Communication data leakage protection method in cloud computing environment

Abstract

The invention discloses a communication data leakage protection method in a cloud computing environment, and relates to the technical field of cloud computing security, and the method comprises the steps: obtaining a communication instruction containing a to-be-executed communication task and a corresponding communication link of a cloud computing platform, and carrying out the data leakage risk inspection result according to the task, if yes, real-time link protection parameters are collected to establish a protection mechanism, a risk prediction channel is introduced to be combined with the mechanism to predict a communication data leakage risk coefficient of the to-be-executed task, and then optimization adjustment is performed on the protection mechanism according to the risk coefficient, a threshold value and different constraint step lengths to construct an optimization mechanism; and finally, protecting and optimizing the communication link to obtain an optimized link, and executing a task in combination with a cloud computing platform. The technical problem that in the prior art, communication data are prone to being leaked and threatened due to the fact that the protection performance of the communication link in the cloud computing environment is poor is solved, and the technical effects of improving the protection performance of the communication link in the cloud computing environment and reducing the risk of data leakage are achieved.

Description

Technical Field

[0001] The present application relates to the field of cloud computing security technology, and in particular to a method for preventing communication data leakage in a cloud computing environment. Background Art

[0002] With the widespread application of cloud computing, massive amounts of data are transmitted and processed in distributed, multi-tenant cloud environments. Traditional protection methods that rely on static security policies and manual risk assessments are difficult to keep up with the ever-changing resource scheduling and data flow requirements in cloud platforms. In addition, due to the complex interactions across regions and systems and the requirements for real-time and high availability, sensitive data faces a higher threat of leakage during transmission. It is urgent to establish a technology that can assess risks in real time and dynamically adjust protection strategies to effectively enhance the security of communication links in cloud computing environments and prevent data leakage accidents.

[0003] At present, there is a technical problem in the relevant technologies that the communication links in the cloud computing environment are poorly protected, making the communication data vulnerable to leakage threats. Summary of the invention

[0004] The present application provides a method for preventing leakage of communication data in a cloud computing environment, thereby solving the technical problem in the prior art that the communication link in a cloud computing environment has poor protection, resulting in the communication data being vulnerable to leakage threats.

[0005] The present application provides a communication data leakage protection method in a cloud computing environment, comprising:

[0006] Obtain a communication instruction of a cloud computing platform, wherein the communication instruction includes a communication task to be executed and a communication link corresponding to the communication task to be executed; perform a data leakage risk test according to the communication task to be executed, and obtain a task leakage risk test result; when the task leakage risk test result is passed, collect real-time protection parameters of the communication link, and establish a communication protection mechanism; introduce a communication leakage risk prediction channel, and perform a communication data leakage risk prediction on the communication task to be executed in combination with the communication protection mechanism to obtain a communication leakage risk coefficient; based on the communication leakage risk coefficient, optimize the communication protection mechanism according to a communication leakage risk threshold, a first constraint step of protection adjustment, and a second constraint step of protection adjustment, and construct an optimized communication protection mechanism, wherein the first constraint step of protection adjustment is greater than the second constraint step of protection adjustment; perform protection optimization on the communication link according to the optimized communication protection mechanism to obtain an optimized communication link, and execute the communication task to be executed in combination with the cloud computing platform.

[0007] A communication data leakage protection method in a cloud computing environment proposed in this application is to first obtain the communication instructions of the cloud computing platform containing the communication tasks to be executed and the corresponding communication links, and obtain the results of the data leakage risk test according to the tasks. If the test passes, the real-time protection parameters of the link are collected to establish a protection mechanism, and a risk prediction channel is introduced to combine with the mechanism to predict the communication data leakage risk coefficient of the task to be executed. Then, the protection mechanism is optimized and adjusted according to the risk coefficient, threshold and different constraint step sizes to construct an optimization mechanism. Finally, the communication link protection is optimized according to the optimization mechanism to obtain an optimized link, and the task is executed in combination with the cloud computing platform, thereby achieving the technical effect of improving the protection performance of the communication link in the cloud computing environment and reducing the risk of data leakage. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0009] Figure 1 A flow chart of a communication data leakage protection method in a cloud computing environment provided by an embodiment of the present application;

[0010] Figure 2 A schematic diagram of the inspection result output process of a communication data leakage protection method in a cloud computing environment provided in an embodiment of the present application. DETAILED DESCRIPTION

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0012] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0013] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server 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 modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0014] The present application embodiment provides a method for preventing communication data leakage in a cloud computing environment, such as Figure 1 As shown, the method includes:

[0015] Step S100, obtain the communication instruction of the cloud computing platform, wherein the communication instruction includes the communication task to be executed and the communication link corresponding to the communication task to be executed. Specifically, in the architecture of the cloud computing platform, the instruction management and distribution subsystem is connected to each core component through a high-speed and reliable internal network (such as Ethernet, Infiniband, etc.), and a redundant design is used to ensure stability. The system monitors the communication instruction requests from various parts of the platform in real time through a specific listening port and a protocol parsing module, parses the data according to the predefined instruction protocol format (such as XML, JSON, etc.), extracts the key information of the communication task to be executed and the corresponding communication link, and then verifies the format, integrity (through checksum comparison) and source legitimacy of the instruction. If there is an error, it will be fed back to the sender or retransmitted, and after confirmation, it will be passed to the subsequent process, providing a basis for communication data leakage protection, ensuring the security and stability of the communication operation of the cloud computing platform, promoting its development and application, meeting social needs, and helping users achieve business and data management goals.

[0016] Step S200, perform data leakage risk inspection according to the communication task to be executed, and obtain the task leakage risk inspection result. Specifically, deploy a task leakage risk inspection system in the cloud computing platform security architecture, integrate technical tools such as encryption technology analysis, data access rights audit, network vulnerability scanning and machine learning abnormal behavior detection, connect with other components of the platform and rely on regularly updated security databases to ensure accuracy. For the communication task to be executed, use data acquisition tools to mine its encryption parameters and information, covering encryption algorithms, key conditions and encryption status, etc., and preliminarily evaluate the encryption security. Then, input this information into the task leakage risk prediction model constructed based on historical data and machine learning algorithms, calculate the task leakage risk coefficient, the numerical value reflects the risk level, and analyze multiple factors to ensure that the assessment is objective and accurate. Finally, input the coefficient into the leakage risk inspection model containing risk judgment rules and threshold systems, and obtain the task leakage risk inspection result based on the comparison between the coefficient and the threshold. If it passes, the risk is acceptable, otherwise measures need to be taken to repair and optimize. This result provides risk information for platform security management and users, ensures the security of communication tasks, improves platform security, reliability and user trust, promotes the development of cloud computing technology applications, and provides support for information security.

[0017] In one possible implementation, Figure 2 According to the communication task to be executed, data leakage risk inspection is performed to obtain the task leakage risk inspection result, and step S200 further includes step S210, constructing a task leakage risk inspection channel, wherein the task leakage risk inspection channel includes a task leakage risk prediction model and a leakage risk inspection model. Specifically, at the security architecture layer of the cloud computing platform, modeling software and big data analysis tools are used to construct a task leakage risk inspection channel, which is constructed based on the mining and analysis of a large amount of historical communication task data to identify key factors and characteristic patterns closely related to data leakage risks, laying the foundation for subsequent risk assessment. The task leakage risk prediction model adopts machine learning algorithms such as the neural network architecture in deep learning. By learning the attributes of the task type, data volume, sensitivity, execution time and frequency in the historical data, as well as whether data leakage occurs, it continuously adjusts the internal weights and parameters, so that it can accurately predict the potential data leakage risk level based on the input task characteristics, and is highly adaptable and accurate; the leakage risk verification model is based on industry standards, security best practices, and the cloud computing platform's own security policies and risk tolerance. For example, it sets clear risk judgment rules and thresholds for determining different acceptable risk ranges based on the confidentiality level of task data. When the input risk coefficient exceeds the corresponding threshold, the task is judged to be high risk, otherwise the risk is controllable. The final task leakage risk verification result is output to ensure the scientificity and reliability of the entire verification process, and to build an effective security guarantee mechanism for cloud computing platform communication tasks.

[0018] Step S220, collect the encryption parameters of the communication task to be executed, and obtain the task encryption scheme. Specifically, first clarify the data source (such as database, storage system, etc.) and transmission path involved in the communication task to be executed, and determine the scope of encryption parameter collection, such as the storage encryption method of the database, the encryption protocol of the network transmission, etc. Then deploy lightweight, high-performance collection tools and agents at the key nodes of the cloud computing platform (such as switches, servers, etc.), which are deeply integrated with the system kernel, network protocol stack, etc., and use deep packet inspection and other technologies to monitor encryption-related events in real time and extract parameters. Then use the parsing engine to deeply analyze the original data, standardize the extracted parameters, and unify the naming specifications, data types and value ranges. Finally, the standardized parameters are integrated into a complete task encryption scheme according to the logical structure, covering the data source, transmission, authentication authorization and storage encryption, providing accurate input for risk assessment, ensuring the data security of the platform communication tasks, and promoting the development of cloud computing technology.

[0019] Step S230, input the task encryption scheme into the task leakage risk prediction model to obtain the task leakage risk coefficient. Specifically, set up the operating environment of the task leakage risk prediction model, load the parameters obtained after in-depth analysis and learning of a large amount of historical communication task data and corresponding data leakage events, and verify its integrity and accuracy. Then adapt and convert the input task encryption scheme, such as converting text data into numerical format using unique hot encoding, normalizing numerical data, parsing and simplifying complex structure data and arranging them into input vectors according to model requirements. Then input it into the model, and the model extracts high-level features related to the risk through convolution, pooling, and fully connected layers, and performs nonlinear transformation and calculation based on neuron connection weights, activation functions, etc., to obtain preliminary evaluation results. After that, a conversion algorithm based on probability distribution is used to generate a task leakage risk coefficient, such as the probability value output by logistic regression or the comprehensive calculation value of a multi-classification decision tree model, with a value of 0 to 1 reflecting the level of risk. Finally, the risk factor is output and stored in a specific structure or transmitted to the security management system via a network interface for visual display, providing support for security decision-making, helping to prevent data leakage, improve the platform's security protection level and user trust, and promote the application and development of cloud computing technology. In addition, the model needs to be continuously optimized to respond to security challenges, ensure platform and data security, and promote the construction of a digital ecosystem.

[0020] Step S240, input the task leakage risk coefficient into the leakage risk verification model, and output the task leakage risk verification result. Specifically, ensure that the leakage risk verification model constructed based on the cloud computing platform security policy, past data leakage incident analysis and industry standards is in an operational state, check and calibrate the risk threshold, judgment rules and related parameters set internally according to the data type, sensitivity and platform risk tolerance. Then, perform data verification on the risk coefficient received from the task leakage risk prediction model to confirm that the format and value range meet the requirements. If there is an error, an alarm will be issued and a correction will be notified. Only when there is no error will the risk assessment be entered. After that, the risk coefficient will be compared with the preset threshold. If it is less than the threshold, it will be judged as "passed", reflecting that the risk is acceptable and the task can be carried out as planned; if it is greater than or equal to the threshold, it will be judged as "failed", indicating that the risk is high and measures such as optimizing the encryption scheme need to be taken. The model will also provide suggestions according to the situation. Finally, the model outputs the results in a clear manner, which may be text labels or detailed reports, and feeds back to the security management system. If it fails, the system will trigger a response process, including notifying personnel, recording events, adjusting configurations, etc. In actual applications, the model needs to be continuously optimized to adapt to changes, ensure platform and data security, and promote technological development and ecological construction.

[0021] In a possible implementation, a task leakage risk verification channel is constructed, wherein the task leakage risk verification channel includes a task leakage risk prediction model and a leakage risk verification model, and step S210 further includes step S211, taking the task encryption scheme sample set as input information and the task leakage risk sample set as output information, training the fully connected neural network, and obtaining the leakage risk prediction loss coefficient after each predetermined number of trainings. Specifically, historical communication task data of different fields, scenarios and periods are collected, and the task encryption scheme (including encryption algorithm, key information, encryption mode, etc.) and the corresponding actual data leakage situation (quantified as risk level or value) are recorded to form a sample set. Then, the sample data is preprocessed by cleaning, denoising and normalization to ensure that the data is accurate and complete and the magnitude of each feature is unified. Then, a fully connected neural network is constructed, whose input layer neurons correspond to the number of encryption scheme features, the hidden layer sets multiple layers and the corresponding number of neurons according to the complexity of the problem, and the output layer determines the number of neurons and the activation function according to the risk result representation method. The encryption scheme sample set is then input into the network, and the predicted leakage risk value is obtained through weighted summation of neurons and nonlinear transformation of activation functions. After each predetermined number of trainings (such as 100 or 500 times), the error between the predicted value and the true value is calculated using loss functions such as mean square error or cross entropy, thereby obtaining the leakage risk prediction loss coefficient. This is used to monitor the learning progress and performance of the model, and provide a basis for subsequent optimization and adjustment to cope with data leakage risks in cloud computing platform communication tasks, ensure data security and platform protection levels, promote the application and development of cloud computing technology, and assist in digital transformation and security ecosystem construction.

[0022] Step S212, if the leakage risk prediction loss coefficient is less than the leakage risk prediction loss threshold, the task leakage risk prediction model is generated. Specifically, after the fully connected neural network completes each predetermined number of rounds of training, the leakage risk prediction loss coefficient is obtained, and it is compared with the leakage risk prediction loss threshold set based on the model performance expectations and business needs. If the coefficient is less than the threshold, it means that the model has achieved the expected performance. At this time, a task leakage risk prediction model is generated, and its neuron connection weights, bias parameters and architecture information are saved to facilitate leakage risk assessment of new communication tasks. In addition, an independent test data set can be used to verify the model. If performance problems are found, such as large prediction deviations on specific tasks, the model can be improved by adjusting the network structure, adopting new training algorithms, and increasing training data. Optimization measures such as improving the generalization ability and accuracy of the model can make it better adapt to business scenarios, provide technical support for cloud computing platform communication data leakage protection, ensure data security, promote the safe development of cloud computing technology, meet the security needs of digital business, and help the high-quality development of the economy and society and the digital transformation of the industry.

[0023] Step S213, construct the leakage risk inspection model, wherein the leakage risk inspection model includes a leakage risk inspection operator, and the leakage risk inspection operator includes: if the task leakage risk coefficient is less than the task leakage risk threshold, the task leakage risk inspection result is passed; if the task leakage risk coefficient is greater than or equal to the task leakage risk threshold, the task leakage risk inspection result is failed. Specifically, a leakage risk inspection model framework is constructed to integrate it into the cloud computing platform security assessment system as an independent module, focusing on interface compatibility and data interaction methods with other modules. Then, the task leakage risk threshold is determined according to the platform task nature, data sensitivity and security policy, such as setting a low threshold for sensitive data tasks and appropriately relaxing the threshold for ordinary business data tasks to flexibly adapt to different risk assessment needs. Then, a leakage risk inspection operator with the comparison of risk coefficient and threshold as the core logic is constructed. If it is less than the threshold, the inspection result is judged as "passed", and the task can proceed normally; if it is greater than or equal to the threshold, it is judged as "failed", and the emergency mechanism needs to be activated to strengthen security protection. After that, the operator is integrated into the model and fully tested, and the accuracy of the result and the evaluation performance are verified with simulated data, and the vulnerabilities or performance problems are debugged and optimized to ensure the efficient and stable operation of the model. After being fully tested and optimized, it will be deployed to the platform and will need to be regularly updated and maintained in the future to respond to changes, ensure platform security, promote technological development and ecological construction, protect data security and user rights, and help promote high-quality economic and social development.

[0024] Step S214, connect the task leakage risk prediction model and the leakage risk verification model to generate the task leakage risk verification channel. Specifically, design and adapt the output interface of the task leakage risk prediction model and the input interface of the leakage risk verification model, clarify the output format, data type and precision of the risk coefficient, optimize the transmission process, and verify and handle the exceptions of the input interface to lay the foundation for model connection. Then establish a reliable data transmission channel and synchronization mechanism, adopt message queue asynchronous transmission and data synchronization confirmation mechanism to ensure real-time and accurate transmission of risk coefficients, and avoid data blocking, loss and inconsistency. Then physically connect and logically integrate the two models, optimize the business logic to make them work together, realize the complete process from encryption scheme input to risk verification result output, ensure the model connection is stable and reliable, and give play to the synergy advantage. Finally, comprehensively test and optimize the task leakage risk verification channel, verify the results with simulated data, take corresponding optimization measures for the reasons of deviation analysis, such as data transmission, parameter setting or algorithm defects, and regularly maintain and update with business and environmental changes to improve the platform security protection capabilities, ensure data security, promote technological innovation and development and industry digital transformation, and help the high-quality development of the economy and society.

[0025] In a possible implementation, the leakage risk verification model is constructed, wherein the leakage risk verification model includes a leakage risk verification operator, wherein the leakage risk verification operator includes: if the task leakage risk coefficient is less than the task leakage risk threshold, the task leakage risk verification result is passed; if the task leakage risk coefficient is greater than or equal to the task leakage risk threshold, the task leakage risk verification result is failed; step S213 further includes step S2131, when the task leakage risk verification result is failed, the task encryption scheme is adjusted to establish a task encryption adjustment scheme space. Specifically, upon receiving a notification that the task leakage risk verification result is "failed", a comprehensive risk assessment is first carried out to review various parts of the task encryption scheme, including encryption algorithms, key management, encryption modes, network transmission security protocols, and system access control settings, etc., to identify security vulnerabilities and weak links, clarify the causes of the risk, and indicate the direction for subsequent adjustments. Then, based on the diagnosis results, we formulate multi-dimensional encryption scheme adjustment strategies, such as upgrading encryption algorithms, optimizing key management, switching encryption modes, strengthening network transmission encryption and system access control, etc., to build a rich task encryption adjustment scheme space, and fully analyze the platform business, performance and technical feasibility, and balance security and performance. Finally, we integrate and organize various strategies, present the scheme space with a structured data model, describe the implementation steps and parameters of each scheme in detail, mark the expected effect, implementation difficulty and performance impact, etc., to provide strong support for subsequent extraction and inspection, so as to effectively respond to data leakage risks, improve the platform security protection level, promote the development of cloud computing technology, protect data security and user rights, help digital transformation and high-quality development, and play an important role in the field of information security.

[0026] Step S2132, according to the task encryption adjustment scheme space, randomly extract the nth scheme of task encryption adjustment, where n is a positive integer. Specifically, set a unique index or identifier for each scheme in the task encryption adjustment scheme space, and establish an index directory, which is associated with the detailed content and attributes of the scheme to facilitate subsequent search. Then, use a random number generation algorithm (such as linear congruential method, Mersenne rotation algorithm, etc.) to generate a random number corresponding to the number of schemes, ensure that it is within the effective range and evenly distributed, so that each scheme has an equal probability of being selected, and set the parameters of the random number generator to ensure randomness. Then, according to the generated random number, extract the nth scheme of task encryption adjustment in the scheme space through the index directory, fully obtain its encryption parameter adjustment, implementation steps and configuration information, and record the extracted scheme index, time and operating environment information, etc. The above records are of great significance to subsequent testing, evaluation and troubleshooting, and help optimize the scheme selection strategy, improve the efficiency and accuracy of the entire encryption scheme adjustment process, ensure platform security, promote the development of cloud computing technology, help various industries digital transformation and high-quality development, and play an important role in the field of information security.

[0027] Step S2133, input the nth scheme of task encryption adjustment into the task leakage risk inspection channel to obtain the leakage risk inspection result of the nth scheme. Specifically, the nth scheme of task encryption adjustment is preprocessed and formatted, its encryption parameters are checked and corrected, and it is arranged and organized according to the requirements of the task leakage risk inspection channel to ensure input specifications. Then, it is input into the channel, and the key encryption features of the scheme are first extracted by the task leakage risk prediction model and converted into a numerical vector. According to the pre-trained neural network structure and parameter calculation, the corresponding task leakage risk coefficient is output. Subsequently, the coefficient is input into the leakage risk inspection model, compared with the pre-set risk threshold, and judged by the inspection operator. If it is less than the threshold, the inspection result is "passed", otherwise it is "failed". Finally, the inspection result is output from the channel and recorded and stored, including scheme details, input time, risk coefficient value, results and environmental information, etc., for subsequent analysis and decision-making. If problems are found, the scheme can be optimized, the environment can be adjusted or the model can be improved to improve the accuracy and reliability of risk inspection, ensure platform security, promote the development of cloud computing technology and the digital transformation of the industry, help the high-quality development of the economy and society, and play an important role in the field of information security.

[0028] Step S2134, when the leakage risk test result of the nth scheme is passed, the communication task to be executed is encrypted and optimized according to the task encryption adjustment nth scheme. Specifically, after receiving the feedback that the leakage risk test result of the nth scheme is "passed", the result is first confirmed and verified, the risk inspection data and process are checked, and the encryption measures and parameters of the task encryption adjustment nth scheme are reviewed to ensure that the result is accurate and the scheme is compliant. The encryption parameters of the communication task to be executed are updated and configured according to the scheme, such as replacing and correctly initializing in the code and system configuration when changing the encryption algorithm, adjusting the key management system, including generating new keys and updating the storage distribution mechanism, and optimizing the encryption mode, network transmission encryption settings, and system access control parameters to ensure accuracy. Afterwards, the encryption optimization effect is verified, and the business function is checked through functional testing. Security testing is used to resist simulated attacks, and system performance is evaluated to avoid excessive negative impact on the overall performance of the platform. In addition, a continuous monitoring and optimization adjustment mechanism can be established to collect encryption-related data during task operation, monitor the status in real time, promptly investigate and deal with problems when they are discovered, and optimize and adjust the plan again when necessary to adapt to security challenges and ensure the long-term effectiveness and stability of the encryption plan. This will provide security for the communication tasks of the cloud computing platform. At the same time, it is necessary to continuously optimize and improve the plan process, enhance the platform's security protection capabilities, ensure data security and user rights, promote the innovative development of cloud computing technology and the digital transformation of the industry, assist in high-quality economic and social development, and play an important role in information security.

[0029] Step S300, when the task leakage risk test result is passed, collect the real-time protection parameters of the communication link and establish a communication protection mechanism. Specifically, after receiving the notification that the task leakage risk test result is "passed", it is necessary to strictly confirm the result, check the operation logs and data records of each link of the inspection channel, ensure that the risk coefficient is calculated accurately, the threshold is set reasonably and is not interfered by abnormal factors, and ensure that the result is true and reliable. Then immediately start the collection of real-time protection parameters of the communication link, and collect network traffic (size, rate, direction, etc.), connection status (duration, source and destination address, port number, etc.), data packets (size, protocol type, content, etc.) and equipment operation status (CPU usage, memory occupancy, interface error rate, etc.) parameters through monitoring tools and sensors deployed at key nodes such as source servers, switches, routers and destination devices, to provide data support for the establishment of a protection mechanism. Finally, a multi-level communication protection mechanism is built based on these parameters. Thresholds and detection rules are set based on traffic data to trigger restrictions or redirections when anomalies occur. White and black lists are established for connection status to prevent illegal connections. Encryption, verification, and filtering are used to protect data packets. Thresholds and warnings are set according to device status, and corresponding measures are taken in case of failure to ensure the security and stability of communication links in all aspects. The solution process is continuously optimized and improved with the development of business and technology, to enhance the platform's security protection capabilities, protect data security and user rights, promote the development of cloud computing technology and the digital transformation of the industry, help with high-quality economic and social development, and play an important role in the field of information security.

[0030] Step S400, introduce the communication leakage risk prediction channel, combine the communication protection mechanism to predict the communication data leakage risk of the communication task to be executed, and obtain the communication leakage risk coefficient. Specifically, first determine the communication leakage risk prediction channel architecture, select a machine learning or deep learning model, clearly cover the input data source of communication protocol, data transmission, sensitivity, authentication method, network environment, etc., and collect and pre-process it to build a prediction channel framework. Then, the communication protection mechanism is organically integrated with the prediction channel, and the real-time protection parameters of the protection mechanism such as network traffic, connection status, data packets and equipment operation status are used as supplementary feature input channels. The risk coefficient of the channel output is also fed back to the protection mechanism to dynamically adjust the strategy to achieve synergy and complementarity. After the communication task is started, its characteristic data and the real-time parameters of the protection mechanism are input into the prediction channel. After model analysis and processing, the risk characteristics are extracted and calculated, and the communication leakage risk coefficient reflecting the degree of data leakage risk is output. If the coefficient is high, it is necessary to strengthen protection or optimize the task. The prediction process must ensure that the data and model run accurately and efficiently. After optimization and improvement, this solution can enhance the platform's security protection capabilities, ensure data security and user rights, promote the development of cloud computing technology and industry digital transformation, assist in high-quality economic and social development, and play an important role in information security.

[0031] In a possible implementation, a communication leakage risk prediction channel is introduced, and the communication data leakage risk prediction is performed on the communication task to be executed in combination with the communication protection mechanism to obtain a communication leakage risk coefficient. Step S400 further includes step S410, and the communication leakage risk prediction channel includes Q communication leakage risk prediction models, where Q is a positive integer greater than 1. Specifically, according to the characteristics of the communication tasks of the cloud computing platform and the risk factors of data leakage, Q communication leakage risk prediction models of different types including decision trees, neural networks, support vector machines, etc. are selected, and the input layer (determines the reception of quantized and encoded feature data such as network traffic, identity information, transmission characteristics, encryption methods, etc.), the hidden layer (determines the number of neurons and activation functions according to the complexity and data characteristics) and the output layer (outputs the risk degree value or category label) are designed for each model to complete the model selection and architecture design. Next, historical communication and leakage event data are collected, and after pre-processing such as cleaning, denoising, and filling, and feature engineering (such as calculating traffic statistical features, quantizing identity and encryption information, analyzing transmission time frequency features, etc.), high-quality data is prepared for model training. Afterwards, use algorithms (such as decision tree algorithm, back propagation algorithm, solving quadratic programming problems, etc.) to train each model separately, use cross-validation, regularization and other technologies to prevent overfitting, and adjust and optimize according to performance indicators such as accuracy and recall rate, so that each model can achieve better prediction performance. Finally, integrate the trained Q models into the communication leakage risk prediction channel, determine the weight distribution or fusion strategy (such as high weight for models with good performance) based on the independent test performance of the model, and establish a monitoring and update mechanism, regularly evaluate performance, and retrain or update the model when necessary to ensure that the channel is effective and reliable, so as to provide strong support for the prediction of cloud computing platform communication risks, and continuously optimize and improve with the development of business and technology, ensure platform security, promote technological development and industry digital transformation, help the high-quality development of the economy and society, and play an important role in information security.

[0032] Step S420, input the communication protection mechanism and the communication task to be executed into the Q communication leakage risk prediction models to obtain Q communication leakage risk prediction coefficients. Specifically, extract the real-time protection parameters such as network traffic, connection status, data packets and equipment operation status from the communication protection mechanism, organize and standardize them, unify the format and type, and process abnormal values. At the same time, extract and quantify the communication protocol, transmission scale, data sensitivity, authentication method and other features of the communication task to be executed, and integrate them into structured data. Then, input these integrated pre-processed data into Q communication leakage risk prediction models respectively. The model is based on its own algorithm structure, such as decision tree according to branching rules, neural network through neuron transmission calculation, support vector machine mapping high-dimensional space to find hyperplane and other methods of analysis and processing, and independently output Q communication leakage risk prediction coefficients. Finally, before receiving the coefficients, conduct a quality check on the model output results, verify the value range, stability, and compare different model coefficients, troubleshoot possible problems, ensure that the coefficients are reasonable and reliable, provide key data support for subsequent risk assessment decisions, and continuously optimize and improve the process based on actual conditions, enhance the platform's security protection capabilities, ensure data security and user rights, promote the development of cloud computing technology and industry digital transformation, assist in high-quality economic and social development, and play an important role in information security.

[0033] Step S430, weighted calculation is performed according to the Q communication leakage risk prediction accuracies corresponding to the Q communication leakage risk prediction models, and leakage risk prediction incentive conditions are established, wherein the leakage risk prediction incentive conditions include Q leakage risk prediction incentive coefficients. Specifically, the Q communication leakage risk prediction models are evaluated using a test data set independent of the training set, and the prediction results are obtained by inputting the test samples into the model, and compared with the real labels, and the accuracy, recall rate, F1 value, precision rate and other indicators are calculated to quantify the model prediction accuracy from multiple dimensions, providing basic data for subsequent weighted calculation. Then, the weighted calculation method and weight distribution principle are determined according to the business needs and the focus of risk assessment. It is common to use the comprehensive performance indicators of the model (such as F1 value) after normalization as the initial weight, and at the same time comprehensively analyze the model stability, calculation complexity, adaptability to specific data and other factors to adjust the weight, so that the weight distribution is more in line with the actual business and security needs, ensuring that the weighted calculation is scientific and reasonable, giving full play to the advantages of each model, and improving the robustness and adaptability of the risk prediction system. Finally, according to the determined weighting method and weights, Q leakage risk prediction incentive coefficients are calculated. This coefficient will be used as the weight for the subsequent weighted calculation of the communication leakage risk prediction coefficient. In actual operation, these incentive coefficients can guide the risk prediction process, so that models with high prediction accuracy and good stability can play a greater role in the final risk assessment, improve the overall prediction accuracy and reliability, and help the platform accurately identify risks and take security measures to ensure data security and user rights. The solution process is continuously optimized and improved with the development of business and technology, promoting cloud computing technology and industry digital transformation, helping high-quality economic and social development, and giving play to the important value of information security.

[0034] Step S440, weighted calculation is performed on the Q communication leakage risk prediction coefficients according to the Q leakage risk prediction incentive coefficients to generate the communication leakage risk coefficient. Specifically, the Q communication leakage risk prediction coefficients and the corresponding Q leakage risk prediction incentive coefficients are checked to ensure that they are accurate and not affected by any interference factors, so as to lay the foundation for subsequent calculations. Next, each communication leakage risk prediction coefficient is multiplied by the corresponding leakage risk prediction incentive coefficient. For a given plurality of prediction coefficients and incentive coefficients, their products are calculated respectively, and then these products are accumulated and summed to obtain the final communication leakage risk coefficient. This process integrates the prediction results of each model and weights them according to their accuracy, which can more accurately reflect the degree of task leakage risk. Finally, the communication leakage risk coefficient obtained is verified and evaluated for rationality to check whether its value range is consistent with business logic and expectations, and compared with historical data and similar task coefficients. If it exceeds a reasonable range or there is a deviation, the weighted calculation links are checked and possible influencing factors are analyzed to ensure that the coefficient is reliable and effective, providing an accurate basis for platform security decisions. In actual applications, it is necessary to continuously optimize and improve the solution process according to the platform's business and security needs, enhance security protection capabilities, ensure data security and user rights, promote the development of cloud computing technology and industry digital transformation, assist in high-quality economic and social development, and play an important role in information security.

[0035] Step S500, based on the communication leakage risk coefficient, the communication protection mechanism is optimized and adjusted according to the communication leakage risk threshold, the first constraint step length of the protection adjustment and the second constraint step length of the protection adjustment, to construct an optimized communication protection mechanism, wherein the first constraint step length of the protection adjustment is greater than the second constraint step length of the protection adjustment. Specifically, the communication leakage risk coefficient is obtained and compared with the preset communication leakage risk threshold, and it is judged whether the communication protection mechanism needs to be optimized. If the coefficient is greater than the threshold, the step length is selected according to the risk level: when the risk is high, a larger first constraint step length of the protection adjustment is used, and when the risk is relatively low, a smaller second constraint step length of the protection adjustment is used to determine the adjustment direction and amplitude. Then, the parameters of the communication protection mechanism are precisely adjusted, such as enhancing network traffic monitoring and filtering, upgrading encryption algorithms and management, strengthening identity authentication and authorization, optimizing network connection management, etc., to ensure that the parameters are coordinated without conflict. Afterwards, the adjusted parameters are combined to construct an optimized communication protection mechanism, and the effect is verified by re-predicting the risk coefficient, simulating attacks to test security, and evaluating the impact on platform performance. If it does not meet the standard, the optimization adjustment process is repeated until the risk coefficient meets the requirements. In actual applications, we will continue to improve this solution based on the platform's business and security needs, enhance protection capabilities, ensure data security and user rights, promote cloud computing technology and industry digital transformation, assist in high-quality economic and social development, and play an important role in information security.

[0036] In a possible implementation, based on the communication leakage risk coefficient, the communication protection mechanism is optimally adjusted according to the communication leakage risk threshold, the first constraint step of the protection adjustment, and the second constraint step of the protection adjustment, to construct an optimized communication protection mechanism, wherein the first constraint step of the protection adjustment is greater than the second constraint step of the protection adjustment, and step S500 further includes step S510, determining whether the communication leakage risk coefficient is greater than or equal to the communication leakage risk threshold. Specifically, the communication leakage risk coefficient is obtained by comprehensively calculating the real-time parameters of the communication protection mechanism and the characteristic data of the communication task to be executed through the communication leakage risk prediction model, while ensuring that the communication leakage risk threshold reasonably set according to the cloud computing platform security policy, data sensitivity level, industry security standards and past experience is accurate and effective, and the data format and accuracy of the two are checked and unified. Next, the communication leakage risk coefficient is compared with the threshold. If the coefficient is less than the threshold, the existing protection measures can be maintained or gently optimized and fine-tuned to improve efficiency and reduce resource consumption. If the coefficient is greater than or equal to the threshold, it indicates that the current protection mechanism is insufficient, and it is necessary to initiate optimization measures such as parameter adjustment and function enhancement of the communication protection mechanism according to the preset protection adjustment strategy to reduce the risk coefficient to an acceptable range. This comparison and judgment process must ensure the accuracy and stability of the algorithm to avoid erroneous results affecting the effectiveness of the protection system. In actual applications, the process should be continuously optimized and improved according to the platform situation, to enhance protection capabilities, to ensure data security and user rights, to promote cloud computing technology and industry digital transformation, to assist in high-quality economic and social development, and to play an important role in information security.

[0037] Step S520, if the communication leakage risk coefficient is greater than or equal to the communication leakage risk threshold, the communication protection mechanism is adjusted according to the first constraint step of the protection adjustment to obtain the first space of the protection mechanism adjustment scheme. Specifically, when it is determined that the communication leakage risk coefficient is greater than or equal to the communication leakage risk threshold, the adjustment range of each parameter is first clarified according to the first constraint step of the protection adjustment (such as 20%), such as the key length of network traffic encryption, network connection timeout, data packet verification frequency, device access control policy and other parameters, all possible values ​​of which are determined within the range less than or equal to the step, such as the key length can be adjusted from 128 bits to 153 bits (about 20% increase), 144 bits (about 12.5% ​​increase), etc., and the timeout time is adjusted from 60 seconds to 48 seconds (20% reduction), 50 seconds (about 16.7% reduction), etc. Next, based on the value range of each parameter, a combination adjustment is performed to generate a series of adjustment schemes. For example, network traffic encryption has multiple key length adjustment methods, network connection timeout has multiple adjustment methods, etc. Through permutations and combinations, many different communication protection mechanism adjustment schemes are formed. These schemes constitute the first space of protection mechanism adjustment schemes, and at the same time, the interaction between parameters is fully considered to ensure its feasibility and coordination. Finally, all adjustment schemes are integrated and stored in a suitable data structure, and unreasonable schemes are preliminarily screened and removed, such as those that lead to excessive resource consumption or parameter conflicts, to form a complete, orderly and reasonable first space of protection mechanism adjustment schemes, which provides a basis for subsequent optimization analysis, and continuously optimizes and improves the process in actual applications, improves the platform's security protection capabilities, protects data security and user rights, promotes cloud computing technology and industry digital transformation, helps the high-quality development of the economy and society, and plays an important role in information security.

[0038] Step S530, according to the communication leakage risk threshold, the first space of the protection mechanism adjustment scheme is optimized and analyzed to obtain the second space of the protection mechanism adjustment scheme. Specifically, it is confirmed that the communication leakage risk threshold and the first space of the protection mechanism adjustment scheme generated by the first constraint step of the protection adjustment have been accurately mastered, and at the same time, it is ensured that the evaluation tools such as the communication leakage risk prediction model are ready and reliable and effective. Then, each adjustment scheme is taken out from the first space of the protection mechanism adjustment scheme in turn and applied to the communication protection mechanism, and the communication leakage risk coefficient is calculated using the risk prediction model and compared with the risk threshold. If a certain scheme makes the risk coefficient lower than the threshold, it is regarded as a potential better scheme because it can meet the basic requirements of platform data security, and this process must ensure that the scheme application and coefficient calculation are accurate. Finally, all schemes that meet the risk coefficient less than the threshold are screened out, integrated to form the second space of the protection mechanism adjustment scheme, and these schemes are further sorted and classified, such as grouping by the protection parameter adjustment method, and basic statistical analysis is performed, such as calculating the number and analyzing the proportion to understand its distribution characteristics. In actual applications, we will continue to optimize and improve this process, enhance the platform's security protection capabilities, ensure data security and user rights, promote cloud computing technology and industry digital transformation, assist in high-quality economic and social development, and play an important role in information security.

[0039] Step S540, according to the second space of the protection mechanism adjustment scheme, the communication leakage risk is minimized and optimized to generate the optimized communication protection mechanism. Specifically, the goal of minimizing the communication leakage risk based on the second space of the protection mechanism adjustment scheme is clarified, and the use of intelligent algorithms such as simulated annealing, genetic or particle swarm optimization is determined. Taking the genetic algorithm as an example, it is necessary to reasonably set parameters such as population size, crossover and mutation probability to ensure the balance of its global and local search capabilities. Then, the second space is initialized according to the selected algorithm, such as randomly selecting a scheme as the initial population in the genetic algorithm, whose gene encoding corresponds to the protection mechanism parameters, and then calculating the communication leakage risk coefficient under each scheme as the evaluation basis to ensure that the evaluation is accurate and efficient. After that, iterative optimization is entered. Taking the genetic algorithm as an example, offspring individuals are generated through crossover and mutation, and their risk coefficients are evaluated. Individuals are selected to enter the next generation according to fitness (related to the risk coefficient), and the convergence is closely monitored. If necessary, the algorithm parameters are adjusted to prevent premature or slow convergence. When the preset termination conditions are met, such as reaching the maximum number of iterations or the optimal individual risk coefficient is stable, the iteration is stopped. At this time, the solution represented by the optimal individual is the solution that minimizes the risk, and it is applied to the communication protection mechanism to generate an optimized protection mechanism. Finally, the optimization mechanism is verified and tested, the actual scene attack situation is simulated, and the prevention and control effect and the impact on the platform performance are observed. If there are any problems, the optimization process is reviewed and analyzed, and the algorithm parameters are adjusted, the solution space is expanded, or the risk prediction model is improved for further optimization. In practice, the process is continuously improved according to the platform situation, the security protection capability is enhanced, the data security and user rights are guaranteed, the cloud computing technology and the digital transformation of the industry are promoted, the high-quality development of the economy and society is promoted, and the important role of information security is played.

[0040] In a possible implementation, the first space of the protection mechanism adjustment scheme is optimized and analyzed according to the communication leakage risk threshold to obtain the second space of the protection mechanism adjustment scheme. Step S530 further includes step S531, according to the first space of the protection mechanism adjustment scheme, randomly extracting the kth protection mechanism adjustment scheme, wherein k is a positive integer. Specifically, all the schemes in the first space of the protection mechanism adjustment scheme are organized in the form of an array or a list, and the total number of schemes N is recorded, and a data structure system that can access each scheme through an index is established to lay the foundation for random extraction, which can ensure the improvement of scheme management efficiency in the complex communication environment of the cloud computing platform, ensure the safe and stable operation of the platform, promote the application and development of cloud computing technology, protect the security and privacy of user data, and help build a digital ecosystem. Then, a reliable random number generation algorithm (such as random.randint(1,N) in Python) is used to generate a positive integer k between 1 and N. This process must ensure the randomness and uniformity of the random number, and avoid the distribution bias from affecting the comprehensive objectivity of the scheme selection. This is the key to achieving fair and unbiased selection from many schemes. Finally, based on the generated random number k and the data structure established previously, the kth protection mechanism adjustment plan is accurately and completely extracted from the first space of the protection mechanism adjustment plan to provide samples for subsequent risk assessment and other operations, and the process is continuously optimized and improved in practical applications to enhance the security protection capabilities of the cloud computing platform, promote the digital transformation of the industry, assist in the high-quality development of the economy and society, and play an important role in information security.

[0041] Step S532, input the protection mechanism adjustment scheme k and the communication task to be executed into the communication leakage risk prediction channel to obtain the communication leakage risk coefficient of the k scheme. Specifically, check and sort out various parameters in the protection mechanism adjustment scheme k, such as parameters in network traffic monitoring and filtering, data encryption, identity authentication and authorization, and network connection management, and sort out the characteristic data such as the communication protocol, transmission data volume, data sensitivity, and information of both parties of the communication task to be executed, to ensure that the data is complete and accurate and the format matches, which is the basis for accurate risk assessment, helps to ensure the security and stability of the cloud computing platform, promote the development of technology applications, protect user data rights and interests, and build a digital ecosystem. Then, input the data of the two together into the communication leakage risk prediction channel composed of multiple models based on different algorithm principles. These models analyze data from multiple angles such as network traffic, data encryption, and identity authentication. For example, some judge abnormalities based on traffic parameters and characteristics, some consider encryption strength and data sensitivity, and some analyze the risks of authentication and authorization links. Each model is independent and collaborative, and comprehensively analyzes the input data. Finally, each model outputs a preliminary risk assessment value based on its own algorithm, and then integrates it through a specific fusion algorithm (such as the weighted average method, summing up the weights assigned according to model accuracy, etc.) to obtain the communication leakage risk coefficient of the kth solution. The value range is determined according to the platform standard, such as 0 to 1. The value reflects the degree of risk. Subsequently, it is compared with the threshold to determine whether the solution meets the security requirements. In practice, this process should be continuously optimized and improved to enhance the platform's protection capabilities, promote the digital transformation of the industry, assist in the high-quality development of the economy and society, and play an important role in information security.

[0042] Step S533, determine whether the communication leakage risk coefficient of the k-th solution is less than the communication leakage risk threshold. Specifically, the communication leakage risk coefficient of the k-th solution is obtained, and the communication leakage risk threshold determined comprehensively based on the cloud computing platform security policy, data sensitivity, industry standards and past experience, and the data type and precision of the two are checked and unified at the same time to prepare for subsequent comparison. This step is crucial. Its accuracy is related to the reliability and effectiveness of the entire protection mechanism optimization process. It is the cornerstone for ensuring the security and stability of the platform, promoting the application and development of cloud computing technology, and protecting user data rights. Next, the two are compared and judged, which is a key decision point. If the communication leakage risk coefficient of the k-th solution is less than the threshold, it means that the solution can effectively control the risk and can be used as a potential better solution, such as being included in the second space of the protection mechanism adjustment solution for subsequent in-depth research and optimization; on the contrary, if it is greater than or equal to the threshold, it means that the solution does not meet the security requirements and needs to be adjusted or abandoned. It may be necessary to re-select the solution for evaluation or optimize the current solution and then evaluate it again. The entire comparison process must ensure that the algorithm is accurate and stable to avoid errors that affect the optimization efficiency and quality of the protection mechanism, provide a basis for platform security decisions, assist in the sustainable development of cloud computing technology in various fields, promote high-quality economic and social development, and highlight the important value of information security. In practice, this process should be continuously improved to enhance the platform's security protection capabilities.

[0043] Step S534, if the communication leakage risk coefficient of the k-th solution is less than the communication leakage risk threshold, the protection mechanism adjustment k-th solution is added to the second space of the protection mechanism adjustment solution. Specifically, verify that the communication leakage risk coefficient of the k-th solution is less than the communication leakage risk threshold. The accuracy of the judgment is directly related to the optimization direction of the protection mechanism, which is the basis for ensuring the security and stability of the cloud computing platform, protecting user data privacy and promoting technological development. Next, prepare the second space of the protection mechanism adjustment solution, check its data structure and storage status, for example, if it is a list structure, confirm the capacity and scalability, if it is a database table form, ensure that the link is normal and the table structure is complete, to ensure that it can smoothly accept the new solution and related information storage, and provide a solid data foundation for subsequent operations. Finally, the kth protection mechanism adjustment plan is added to the second space completely and accurately, and the adjustment details of various parameters in the plan are recorded, such as network traffic encryption, identity authentication enhancement, connection timeout adjustment and other information. At the same time, metadata such as the addition time and plan source are attached for subsequent tracing and analysis, and added to the second space plan reserve to provide more high-quality options for optimizing the protection mechanism. In actual applications, the process should be continuously improved according to the platform situation to enhance security protection capabilities, assist cloud computing technology and industry digital transformation, promote high-quality economic and social development, and highlight the important value of information security.

[0044] In a possible implementation, if the communication leakage risk coefficient of the kth scheme is less than the communication leakage risk threshold, the protection mechanism adjustment kth scheme is added to the second space of the protection mechanism adjustment scheme, and step S534 further includes step S5341, if the communication leakage risk coefficient is less than the communication leakage risk threshold, the communication protection mechanism is added to the third space of the protection mechanism adjustment scheme. Specifically, obtaining the communication leakage risk coefficient, verifying the communication leakage risk threshold determined based on the cloud computing platform security policy, data sensitivity, industry standards and past experience, and then comparing to ensure data accuracy and comparison algorithm reliability is a key prerequisite for ensuring platform security and subsequent decision-making, and is of great significance to improving platform security and stability and promoting the development of cloud computing technology applications. When it is determined that the risk coefficient is less than the threshold, prepare to add the communication protection mechanism to the third space of the protection mechanism adjustment scheme, check its data structure and storage status, such as the list form needs to confirm that there is enough space, and the database storage must ensure that the link is normal and the table structure is adapted, so as to fully and accurately record the network traffic monitoring rules, data encryption details, identity authentication methods, network connection parameters and other information of the communication protection mechanism, and at the same time organize the existing schemes and update indexes. Next, the communication protection mechanism is accurately added to the third space in strict accordance with the predetermined format, a unique identifier is generated for easy retrieval, and metadata such as the addition time, risk factor assessment results, and the type of communication task are recorded to enrich the content of the third space and provide more possibilities for the optimization of the protection mechanism. In practice, the process should be continuously improved according to the platform situation, to enhance security protection capabilities, promote the digital transformation of the industry, assist in high-quality economic and social development, and play an important role in information security.

[0045] Step S5342, adjust the communication protection mechanism according to the second constraint step of the protection adjustment, and establish the fourth space of the protection mechanism adjustment scheme. Specifically, the specific value of the second constraint step of the protection adjustment is clarified, such as setting it to 15%, and the adjustment range of each parameter of the communication protection mechanism is determined accordingly, such as the key length of network traffic encryption, network connection timeout, data packet verification frequency, device access control strategy and other parameters, which are respectively taken within a range less than or equal to the step length, such as a 128-bit key can be adjusted to 147 bits (about 14.8% increase), 136 bits (about 6.25% increase), etc., and a 60-second timeout can be adjusted to 51 seconds (15% decrease), 55 seconds (about 8.33% decrease), etc. By calculating the possible values ​​of each parameter, preparations are made for the subsequent generation of a mediation scheme, which is the basis for ensuring system stability and exploring optimization schemes, and is crucial to improving platform security and promoting the development of cloud computing technology applications. Next, based on the range of each parameter value, a combination adjustment is made. For example, network traffic encryption has multiple key length adjustment methods and other parameter adjustment methods. A series of 120 different adjustment schemes are generated. The above schemes optimize the communication protection mechanism under the constraint step size limit, and fully analyze the interaction between parameters to ensure feasibility and coordination, aiming to improve communication security, provide diverse options for optimization analysis, increase the probability of finding the optimal mechanism, promote the safe development of cloud computing technology, and help the digital transformation of various industries. Finally, all adjustment schemes are integrated to form the fourth space of the protection mechanism adjustment scheme, which is stored in the form of lists, arrays or database tables, records the parameter settings and adjustment instructions of each scheme, and preliminarily screens and removes unreasonable schemes such as those that cause excessive resource consumption or parameter conflicts, improves the efficiency and accuracy of subsequent optimization analysis, provides strong support for platform security decisions, ensures the security and stability of platform communications, promotes the in-depth application and innovative development of cloud computing technology, builds a safe and efficient digital ecosystem, helps the high-quality development of the economy and society, and plays an important role in information security. In practice, the process should be continuously improved according to the platform situation to enhance security protection capabilities.

[0046] Step S5343, according to the communication leakage risk threshold, the fourth space of the protection mechanism adjustment scheme is optimized and analyzed to obtain the fifth space of the protection mechanism adjustment scheme. Specifically, mastering the communication leakage risk threshold and the fourth space of the protection mechanism adjustment scheme generated by the second constraint step of the protection adjustment, while ensuring that the evaluation tools such as the communication leakage risk prediction model that has been fully trained and verified are ready, is the basis for subsequent optimization analysis, can ensure the scientificity and reliability of the process, improve the security and stability of the cloud computing platform, promote its wide application and development, and is of great significance to protecting user data assets and privacy rights. Next, take out the adjustment schemes from the fourth space one by one and apply them to the communication protection mechanism, use the risk prediction model to calculate its communication leakage risk coefficient, and compare it with the threshold. This is a key evaluation step. If a certain scheme makes the risk coefficient less than the threshold, such as through strengthening encryption strength, optimizing connection strategy, increasing verification frequency and other measures, the scheme is regarded as a potential better scheme, and in the process, it is necessary to ensure that the scheme application and coefficient calculation are accurate to avoid incorrect evaluation. Finally, all solutions that meet the requirement of risk coefficient less than the threshold are screened out and integrated to form the fifth space of protection mechanism adjustment solution. The solutions are further sorted and classified, such as grouping by protection parameter adjustment method. Basic statistical analysis is also required to understand their distribution characteristics. This provides a more targeted and effective selection range for determining the optimization of communication protection mechanism, which can improve optimization efficiency and quality, ensure the security and stability of platform communications, promote the in-depth application and innovative development of cloud computing technology in various fields, and help build a safe and efficient digital ecosystem. In practice, this process should be continuously improved according to the platform situation to enhance security protection capabilities.

[0047] Step S5344, based on the fifth space of the protection mechanism adjustment scheme, the third space of the protection mechanism adjustment scheme is expanded to obtain the sixth space of the protection mechanism adjustment scheme. Specifically, the scheme data selected by the optimization analysis based on the communication leakage risk threshold is extracted from the fifth space of the protection mechanism adjustment scheme, and the existing scheme information of the third space of the protection mechanism adjustment scheme is obtained at the same time, and then the compatibility check is performed on the two to ensure that there is no technical conflict between the parameter settings and the protection mechanism in aspects such as network traffic monitoring, data encryption algorithm, and identity authentication module, which is an important prerequisite for ensuring the effective implementation of the protection mechanism, and is of great significance to improving the security and stability of the cloud computing platform, promoting the development of technology applications, and protecting the rights and interests of user data. Then, the fifth space schemes are added one by one to the third space to expand it. In this process, similar schemes are optimized and merged according to factors such as communication leakage risk coefficient and system performance, such as selecting the optimal settings and merging similar schemes with different network traffic encryption strengths, and recording the source of the new scheme and the differences and optimization points with the original scheme, so as to enrich the diversity of the schemes, provide more possibilities for finding the optimal protection mechanism, promote the safe development of cloud computing technology, and help the digital transformation of various industries. Finally, the sixth space of the protection mechanism adjustment plan is constructed, and the plan is stored in the form of a database table, etc., which includes detailed parameters, risk assessment results, added time, source space and other fields, and the index is updated to facilitate rapid query and screening based on the risk factor range, specific protection parameter values, etc., to improve the efficiency of plan retrieval and use, provide support for determining and optimizing communication protection mechanisms, ensure the security and stability of platform communications, promote the in-depth application and innovative development of cloud computing technology, and build a safe and efficient digital ecosystem. In practice, this process should be continuously improved according to the platform situation to enhance security protection capabilities.

[0048] Step S5345, according to the sixth space of the protection mechanism adjustment scheme, the communication leakage risk is minimized and optimized to generate the optimized communication protection mechanism. Specifically, it is clear that the communication leakage risk minimization is the optimization goal in the sixth space of the protection mechanism adjustment scheme, and a suitable optimization method is determined, such as a genetic algorithm, and key parameters such as population size (such as 50), crossover probability (such as 0.7) and mutation probability (such as 0.05) are set to balance the algorithm's global and local search capabilities. This is the key to achieving risk minimization and is of great significance to improving the communication security of the cloud computing platform and promoting the development of technology applications. Next, the sixth space is initialized according to the selected algorithm, such as randomly selecting 50 schemes from the space as the initial population in the genetic algorithm, and the individual gene encoding corresponds to the protection mechanism parameters. Then, the communication leakage risk prediction model is used to evaluate these individuals, and the communication leakage risk coefficient is calculated as the basis for subsequent evolutionary operations. At this stage, the evaluation must be accurate and efficient. Then iterative optimization is carried out. Taking genetic algorithms as an example, offspring individuals are generated through crossover and mutation to simulate the biological evolution process, increase population diversity, avoid falling into local optimality, and then evaluate the risk coefficient of offspring individuals. Excellent individuals are selected according to fitness (related to risk coefficient) to enter the next generation. In the process, close attention is paid to the convergence of the algorithm, and parameters are adjusted when necessary to prevent premature or slow convergence. When the preset termination conditions (such as reaching the maximum number of iterations, etc.) are met, the iteration is stopped. At this time, the solution represented by the optimal individual is what is required, and it is applied to the communication protection mechanism to generate an optimized protection mechanism. Finally, the optimization mechanism is verified and tested, and the actual scene attack situation is simulated to observe the prevention and control effect and the impact on platform performance. If there are any problems, the optimization process is reviewed and analyzed, and the algorithm parameters are adjusted, the solution space is expanded, or the risk prediction model is improved for further optimization. In practice, the process is continuously improved according to the platform situation, the security protection capability is improved, the data security and user rights are guaranteed, the cloud computing technology and the digital transformation of the industry are promoted, the high-quality development of the economy and society is promoted, and the important role of information security is played.

[0049] Step S600, optimize the protection of the communication link according to the optimized communication protection mechanism, obtain the optimized communication link, and execute the communication task to be executed in combination with the cloud computing platform. Specifically, analyze and optimize the communication protection mechanism, clarify its various protection components and parameter settings, such as the algorithm, range, and timing of network traffic encryption, the rules of network access control, the parameters of the intrusion detection system, etc., to lay the foundation for subsequent applications, which is crucial to ensure the communication security of the cloud computing platform and promote technological development. Then, optimize each link of the communication link according to the analysis results, encrypt data and restrict access at the network layer, such as using a firewall to intercept illegal data packets according to rules; improve the error detection and correction mechanism at the data link layer, and adopt an efficient verification algorithm; optimize the communication protocol and data processing method at the application layer, such as using the HTTPS protocol and verifying the data signature, to build an optimized communication link with significantly improved confidentiality, integrity and availability. Finally, the communication link will be optimized and seamlessly integrated with the cloud computing platform to execute the communication tasks to be executed, ensuring the compatibility and coordination of the link and platform software and hardware, reasonably allocating bandwidth resources, establishing a monitoring and management mechanism, real-time monitoring of performance and security status, and timely handling of abnormalities to ensure smooth execution of tasks and achieve safe and efficient communication on the platform. In practice, the process should be continuously improved according to the platform situation, the security protection capabilities should be enhanced, the digital transformation of cloud computing technology and the industry should be promoted, high-quality economic and social development should be promoted, and an important role of information security should be played.

[0050] The embodiment of the present application adopts the method of obtaining communication instructions containing communication tasks to be executed and corresponding communication links from a cloud computing platform, and performing data leakage risk inspection results according to the tasks. If the inspection passes, real-time protection parameters of the link are collected to establish a protection mechanism, and a risk prediction channel is introduced to combine with the mechanism to predict the communication data leakage risk coefficient of the task to be executed. Then, the protection mechanism is optimized and adjusted according to the risk coefficient, threshold and different constraint step sizes to construct an optimization mechanism. Finally, the communication link protection is optimized to obtain an optimized link, and the task is executed in combination with the cloud computing platform, thereby achieving the technical effect of improving the protection performance of the communication link in the cloud computing environment and reducing the risk of data leakage.

[0051] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0052] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A communication data leakage protection method in a cloud computing environment, characterized in that: The method comprises: Obtaining a communication instruction of the cloud computing platform, wherein the communication instruction includes a communication task to be executed and a communication link corresponding to the communication task to be executed; Performing a data leakage risk test according to the communication task to be executed, and obtaining a task leakage risk test result; When the task leakage risk test result is passed, real-time protection parameters of the communication link are collected to establish a communication protection mechanism; Introducing a communication leakage risk prediction channel, combining the communication protection mechanism to predict the communication data leakage risk of the communication task to be executed, and obtaining a communication leakage risk coefficient; Based on the communication leakage risk coefficient, the communication protection mechanism is optimally adjusted according to the communication leakage risk threshold, the first constraint step length of the protection adjustment, and the second constraint step length of the protection adjustment, to construct an optimized communication protection mechanism, wherein the first constraint step length of the protection adjustment is greater than the second constraint step length of the protection adjustment; The communication link is protected and optimized according to the optimized communication protection mechanism to obtain an optimized communication link, and the communication task to be executed is executed in combination with the cloud computing platform.

2. The method according to claim 1, characterized in that Performing a data leakage risk test according to the communication task to be executed to obtain a task leakage risk test result includes: Constructing a task leakage risk verification channel, wherein the task leakage risk verification channel includes a task leakage risk prediction model and a leakage risk verification model; Collecting encryption parameters of the communication task to be executed to obtain a task encryption scheme; Inputting the task encryption scheme into the task leakage risk prediction model to obtain a task leakage risk coefficient; The task leakage risk coefficient is input into the leakage risk verification model, and the task leakage risk verification result is output.

3. The method according to claim 2, characterized in that Build a task leakage risk inspection channel, including: Taking the task encryption scheme sample set as input information and the task leakage risk sample set as output information, the fully connected neural network is trained, and the leakage risk prediction loss coefficient is obtained after each predetermined number of trainings. If the leakage risk prediction loss coefficient is less than the leakage risk prediction loss threshold, generating the task leakage risk prediction model; Constructing the leakage risk verification model, wherein the leakage risk verification model includes a leakage risk verification operator, wherein the leakage risk verification operator includes: if the task leakage risk coefficient is less than the task leakage risk threshold, the task leakage risk verification result is passed; if the task leakage risk coefficient is greater than or equal to the task leakage risk threshold, the task leakage risk verification result is failed; The task leakage risk prediction model and the leakage risk verification model are connected to generate the task leakage risk verification channel.

4. The method according to claim 2, characterized in that When the task leakage risk inspection result is failed, adjusting the task encryption scheme and establishing a task encryption adjustment scheme space; According to the task encryption adjustment scheme space, randomly extract the task encryption adjustment nth scheme, where n is a positive integer; Inputting the task encryption adjustment nth scheme into the task leakage risk inspection channel to obtain the leakage risk inspection result of the nth scheme; When the leakage risk inspection result of the nth scheme is passed, encryption optimization is performed on the communication task to be executed according to the task encryption adjustment nth scheme.

5. The method according to claim 1, characterized in that Introducing a communication leakage risk prediction channel, combining the communication protection mechanism to predict the communication data leakage risk of the communication task to be executed, and obtaining a communication leakage risk coefficient, including: The communication leakage risk prediction channel includes Q communication leakage risk prediction models, where Q is a positive integer greater than 1; Inputting the communication protection mechanism and the communication task to be executed into the Q communication leakage risk prediction models to obtain Q communication leakage risk prediction coefficients; Performing weighted calculation according to the Q communication leakage risk prediction accuracies corresponding to the Q communication leakage risk prediction models, and establishing leakage risk prediction incentive conditions, wherein the leakage risk prediction incentive conditions include Q leakage risk prediction incentive coefficients; The Q communication leakage risk prediction coefficients are weightedly calculated according to the Q leakage risk prediction incentive coefficients to generate the communication leakage risk coefficient.

6. The method according to claim 1, characterized in that Based on the communication leakage risk coefficient, the communication protection mechanism is optimally adjusted according to the communication leakage risk threshold, the first constraint step length of the protection adjustment, and the second constraint step length of the protection adjustment, to construct an optimized communication protection mechanism, wherein the first constraint step length of the protection adjustment is greater than the second constraint step length of the protection adjustment, including: Determining whether the communication leakage risk coefficient is greater than or equal to the communication leakage risk threshold; If the communication leakage risk coefficient is greater than or equal to the communication leakage risk threshold, adjusting the communication protection mechanism according to the first constraint step of the protection adjustment to obtain a first space of the protection mechanism adjustment scheme; Performing optimization analysis on the first space of the protection mechanism adjustment scheme according to the communication leakage risk threshold to obtain a second space of the protection mechanism adjustment scheme; According to the protection mechanism adjustment scheme, the second space performs optimization for minimizing the risk of communication leakage to generate the optimized communication protection mechanism.

7. The method according to claim 6, characterized in that The first space of the protection mechanism adjustment scheme is optimized and analyzed according to the communication leakage risk threshold to obtain the second space of the protection mechanism adjustment scheme, including: According to the first space of protection mechanism adjustment schemes, randomly extracting the kth protection mechanism adjustment scheme, where k is a positive integer; Inputting the protection mechanism adjustment k-th scheme and the communication task to be executed into the communication leakage risk prediction channel to obtain the communication leakage risk coefficient of the k-th scheme; Determining whether the communication leakage risk coefficient of the k-th solution is less than the communication leakage risk threshold; If the communication leakage risk coefficient of the k-th scheme is less than the communication leakage risk threshold, the protection mechanism adjustment k-th scheme is added to the second space of the protection mechanism adjustment scheme.

8. The method according to claim 6, characterized in that If the communication leakage risk coefficient is less than the communication leakage risk threshold, adding the communication protection mechanism to the third space of the protection mechanism adjustment scheme; Adjust the communication protection mechanism according to the second constraint step of the protection adjustment to establish a fourth space of the protection mechanism adjustment scheme; Performing optimization analysis on the fourth space of the protection mechanism adjustment scheme according to the communication leakage risk threshold to obtain a fifth space of the protection mechanism adjustment scheme; Based on the fifth space of the protection mechanism adjustment scheme, the third space of the protection mechanism adjustment scheme is expanded to obtain a sixth space of the protection mechanism adjustment scheme; According to the protection mechanism adjustment scheme, the sixth space performs optimization for minimizing the risk of communication leakage to generate the optimized communication protection mechanism.

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