reducing the software and hardware costs required for financial network security training. At the same time, corresponding learning content is pushed to students based on their type, thereby realizing personalized training for students and improving the training effect of financial network security.

[0038] Based on the above embodiment, optionally, the learning data includes learning logs and behavior data.

[0039] Specifically, the contents of the learning log are as follows: 1. Student information, including student ID, name, login time and other basic personal information; 2. Course information, including the name of the course the student is studying, course number, and teacher information; 2. Learning progress information, including the number of videos the student has studied, the courseware browsed, the experiments completed, and the homework submitted. This information can reflect the student's learning activity and the degree of mastery of the learning content; 4. Interaction data, including questions asked, discussion participation, and comment feedback. This information helps to analyze the student's learning participation.

[0040] The behavioral data includes the following: 1. Operation records: In the shooting range environment, every operation performed by the trainee will be recorded, such as command execution, system login and logout, file upload and download, attack simulation, etc.; 2. Behavior sequence: that is, the order of the trainee's behavior in the shooting range, including the order and duration of each operation step, which helps to analyze the trainee's thinking logic and efficiency in solving problems; 3. Operation results: that is, system feedback after each operation, such as command execution results, whether the attack is successful, etc., which helps to judge the trainee's operational skills and problem-solving ability.

[0041] Figure 2A This is a flowchart of an intelligent coaching method for financial network security provided by another embodiment of the present invention. This embodiment is optimized and improved on the basis of the above embodiment. Figure 2A As shown, the method includes:

[0042] S210: Construct a financial cybersecurity training ground and obtain learning data of trainees in the financial cybersecurity training ground.

[0043] S220. Extract features of at least two dimensions from the learning data to obtain a feature matrix; substitute the feature matrix into an intelligent coaching model to obtain a prediction value of whether the student is an excellent student.

[0044] S230. If the predicted value is not less than the preset threshold, the student type to which the student belongs is determined to be an excellent student; if the predicted value is less than the preset threshold, the student type to which the student belongs is determined to be a non-excellent student.

[0045] Selected trainees are those who demonstrate outstanding performance in financial cybersecurity and are eligible for advanced training. The intelligent coaching model is a logistic regression model constructed using logistic regression as a supervised learning algorithm. This model is a cybersecurity intelligent coach that excels in learning, practice, and security awareness, and can play a positive role in the cybersecurity field.

[0046] Specifically, during actual prediction, features of at least N (N ≥ 2) dimensions are first extracted from the learning data to obtain a 1*N feature matrix. Substituting the matrix into the mathematical formula corresponding to the intelligent coaching modeling, a predicted value for whether the student is an excellent student is obtained. The predicted value is compared with a preset threshold. If the predicted value is not less than the preset threshold, it indicates that the student is likely to be an excellent student and the student type is determined to be an excellent student. If the predicted value is less than the preset threshold, it indicates that the student is unlikely to be an excellent student and the student type is determined to be a non-excellent student.

[0047] Optionally, based on the above embodiment, the process of constructing the intelligent coaching model is as follows:

[0048] Use historical learning data as training set data, and use the learning status of students corresponding to historical learning data as known labels

[0049] Using logistic regression as a supervised learning algorithm, fitting the training set data and the known labels to obtain a model parameter vector;

[0050] An intelligent coaching model is constructed according to the model parameter vector.

[0051] Specifically, when building the intelligent coaching model, an m*n matrix is ​​constructed based on the historical learning data of existing students as the training data set, and the learning status of the corresponding students is used as the known label, as shown below:

[0052]

[0053] Among them, m is the number of student samples and n is the number of features.

[0054] Logistic regression is selected as the supervised learning algorithm. The logistic regression model can be expressed as:

[0055]

[0056] Among them, h θ(X) is the model's predicted output, θ is the model's parameter vector, X is the feature matrix, e is the base of the natural logarithm, and T is the transpose of the model parameter vector. The model building process can be represented as fitting the training dataset and known labels y to obtain the optimal model parameter vector θ. The optimization process can be implemented using optimization algorithms such as maximum likelihood estimation or gradient descent. Once the model parameters θ are determined, the intelligent coaching model is constructed.

[0057] Based on the above embodiment, optionally, the formula of the intelligent coaching model is as follows:

[0058]

[0059] Among them, h θ (X) is the predicted value, θ is the model parameter vector determined by pre-fitting, X is the feature matrix, e is the base of the natural logarithm, and T refers to the transpose operation of the model parameter vector.

[0060] S240: Push subsequent learning content that matches the student type to the student.

[0061] Based on the above embodiment, optionally, the pushing of subsequent learning content matching the student type to the student includes:

[0062] If the student type is an outstanding student, network security projects, network security competitions, and advanced unlearned resources similar to those already learned by the student will be pushed to the student;

[0063] If the student type is a non-excellent student, relevant practice questions, relevant practical projects, and basic unlearned resources similar to the resources the student has already learned will be pushed to the student.

[0064] Specifically, for outstanding students:

[0065] On the one hand, the learned resources are determined based on the user's historical learning records, and the similarity between the learned resources and the unlearned resources is calculated. Then, some advanced unlearned resources with high similarity to the learned resources are selected as recommendation results to provide more in-depth learning network security.

[0066] On the other hand, it is inferred that the students have mastered the basic knowledge and demonstrated a high learning ability, and are recommended to participate in more challenging and complex cybersecurity projects or cybersecurity competitions to enhance their practical and problem-solving abilities.

[0067] For students who are not outstanding students:

[0068] On the one hand, the learned resources are determined based on the user's historical learning records, and the similarity between the learned resources and the unlearned resources is calculated. Then, some basic unlearned resources with high similarity to the learned resources are selected as recommendation results to fill knowledge gaps or provide targeted learning materials.

[0069] On the other hand, it infers that students have weaknesses in their current learning stage and recommends relevant practice questions or practical projects to help consolidate the knowledge they have learned and improve their skill level.

[0070] Based on the above embodiment, optionally, before pushing subsequent learning content matching the student type to the student, the method further includes:

[0071] For any unlearned resource, determining the cosine similarity between a feature vector of the unlearned resource and a feature vector of a learned resource;

[0072] Determine whether the unlearned resource is similar to the learned resource according to the cosine similarity.

[0073] Specifically, the similarity between learning resources can be calculated using the following formula:

[0074]

[0075] Among them, sim(i,j) is the cosine similarity, v i and v j are the feature vectors of the unlearned resource i and the learned resource j, respectively. If the calculated cosine similarity is greater than the threshold, the two are determined to be similar; if the calculated cosine similarity is not greater than the threshold, the two are determined to be dissimilar.

[0076] For example, Figure 2B As shown, the intelligent coaching model is deployed in a cybersecurity training range. Based on learning data, it determines whether a student is an outstanding student. If the student is an outstanding student, the system will recommend advanced learning resources, advanced projects, and competitions. If the student is not an outstanding student, the system will recommend relevant learning materials to fill knowledge gaps and improve skills.

[0077] The embodiments of the present invention provide comprehensive learning support by recommending relevant unlearned resources and training activities based on the skill level and needs of the learners.

[0078] Figure 3 This is a schematic diagram of the structure of an intelligent coaching device for financial network security provided by another embodiment of the present invention. Figure 3 As shown, the device includes:

[0079] An acquisition module 310 is used to construct a financial cybersecurity training ground and obtain learning data of trainees in the financial cybersecurity training ground;

[0080] An analysis module 320 is configured to determine the student type of the student based on the learning data;

[0081] The push module 330 is used to push subsequent learning content that matches the student type to the student.

[0082] The intelligent coaching device for financial network security provided by the embodiment of the present invention can execute the intelligent coaching method for financial network security provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0083] Optionally, the analysis module includes:

[0084] A feature extraction unit, configured to extract features of at least two dimensions from the learning data to obtain a feature matrix;

[0085] a feature substitution unit, configured to substitute the feature matrix into an intelligent coaching model to obtain a prediction value indicating whether the student is an excellent student; the intelligent coaching model is a logistic regression model constructed by using logistic regression as a supervised learning algorithm;

[0086] A first analyzing unit is configured to determine that the student type of the student is an excellent student if the predicted value is not less than a preset threshold;

[0087] The second analysis unit is used to determine the student type of the student as a non-excellent student if the predicted value is less than a preset threshold.

[0088] Optionally, the process of building the intelligent coaching model is as follows:

[0089] Use historical learning data as training set data, and use the learning status of students corresponding to historical learning data as known labels

[0090] Using logistic regression as a supervised learning algorithm, fitting the training set data and the known labels to obtain a model parameter vector;

[0091] An intelligent coaching model is constructed according to the model parameter vector.

[0092] Optionally, the formula of the intelligent coaching model is as follows:

[0093]

[0094] Among them, h θ (X) is the predicted value, θ is the model parameter vector determined by pre-fitting, X is the feature matrix, e is the base of the natural logarithm, and T refers to the transpose operation of the model parameter vector.

[0095] Optionally, the push module 330 includes:

[0096] A first push unit is configured to push cybersecurity projects, cybersecurity competitions, and advanced unlearned resources similar to those already learned by the student to the student if the student type is an outstanding student;

[0097] The second push unit is used to push relevant exercises, relevant practice projects, and basic unlearned resources similar to the resources the student has learned to the student if the student type is a non-excellent student.

[0098] Optionally, the device further includes a similarity calculation module for determining, for any unlearned resource, the cosine similarity between the feature vector of the unlearned resource and the feature vector of the learned resource; and determining whether the unlearned resource is similar to the learned resource based on the cosine similarity.

[0099] Optionally, the learning data includes learning logs and behavior data.

[0100] The intelligent coaching device for financial network security further described can also execute the intelligent coaching method for financial network security provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0101] Figure 4 A schematic diagram of the structure of an electronic device 40 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0102] like Figure 4As shown, the electronic device 40 includes at least one processor 41, and a memory connected to the at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., wherein the memory stores a computer program that can be executed by the at least one processor, and the processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 to the random access memory (RAM) 43. Various programs and data required for the operation of the electronic device 40 can also be stored in the RAM 43. The processor 41, ROM 42 and RAM 43 are connected to each other via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0103] Multiple components in the electronic device 40 are connected to the I / O interface 45, including an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a magnetic disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0104] Processor 41 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any other suitable processors, controllers, microcontrollers, etc. Processor 41 executes the various methods and processes described above, such as the intelligent coaching method for financial cybersecurity.

[0105] In some embodiments, the intelligent coaching method for financial cybersecurity can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the intelligent coaching method for financial cybersecurity described above can be performed. Alternatively, in other embodiments, processor 41 can be configured to execute the intelligent coaching method for financial cybersecurity in any other suitable manner (e.g., via firmware).

[0106] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0107] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0108] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0109] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0110] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0111] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0112] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0113] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. An intelligent coaching method for financial network security, characterized in that: The method comprises: Constructing a financial cybersecurity training ground and obtaining students' learning data in the training ground; determining the student type of the student according to the learning data; Push subsequent learning content that matches the student type to the student.

2. The method according to claim 1, characterized in that Determining the student type of the student according to the learning data includes: Performing feature extraction on the learning data in at least two dimensions to obtain a feature matrix; Substituting the feature matrix into an intelligent coaching model to obtain a prediction value of whether the student is an excellent student; the intelligent coaching model is a logistic regression model constructed by using logistic regression as a supervised learning algorithm; If the predicted value is not less than the preset threshold, the student type of the student is determined to be an excellent student; If the predicted value is less than the preset threshold, the student type of the student is determined to be a non-excellent student.

3. The method according to claim 2, characterized in that The construction process of the intelligent coaching model is as follows: Use historical learning data as training set data, and use the learning status of students corresponding to historical learning data as known labels Using logistic regression as a supervised learning algorithm, fitting the training set data and the known labels to obtain a model parameter vector; An intelligent coaching model is constructed according to the model parameter vector.

4. The method according to claim 3, characterized in that The formula of the intelligent coaching model is as follows: Among them, h θ (X) is the predicted value, θ is the model parameter vector determined by pre-fitting, X is the feature matrix, e is the base of the natural logarithm, and T refers to the transpose operation of the model parameter vector.

5. The method according to claim 2, characterized in that The pushing of subsequent learning content matching the student type to the student includes: If the student type is an outstanding student, network security projects, network security competitions, and advanced unlearned resources similar to those already learned by the student will be pushed to the student; If the student type is a non-excellent student, relevant exercises, relevant practical projects, and basic unlearned resources similar to the resources the student has already learned will be pushed to the student.

6. The method according to claim 5, characterized in that Before pushing subsequent learning content matching the student type to the student, the method further includes: For any unlearned resource, determining the cosine similarity between a feature vector of the unlearned resource and a feature vector of a learned resource; Determine whether the unlearned resource is similar to the learned resource according to the cosine similarity.

7. The method according to claim 1, characterized in that The learning data includes learning logs and behavior data.

8. An intelligent coaching device for financial network security, characterized in that: The device comprises: An acquisition module is used to build a financial cybersecurity shooting range and obtain the learning data of trainees in the financial cybersecurity shooting range; An analysis module, configured to determine the student type of the student based on the learning data; The push module is used to push subsequent learning content that matches the student type to the student.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the intelligent coaching method for financial network security according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the intelligent coaching method for financial network security according to any one of claims 1 to 7 when executed.

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