Multimedia teaching safety detection method
By integrating machine learning models into multimedia teaching equipment for security detection and regularly updating vulnerability information, the security problems of multimedia teaching equipment are solved, the safety of equipment and the accuracy of detection are improved, and the smooth progress of teaching activities are ensured.
Patent Information
- Application Number
- CN202510343477.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-21
- Publication Date
- 2025-08-01
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Multimedia teaching equipment has many security problems such as remote control interface security vulnerabilities, data injection attacks, weak passwords for administrator accounts and remote backdoors, and lacks effective detection methods, which leads to the equipment being vulnerable to network attacks, affecting the smooth progress of teaching activities.
Machine learning models are used for feature extraction and training, integrated into multimedia teaching equipment for security detection, and regularly updated vulnerability information database to optimize the model and improve detection accuracy.
Effectively reduce the risk of malicious attacks on multimedia teaching equipment, ensure the smooth progress of teaching activities, provide security guarantees, and improve the accuracy and generalization capabilities of the model.
Smart Images
Figure CN120408628A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to a multimedia teaching security detection method, belonging to the technical field of multimedia teaching. Background Art
[0002] With the rapid development of information technology, multimedia technology has been increasingly widely applied in the field of teaching. Multimedia teaching devices are widely used in school education, distance training, enterprise training and other fields. However, there are various security problems with multimedia teaching devices.
[0003] For remote interface control, technicians can solve equipment failures through remote control means, but remote control interfaces lacking security design will be invaded by viruses or malware. In addition, the widespread application of remote control technology also makes the device more vulnerable to network attacks, such as SQL injection, command injection, etc.
[0004] For remote data injection, SQL injection attacks allow attackers to obtain sensitive data or control the server through malicious SQL statements. In addition, command injection attacks may also execute arbitrary commands through insecure remote interfaces.
[0005] For administrator accounts and passwords, the initial passwords of many online education platforms are set weakly and are easily vulnerable to brute-force cracking or credential stuffing attacks. In addition, if the administrator uses a weak password or does not change the password regularly, it will also increase the risk of being attacked.
[0006] For remote backdoors, some malware can achieve remote control of the device through backdoor modules. In a multimedia teaching environment, the existence of a backdoor may lead to data leakage, the system being infected by malware, or being remotely controlled by hackers.
[0007] Effectively detecting and reasonably solving various security problems widely existing in multimedia teaching devices is of great significance for ensuring the security of multimedia devices and the smooth progress of multimedia teaching activities. Summary of the Invention
[0008] The purpose of the present invention is to provide a multimedia teaching security detection method, which can realize the automatic security detection of multimedia teaching devices and reduce the risk of multimedia teaching devices being attacked.
[0009] To achieve the above purpose, the present invention provides the following technical solutions: In a first aspect, the present invention provides a multimedia teaching security detection method, including: Obtaining relevant data of different security problems of multimedia teaching devices and performing feature extraction to obtain key features of different security problems; Screen machine learning models for training according to the key features of different security issues, optimize the parameters of the trained machine learning models using the relevant data of different security issues, evaluate the performance of the optimized machine learning models through experiments on different security issues, and obtain the machine learning models with the best performance for different security issues; Integrate the machine learning models with the best performance for different security issues into the multimedia teaching equipment to conduct security detection for different security issues; It also includes: Regularly update the vulnerability information database, and retrain or fine-tune each machine learning model according to the updated vulnerability information.
[0010] Combined with the first aspect, further, the security issues of the multimedia teaching equipment include remote interface control security issues, data injection security issues, administrator account and password security issues, and remote backdoor security issues; Among them, the remote interface control security issues include unencrypted data transmission, denial of service attacks, and software vulnerabilities; The data injection security issues include malicious payload identification and abnormal application programming interface call sequences; The administrator account and password security issues include brute force cracking, weak passwords, and unchanged default credentials; The remote backdoor security issues include abnormal process behaviors, abnormal network communications, and abuse of privilege escalation.
[0011] Combined with the first aspect, further, the key features of the remote interface control security issues include the protocol type of network packets, source / destination port numbers, response time, and device information; The key features of the data injection security issues include malicious payload samples, application programming interface call sequences, system call sequences, file system operations, registry operations, network connections, and memory operation information; The key features of the administrator account and password security issues include the user names attempting to log in, source IP addresses, login timestamps, and login results; The key features of the remote backdoor security issues include process metadata, process resource usage, process system interaction behaviors, process relationship data, and time series data.
[0012] In combination with the first aspect, further, when screening machine learning models for training according to the key features of different security issues, the machine learning models include logistic regression models, support vector machine models, decision tree models, random forest models, convolutional neural network models, recurrent neural network models, and Transformer models; when optimizing the parameters of the trained machine learning models using the relevant data of different security issues, the parameter optimization method includes the cross-validation method; when evaluating the performance of the optimized machine learning models through different security issue experiments, the performance evaluation metrics include accuracy, recall rate, and F1 score.
[0013] In combination with the first aspect, further, an automated tool for vulnerability scanning and verification is also deployed in the multimedia teaching device.
[0014] In combination with the first aspect, further, regularly updating the vulnerability information database and retraining or fine-tuning each machine learning model according to the updated vulnerability information includes: Regularly obtaining the latest vulnerability information to update the vulnerability information database; According to the updated vulnerability information, retraining or fine-tuning each machine learning model; Integrating the retrained or fine-tuned machine learning models into the multimedia teaching device to perform security detection for new security issues.
[0015] In a second aspect, the present invention provides a multimedia teaching security detection system, including: A feature extraction module for obtaining relevant data of different security issues of the multimedia teaching device and performing feature extraction to obtain the key features of different security issues; A model training module for screening machine learning models for training according to the key features of different security issues, optimizing the parameters of the trained machine learning models using the relevant data of different security issues, and evaluating the performance of the optimized machine learning models through different security issue experiments to obtain the machine learning models with the best performance for different security issues; A model deployment module for integrating the machine learning models with the best performance for different security issues into the multimedia teaching device to perform security detection for different security issues; It further includes: A model update module for regularly updating the vulnerability information database and retraining or fine-tuning each machine learning model according to the updated vulnerability information.
[0016] In a third aspect, the present invention provides a computer device, including: A storage medium for storing a computer program; A processor for executing the computer program to implement the multimedia teaching security detection method according to any one of the first aspect.
[0017] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, it implements the multimedia teaching security detection method according to any one of the first aspect.
[0018] In a fifth aspect, the present invention provides a computer program product including a computer program, and when the computer program is executed by a processor, it implements the multimedia teaching security detection method according to any one of the first aspect.
[0019] Compared with the prior art, the beneficial effects of the present invention are: The multimedia teaching security detection method provided by the present invention collects relevant data for feature extraction for different security problems of multimedia teaching devices, selects appropriate machine learning models for training and parameter optimization, obtains the machine learning models with the best performance for different security problems through experimental evaluation, and integrates these machine learning models into the multimedia teaching devices, which can perform security detection for different security problems, effectively reduce the risk of malicious attacks on multimedia teaching devices, ensure the smooth progress of teaching activities, and provide security guarantees for educational informatization. Regularly update the vulnerability information database, and retrain or fine-tune each machine learning model according to the updated vulnerability information, which can improve the accuracy and generalization ability of each machine learning model to cope with newly emerging security problems. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] Figure 1 is a flowchart of the multimedia teaching security detection method provided by an embodiment of the present invention; Figure 2 is a flowchart of the security detection for the problem of unencrypted data transmission in remote interface control provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0021] The technical solutions of the present application will be further described in detail below in conjunction with the specific embodiments.
[0022] The embodiments of the present application will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions from beginning to end. The embodiments described below with reference to the drawings are exemplary and are only used to explain the present application and should not be construed as a limitation of the present application. Without conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.
[0023] The embodiments of the present application provide a multimedia teaching security detection method, including: Obtain relevant data on different security issues of multimedia teaching equipment and perform feature extraction to obtain the key features of different security issues; Select a machine learning model for training according to the key features of different security issues, optimize the parameters of the trained machine learning model using the relevant data of different security issues, and evaluate the performance of the optimized machine learning model through experiments on different security issues to obtain the machine learning model with the best performance for different security issues; Integrate the machine learning model with the best performance for different security issues into the multimedia teaching equipment to perform security detection for different security issues; It also includes: Regularly update the vulnerability information database, and retrain or fine-tune each machine learning model according to the updated vulnerability information.
[0024] The multimedia teaching security detection method provided by the embodiments of this application collects relevant data for different security issues of multimedia teaching equipment for feature extraction, selects a suitable machine learning model for training and parameter optimization, obtains the machine learning model with the best performance for different security issues through experimental evaluation, and integrates these machine learning models into the multimedia teaching equipment, which can perform security detection for different security issues, effectively reduce the risk of malicious attacks on multimedia teaching equipment, ensure the smooth progress of teaching activities, and provide security guarantees for educational informatization. Regularly updating the vulnerability information database and retraining or fine-tuning each machine learning model according to the updated vulnerability information can improve the accuracy and generalization ability of each machine learning model to cope with newly emerging security issues.
[0025] In a possible embodiment, as Figure 1 shown, the security issues of multimedia teaching equipment include remote interface control security issues, data injection security issues, administrator account and password security issues, and remote backdoor security issues.
[0026] Specifically, as Figure 1 shown, the remote interface control security issues include unencrypted data transmission, denial of service attacks, and software vulnerabilities. The data injection security issues include malicious payload identification and abnormal application programming interface call sequences. The administrator account and password security issues include brute force cracking, weak passwords, and unchanged default credentials. The remote backdoor security issues include abnormal process behavior, abnormal network communication, and abuse of privilege escalation.
[0027] In this embodiment, the key features of remote interface control security issues include the protocol type of network data packets, source / destination port numbers, response time, and device information. The key features of data injection security issues include malicious payload samples, application programming interface call sequences, system call sequences, file system operations, registry operations, network connections, and memory operation information. The key features of administrator account and password security issues include the user names attempting to log in, source IP addresses, login timestamps, and login results. The key features of remote backdoor security issues include process metadata, process resource usage, process system interaction behaviors, process relationship data, and time series data.
[0028] The multimedia teaching security detection method provided in this embodiment starts from multiple perspectives and comprehensively considers factors such as device architecture, data processing flow, user interaction methods, and external threat environments to comprehensively and systematically analyze security issues in multimedia teaching devices, including remote interface control security issues, data injection security issues, administrator account and password security issues, and remote backdoor security issues. For different security issues, relevant data is collected and key features are extracted. A suitable machine learning model is selected for training, and the collected data is used to train and validate the model, and the model parameters are optimized. The performance of the model is evaluated through experiments, and according to the experimental results, the model with the best performance is selected. The trained model is integrated into the multimedia teaching software to achieve an automated security detection function. The vulnerability information database is updated regularly to cope with newly emerging security threats.
[0029] In a possible embodiment, as Figure 2 shown, when screening machine learning models for training according to the key features of different security issues, the machine learning models include logistic regression models, support vector machine models, decision tree models, random forest models, convolutional neural network models, recurrent neural network models, and Transformer models.
[0030] In this embodiment, when optimizing the parameters of the trained machine learning model using the relevant data of different security issues, the parameter optimization method includes the cross-validation method.
[0031] Specifically, as Figure 2 shown, when evaluating the performance of the optimized machine learning model through experiments on different security issues, the performance evaluation metrics include accuracy, recall rate, and F1 score.
[0032] The multimedia teaching security detection method provided in the embodiments of this application uses machine learning technology and can accurately identify and predict various potential security threats. By analyzing a large amount of data and learning normal and abnormal behavior patterns, the detection model can be continuously optimized to adapt to newly emerging attack methods. This self-learning and adaptation ability significantly improves the accuracy of detection and reduces the situations of false positives and false negatives.
[0033] In a possible embodiment, an automated tool for vulnerability scanning and verification is also deployed in the multimedia teaching device.
[0034] Specifically, the automated tools include Nmap, OpenVAS, Metasploit, etc., which are used for vulnerability scanning and verification.
[0035] In a possible embodiment, the vulnerability information database is updated regularly, and according to the updated vulnerability information, retraining or fine-tuning of each machine learning model includes: Regularly obtain the latest vulnerability information to update the vulnerability information database; According to the updated vulnerability information, retrain or fine-tune each machine learning model; Integrate the retrained or fine-tuned machine learning models into the multimedia teaching device to perform security detection for new security issues.
[0036] In this embodiment, the latest vulnerability information is regularly obtained from channels such as public vulnerability libraries (CVE, NVD), vendor vulnerability announcements, and security research reports. Update the vulnerability information database, including vulnerability descriptions, affected software versions, CVSS scores, etc. According to the new vulnerability information, retrain or fine-tune the model to improve the accuracy and generalization ability of the model. Deploy the updated model to the multimedia teaching software to address newly emerging security threats.
[0037] The multimedia teaching security detection method provided by the embodiments of the present application has the ability to monitor the device status in real time and can quickly respond when a security threat is detected. This immediate threat response mechanism greatly improves the processing speed of security incidents and reduces potential damage.
[0038] The embodiments of the present application provide a multimedia teaching security detection system, including: A feature extraction module, configured to obtain relevant data of different security issues of the multimedia teaching device and perform feature extraction to obtain key features of different security issues; A model training module, configured to screen machine learning models for training according to the key features of different security issues, optimize the parameters of the trained machine learning models using the relevant data of different security issues, and evaluate the performance of the optimized machine learning models through experiments on different security issues to obtain the machine learning models with the best performance for different security issues; A model deployment module, configured to integrate the machine learning models with the best performance for different security issues into the multimedia teaching device to perform security detection for different security issues; It further includes: A model update module, which is used to regularly update the vulnerability information database, and retrain or fine-tune each machine learning model according to the updated vulnerability information.
[0039] The multimedia teaching security detection system provided in this embodiment can execute the multimedia teaching security detection method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects for executing the method.
[0040] An embodiment of this application provides a computer device, including: A storage medium for storing a computer program; A processor for executing the computer program to implement the multimedia teaching security detection method provided in any embodiment of this application.
[0041] An embodiment of this application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the multimedia teaching security detection method provided in any embodiment of this application.
[0042] An embodiment of this application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the multimedia teaching security detection method provided in any embodiment of this application.
[0043] Those skilled in the art should understand that the embodiments of this application can be provided as a method, a system, or a computer program product. Therefore, this application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.
[0044] This application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of this application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one or more flows or multiple flows and / or blocks Figure 1 one or more blocks or multiple blocks.
[0045] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction means that implements the functions specified in one or more of the processes and / or blocks Figure 1 one or more of the processes and / or blocks Figure 1 specified in the function.
[0046] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more of the processes and / or blocks Figure 1 one or more of the processes and / or blocks Figure 1 specified in the steps of the function.
[0047] The foregoing are only the preferred embodiments of the present application. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present application, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present application.
Claims
1. A multimedia teaching safety detection method, characterized in that, Including: Obtaining relevant data on different security issues of multimedia teaching equipment and performing feature extraction to obtain the key features of different security issues; Screening machine learning models according to the key features of different security issues for training, optimizing the parameters of the trained machine learning models using the relevant data of different security issues, and evaluating the performance of the optimized machine learning models through different security issue experiments to obtain the machine learning models with the best performance for different security issues; Integrating the machine learning models with the best performance for different security issues into the multimedia teaching equipment to perform security detection for different security issues; Also including: Regularly updating the vulnerability information database, and retraining or fine-tuning each machine learning model according to the updated vulnerability information.
2. The multimedia teaching safety detection method according to claim 1, wherein The security issues of the multimedia teaching equipment include remote interface control security issues, data injection security issues, administrator account and password security issues, and remote backdoor security issues; Among them, the remote interface control security issues include unencrypted data transmission, denial-of-service attacks, and software vulnerabilities; The data injection security issues include malicious payload identification and abnormal application programming interface call sequences; The administrator account and password security issues include brute force cracking, weak passwords, and unchanged default credentials; The remote backdoor security issues include abnormal process behaviors, abnormal network communications, and privilege escalation abuses.
3. The multimedia teaching security detection method according to claim 2, characterized in that, The key features of the remote interface control security issues include the protocol type of network packets, source / destination port numbers, response time, and device information; The key features of the data injection security issues include malicious payload samples, application programming interface call sequences, system call sequences, file system operations, registry operations, network connections, and memory operation information; The key features of the administrator account and password security issues include the username attempting to log in, source IP address, login timestamp, and login result; The key features of the remote backdoor security issues include process metadata, process resource usage, process system interaction behaviors, process relationship data, and time series data.
4. The multimedia teaching security detection method according to claim 1, wherein When screening machine learning models according to the key features of different security issues for training, the machine learning models include logistic regression models, support vector machine models, decision tree models, random forest models, convolutional neural network models, recurrent neural network models, and Transformer models; when optimizing the parameters of the trained machine learning models using the relevant data of different security issues, the parameter optimization method includes the cross-validation method; When evaluating the performance of the optimized machine learning models through different security issue experiments, the performance evaluation metrics include accuracy, recall rate, and F1 score.
5. The multimedia teaching safety detection method according to claim 1, characterized in that Automation tools for vulnerability scanning and verification are also deployed in the multimedia teaching equipment.
6. The multimedia teaching security detection method according to claim 1, wherein Regularly updating the vulnerability information database and retraining or fine-tuning each machine learning model according to the updated vulnerability information includes: Regularly obtaining the latest vulnerability information to update the vulnerability information database; Retraining or fine-tuning each machine learning model according to the updated vulnerability information; Re-integrating each retrained or fine-tuned machine learning model into the multimedia teaching equipment to perform security detection for new security issues.
7. A multimedia teaching security detection system, characterized in that, Including: A feature extraction module, configured to obtain relevant data of different security issues of the multimedia teaching device and perform feature extraction to obtain key features of different security issues; A model training module, configured to screen a machine learning model for training according to the key features of different security issues, optimize the parameters of the trained machine learning model by using the relevant data of different security issues, and evaluate the performance of the optimized machine learning model through experiments on different security issues, so as to obtain the machine learning model with the best performance for different security issues; A model deployment module, configured to integrate the machine learning model with the best performance for different security issues into the multimedia teaching device to perform security detection for different security issues; It further includes: A model update module, configured to regularly update the vulnerability information database, and retrain or fine-tune each machine learning model according to the updated vulnerability information.
8. A computer device, characterized in that, It includes: A storage medium, configured to store a computer program; A processor, configured to execute the computer program to implement the multimedia teaching security detection method according to any one of claims 1 to 6.
9. 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 multimedia teaching security detection method according to any one of claims 1 to 6.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the multimedia teaching security detection method according to any one of claims 1 to 6.