Cloud security vulnerability early warning method based on machine learning

Abstract

The invention relates to the technical field of information security, in particular to a cloud security vulnerability early warning method based on machine learning. Comprising the following steps: collecting log data in a plurality of cloud service environments; the collected log data are preprocessed; multiple features are extracted from the preprocessed log data; a plurality of machine learning algorithms are used to train a plurality of security early warning models, wherein the models are used to predict potential vulnerabilities and security risks in the cloud environment; applying the trained security early warning model to newly generated log data, and triggering an early warning mechanism when the model outputs and displays that possible security threats exist; model parameters are automatically adjusted to optimize early warning accuracy and reduce false alarm and missing alarm phenomena; and according to a model prediction result, generating early warning information, and sending the information to a cloud service provider and a user. According to the cloud security vulnerability early warning method based on machine learning provided by the invention, the security and response speed of a cloud environment are improved through automatic and intelligent means.

Description

Technical Field

[0001] The present invention relates to the field of information security technology, and in particular to a cloud security vulnerability early warning method based on machine learning. Background Art

[0002] With the rapid development of cloud computing technology, more and more enterprises and individuals choose to deploy data and applications to the cloud to enjoy the flexibility, scalability and cost-effectiveness brought by cloud computing. However, the security of the cloud environment has also become an important concern. There are many security threats in the cloud service environment, including but not limited to malicious attacks, data leakage, internal threats, configuration errors and system vulnerabilities. These threats may have a serious impact on the business continuity and data security of enterprises and individuals.

[0003] Existing cloud security solutions mainly rely on traditional security devices and rules. These methods are relatively effective in dealing with known threats, but it is often difficult to detect and respond to unknown or new security threats in a timely manner, especially the dynamics and complexity unique to cloud computing environments. Therefore, there is an urgent need for a method that can proactively predict and warn of potential security threats to improve the security defense capabilities of cloud environments. Summary of the invention

[0004] The present invention proposes a cloud security vulnerability early warning method based on machine learning, which improves the security and response speed of the cloud environment through automated and intelligent means.

[0005] The technical solution adopted by the present invention is: a cloud security vulnerability early warning method based on machine learning, comprising the following steps:

[0006] Step 1: Collect log data in multiple cloud service environments, including but not limited to access records, operation records, and abnormal warnings;

[0007] Step 2: Preprocess the collected log data, including data cleaning, formatting, and repair to ensure data quality and consistency;

[0008] Step 3: Extract multiple features from the preprocessed log data, including but not limited to user behavior patterns, system operation time series, and abnormal activity frequency;

[0009] Step 4: Use multiple machine learning algorithms to train a security warning model, which is used to predict potential vulnerabilities and security risks in the cloud environment;

[0010] Step 5: Apply the trained security warning model to the newly generated log data. When the model output shows a possible security threat, the warning mechanism is triggered.

[0011] Step 6: Automatically adjust model parameters to optimize warning accuracy and reduce false positives and missed positives;

[0012] Step 7: Generate warning information based on the model prediction results, including but not limited to threat level, impact scope, and recommended solutions, and send the information to cloud service providers and users.

[0013] As a further improvement of the present invention, the log data collection has a technical means of cross-platform data integration, supporting data access to AWS, Azure, and Google Cloud cloud service platforms.

[0014] As a further improvement of the present invention, the preprocessing of the log data adopts a data repair algorithm, which can automatically identify and repair missing values ​​and abnormal values ​​in the data, thereby improving the accuracy of subsequent analysis.

[0015] As a further improvement of the present invention, the data cleaning includes but is not limited to removing duplicate data, filling missing values, and correcting erroneous data. The formatting and repair are used to unify the data format, improve data compatibility, and provide a reliable data foundation for subsequent feature extraction and model training.

[0016] As a further improvement of the present invention, the feature extraction uses statistical methods to analyze user behavior patterns, time series analysis of system operations, and frequency analysis to identify abnormal activities. These features can comprehensively reflect the security status in the cloud environment and provide key information for model training.

[0017] As a further improvement of the present invention, the machine learning algorithm includes deep learning algorithm, support vector machine and random forest.

[0018] As a further improvement of the present invention, the safety warning model adopts an integrated learning method to fuse the prediction results of multiple machine learning models to further improve the prediction performance of the overall model.

[0019] As a further improvement of the present invention, the model parameter adjustment process is based on a real-time feedback mechanism. By monitoring the performance of the model on new data, the learning rate and regularization parameters are dynamically adjusted to adapt to changes in the security status of the cloud environment, ensuring that the model always remains in the best working state.

[0020] As a further improvement of the present invention, the warning information generation process adopts natural language generation technology to convert complex prediction results into easy-to-understand text descriptions, including specific threat nature, possible impacts and response strategies.

[0021] As a further improvement of the present invention, the warning information sending step has a multi-channel notification mechanism, which can promptly notify the cloud service provider and users of the warning information via email, text messages, and instant messaging tools to ensure rapid transmission and response of information.

[0022] Beneficial effects of the present invention: (1) Active prediction and early warning: The present invention uses machine learning algorithms to perform real-time analysis and prediction of log data in the cloud environment, and can actively discover potential security threats and vulnerabilities. Compared with traditional passive defense methods, this method can identify and warn of unknown or new security threats in advance, thereby greatly improving the security and defense capabilities of the cloud environment. Users can take timely measures based on the early warning information to avoid the occurrence of security incidents and protect business continuity and data security.

[0023] (2) High accuracy and low false alarm rate: The present invention adopts multiple machine learning algorithms and ensemble learning methods to improve the prediction accuracy and robustness of the model by comprehensively analyzing multiple features. The real-time feedback mechanism that automatically adjusts the model parameters can dynamically optimize the model performance and reduce false alarms and missed alarms. This not only improves the reliability of the warning, but also reduces the unnecessary alertness and workload of users caused by false alarms, ensuring that users can focus on real security issues.

[0024] (4) Multi-platform compatibility and multi-functional notification: The present invention supports the collection of log data from multiple mainstream cloud service platforms (such as AWS, Azure, and Google Cloud), has the ability to integrate data across platforms, and is suitable for various cloud environments. The early warning information generation process uses natural language generation technology to convert complex prediction results into easy-to-understand text descriptions, including the specific nature of the threat, possible impacts, and response strategies. The multi-channel notification mechanism promptly notifies cloud service providers and users of early warning information through various means such as email, text messages, and instant messaging tools, ensuring rapid information delivery and response, and improving the overall security management level and emergency response speed. DETAILED DESCRIPTION

[0025] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present application more clearly understood, the present application is further described in detail below in conjunction with the embodiments. It should be understood that the embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0026] The present invention provides a cloud security vulnerability early warning method based on machine learning, comprising the following steps:

[0027] Step 1: Collect log data in multiple cloud service environments. The log data includes but is not limited to access records, operation records, and abnormal warnings.

[0028] The log data collection in the present invention has the technical means of cross-platform data integration, supporting data access to AWS, Azure, and Google Cloud cloud service platforms;

[0029] Step 2: Preprocess the collected log data, including data cleaning, formatting, and repair to ensure data quality and consistency;

[0030] The preprocessing of log data in the present invention adopts a data repair algorithm, which can automatically identify and repair missing values ​​and outliers in the data, thereby improving the accuracy of subsequent analysis;

[0031] Data cleaning in the present invention includes but is not limited to removing duplicate data, filling missing values, correcting erroneous data, formatting and repairing to unify data formats, improve data compatibility, and provide a reliable data foundation for subsequent feature extraction and model training;

[0032] Step 3: Extract multiple features from the preprocessed log data, including but not limited to user behavior patterns, system operation time series, and abnormal activity frequency;

[0033] The feature extraction in the present invention uses statistical methods to analyze user behavior patterns, time series analysis of system operations, and frequency analysis to identify abnormal activities. These features can fully reflect the security status of the cloud environment and provide key information for model training;

[0034] Step 4: Use multiple machine learning algorithms to train a security warning model, which is used to predict potential vulnerabilities and security risks in the cloud environment;

[0035] The machine learning algorithms in the present invention include deep learning algorithms, support vector machines and random forests;

[0036] The safety warning model in this invention adopts an integrated learning method to integrate the prediction results of multiple machine learning models to further improve the prediction performance of the overall model.

[0037] Step 5: Apply the trained security warning model to the newly generated log data. When the model output shows a possible security threat, the warning mechanism is triggered.

[0038] Step 6: Automatically adjust model parameters to optimize warning accuracy and reduce false positives and missed positives;

[0039] The model parameter adjustment process in the present invention is based on a real-time feedback mechanism. By monitoring the performance of the model on new data, the learning rate and regularization parameters are dynamically adjusted to adapt to changes in the security status of the cloud environment, ensuring that the model always remains in the best working state;

[0040] Step 7: Generate warning information based on the model prediction results, including but not limited to threat level, impact scope, and recommended solutions, and send the information to the cloud service provider and user;

[0041] The early warning information generation process in the present invention uses natural language generation technology to convert complex prediction results into easy-to-understand text descriptions, including the specific nature of the threat, possible impacts and response strategies;

[0042] The step of sending the early warning information in the present invention has a multi-channel notification mechanism, which can promptly notify the cloud service provider and the user of the early warning information via email, text message, and instant messaging tools, thereby ensuring rapid transmission and response of the information.

[0043] Embodiment 1:

[0044] This embodiment provides a cloud security vulnerability early warning method based on machine learning, and the specific steps are as follows:

[0045] 1. Log data collection

[0046] Collect log data in multiple cloud service environments (such as AWS, Azure, and Google Cloud). These log data include but are not limited to access records, operation records, and abnormal warnings. Through cross-platform data integration technology, log data can be automatically extracted from different cloud service platforms and aggregated into a unified data warehouse. For example, you can use services such as AWS CloudTrail, Azure Monitor, and Google Cloud Logging to collect log data.

[0047] 2. Log data preprocessing

[0048] The collected raw log data may contain duplicate records, missing values, and outliers, and needs to be preprocessed to ensure the quality and consistency of the data. The specific preprocessing steps include: (1) De-duplicate data: Delete duplicate log records through unique identifiers (such as the timestamp and operation ID of the log record). (2) Fill missing values: For missing data fields, you can use the mean, median, or mode to fill, or fill them through interpolation methods. (3) Correct erroneous data: Identify and correct erroneous data records through data repair algorithms. (4) Format data: Unify log data from different platforms into a standard format to improve data compatibility. For example, all timestamps can be converted to UTC time format, and all operation records can be standardized into a unified JSON format.

[0049] (III) Feature extraction

[0050] Extract multiple features from the preprocessed log data, including but not limited to user behavior patterns, system operation time series, and abnormal activity frequency. Specific feature extraction methods include: (1) User behavior patterns: Analyze the user's access frequency, access time, access path, etc. through statistical methods to generate user behavior features. For example, the number of visits per user in a day, the duration of each visit, and the type of resources accessed can be calculated. (2) System operation time series: Use time series analysis methods to analyze the time pattern of system operations. For example, time series data of operations such as system startup, restart, and configuration changes can be extracted to identify possible abnormal operation patterns. (3) Abnormal activity frequency: Use frequency analysis methods to identify the frequency of abnormal activities in the log. For example, the number of abnormal warnings per minute, the frequency of abnormal operations, etc. can be calculated.

[0051] (IV) Model training

[0052] Use a variety of machine learning algorithms to train the security warning model, including deep learning algorithms, support vector machines (SVM) and random forests. Through ensemble learning methods, the prediction results of multiple machine learning models are integrated to further improve the prediction performance of the overall model. For example, deep learning algorithms (such as LSTM) can be used to capture complex patterns in time series data, SVM can be used to process high-dimensional feature data, and random forests can be used to process noise and outliers in large-scale data sets.

[0053] (V) Model application and early warning

[0054] Apply the trained security warning model to the newly generated log data, and trigger the warning mechanism when the model output shows a possible security threat. For example, the newly generated log data can be passed to the model in real time for prediction through real-time stream processing technology (such as Apache Kafka and Apache Flink). If the model detects abnormal activity, the warning mechanism is immediately triggered and warning information is generated.

[0055] (VI) Model parameter adjustment

[0056] Automatically adjust model parameters to optimize warning accuracy, reduce false positives and negatives, monitor the performance of the model on new data through real-time feedback mechanisms, and dynamically adjust learning rates and regularization parameters. For example, online learning methods can be used to continuously update model parameters based on feedback from new data to ensure that the model is always in the best working state in a constantly changing cloud environment.

[0057] (VII) Generation and sending of early warning information

[0058] Based on the model prediction results, early warning information is generated, including but not limited to threat level, impact scope and recommended solutions, and the information is sent to cloud service providers and users. The specific process is as follows: (1) Threat level: Determine the threat level (such as low, medium, and high) based on the confidence of the prediction results and historical data. (2) Impact scope: Analyze the resources and services that may be affected by abnormal activities and generate a description of the impact scope. (3) Recommended solutions: Generate specific response strategies based on the nature of the threat and the impact scope. For example, it is recommended to close specific ports, modify access control policies, or restart the affected systems. (4) Multi-channel notification: The early warning information is promptly notified to relevant parties through various means such as email, SMS, and instant messaging tools to ensure rapid information delivery and response. For example, AWS SNS (Simple Notification Service) and SMTP protocols can be used to send early warning notifications.

[0059] Embodiment 2:

[0060] This embodiment provides a specific application scenario of a cloud security vulnerability early warning method based on machine learning, which is described in detail as follows:

[0061] 1. Log data collection

[0062] In the cloud environment of a large enterprise, different business systems are deployed using multiple cloud service platforms (such as AWS, Azure, and Google Cloud). Log data of these systems are automatically collected by integrating log collection tools such as AWS CloudTrail, Azure Monitor, and Google Cloud Logging. The log data includes system operation records, user access records, abnormal warnings, etc. These data are aggregated into a unified log management platform (such as Elasticsearch).

[0063] 2. Log data preprocessing

[0064] The collected raw log data is transferred to a data preprocessing module. This module performs the following steps: (1) De-duplicate data: Delete duplicate log records through unique identifiers (such as the timestamp and operation ID of the log record). (2) Fill missing values: For missing data fields, use the mean filling method to fill them. For example, if the access time field of a user is missing, the mean of the user's historical access time can be used to fill it. (3) Correct erroneous data: Identify and correct erroneous data records through data repair algorithms (such as rule-based data cleaning and statistical-based outlier detection). (4) Format data: Unify the log data of different platforms into JSON format to ensure data standardization and compatibility.

[0065] (III) Feature extraction

[0066] Extract a variety of features from the preprocessed log data, including: (1) User behavior patterns: Generate user behavior features by counting the user's access frequency, access time, and access path. For example, you can count the number of times each user accesses a specific resource in a week and analyze the distribution of their access time. (2) System operation time series: Use time series analysis methods to analyze the time patterns of system startup, restart, configuration change, and other operations. For example, you can extract the startup and restart time of each system in a day and generate a time series chart. (3) Abnormal activity frequency: Use frequency analysis methods to identify the frequency of abnormal activities in the log. For example, you can count the number of abnormal warnings per hour and generate a frequency distribution chart.

[0067] (IV) Model training

[0068] The security warning model is trained using a variety of machine learning algorithms, including: (1) Deep learning algorithm: Use LSTM (Long Short-Term Memory Network) to capture complex patterns in the system operation time series. (2) Support Vector Machine (SVM): Process high-dimensional feature data, such as user behavior patterns and abnormal activity frequencies. (3) Random Forest: Process noise and outliers in large-scale data sets to improve the robustness of the model.

[0069] Through the ensemble learning method, the prediction results of the above multiple models are integrated to generate the final safety warning model. For example, the prediction results of multiple models can be integrated using the voting method or weighted average method to improve the overall prediction performance.

[0070] (V) Model application and early warning

[0071] Apply the trained security warning model to the newly generated log data. When the model output shows a possible security threat, the warning mechanism is triggered. The specific steps are as follows: (1) Real-time stream processing: Use Apache Flink to process the newly generated log data and pass it to the model in real time for prediction. (2) Warning triggering: If the model detects abnormal activity, the warning mechanism is immediately triggered and warning information is generated. (3) Warning information: The warning information includes the threat level (such as low, medium, and high), the scope of impact (such as affected resources and services), and recommended solutions (such as closing specific ports and modifying access control policies).

[0072] (VI) Model parameter adjustment

[0073] Through the real-time feedback mechanism, the model parameters are automatically adjusted to optimize the warning accuracy and reduce false positives and negatives. The specific methods are as follows: (1) Real-time monitoring: Use real-time monitoring tools (such as Prometheus and Grafana) to monitor the performance of the model on new data and record the model's prediction results and actual results. (2) Parameter adjustment: Dynamically adjust the model's learning rate and regularization parameters based on monitoring data. For example, if a high false positive rate is detected, the learning rate can be appropriately reduced and the regularization parameters can be increased to improve the stability of the model.

[0074] (VII) Generation and sending of early warning information

[0075] Based on the model prediction results, early warning information is generated and sent to relevant parties through multiple channels. The specific process is as follows: (1) Threat level: Determine the threat level based on the confidence of the prediction results and historical data. For example, if the confidence exceeds 90% and there are records of similar threats in the historical data, it can be classified as a "high" level. (2) Impact scope: Analyze the resources and services that may be affected by abnormal activities and generate a description of the impact scope. For example, if a user's access behavior is found to be abnormal, the resources accessed by the user can be further analyzed and a list of affected resources can be generated. (3) Recommended solutions: Generate specific response strategies based on the nature of the threat and the scope of impact. For example, it is recommended to shut down the network connection of the affected resources, restart the system, or modify the access control policy. (4) Multi-channel notification: Send early warning information to relevant parties through various methods such as email, SMS, and instant messaging tools. For example, you can use AWS SNS to send SMS notifications, use the SMTP protocol to send email notifications, and send instant message notifications through enterprise instant messaging tools (such as Slack or WeChat for Enterprise).

[0076] Through the above embodiments, the present invention provides a cloud security vulnerability early warning method based on machine learning, which can effectively respond to various security threats in the cloud environment and improve security and response speed through automated and intelligent means. The method not only supports cross-platform data integration, but also has the characteristics of high-precision prediction and low false alarm rate, and is suitable for security management of various cloud environments.

[0077] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A cloud security vulnerability early warning method based on machine learning, characterized in that: The following steps are involved: Step 1: Collect log data in multiple cloud service environments, including but not limited to access records, operation records, and abnormal warnings; Step 2: Preprocess the collected log data, including data cleaning, formatting, and repair to ensure data quality and consistency; Step 3: Extract multiple features from the preprocessed log data, including but not limited to user behavior patterns, system operation time series, and abnormal activity frequency; Step 4: Use multiple machine learning algorithms to train a security warning model, which is used to predict potential vulnerabilities and security risks in the cloud environment; Step 5: Apply the trained security warning model to the newly generated log data. When the model output shows a possible security threat, the warning mechanism is triggered. Step 6: Automatically adjust model parameters to optimize warning accuracy and reduce false positives and missed positives; Step 7: Generate warning information based on the model prediction results, including but not limited to threat level, impact scope, and recommended solutions, and send the information to cloud service providers and users.

2. According to claim 1, a cloud security vulnerability early warning method based on machine learning is characterized in that: The log data collection has the technical means of cross-platform data integration and supports data access to AWS, Azure, and Google Cloud cloud service platforms.

3. The cloud security vulnerability early warning method based on machine learning according to claim 1 is characterized in that: The preprocessing of the log data adopts a data repair algorithm, which can automatically identify and repair missing values ​​and abnormal values ​​in the data, thereby improving the accuracy of subsequent analysis.

4. The cloud security vulnerability early warning method based on machine learning according to claim 1 is characterized in that: The data cleaning includes but is not limited to removing duplicate data, filling missing values, and correcting erroneous data. The formatting and repairing are used to unify the data format, improve data compatibility, and provide a reliable data foundation for subsequent feature extraction and model training.

5. The cloud security vulnerability early warning method based on machine learning according to claim 1 is characterized in that: The feature extraction uses statistical methods to analyze user behavior patterns, time series analysis of system operations, and frequency analysis to identify abnormal activities. These features can comprehensively reflect the security status of the cloud environment and provide key information for model training.

6. The cloud security vulnerability early warning method based on machine learning according to claim 1 is characterized in that: The machine learning algorithms include deep learning algorithms, support vector machines and random forests.

7. The cloud security vulnerability early warning method based on machine learning according to claim 1 is characterized in that: The safety warning model adopts an integrated learning method to fuse the prediction results of multiple machine learning models to further improve the prediction performance of the overall model.

8. The cloud security vulnerability early warning method based on machine learning according to claim 1 is characterized in that: The model parameter adjustment process is based on a real-time feedback mechanism. By monitoring the performance of the model on new data, the learning rate and regularization parameters are dynamically adjusted to adapt to changes in the security status of the cloud environment, ensuring that the model always remains in the best working state.

9. The cloud security vulnerability early warning method based on machine learning according to claim 1 is characterized in that: The warning information generation process adopts natural language generation technology to convert complex prediction results into easy-to-understand text descriptions, including the specific nature of the threat, possible impacts and response strategies.

10. The cloud security vulnerability early warning method based on machine learning according to claim 1, characterized in that: The step of sending the early warning information has a multi-channel notification mechanism, which can promptly notify the cloud service provider and users of the early warning information via email, SMS, and instant messaging tools, ensuring rapid delivery and response of information.