Machine Learning-Based Instant Identification Method and System for High-Risk Vulnerabilities

Abstract

The present invention provides a method and system for instant identification of high-risk vulnerabilities based on machine learning, which relates to the technical fields of network security and software engineering. It includes collecting data; preprocessing the collected code and vulnerability descriptions; performing static code analysis, then dynamic code analysis, constructing a machine learning model, extracting code quality metrics, collecting runtime data for training the machine learning model; training and testing multiple models, using model fusion technology to combine the predictions of these models; integrating the trained model into CI / CD tools; generating adversarial samples, performing data encryption testing and improving the robustness of the model; performing model interpretation; analyzing the contributing factors of individual predictions, interpreting and visualizing the global model performance; and updating the model, regularly updating the model parameters. The present invention automates and makes real-time the vulnerability detection process, provides more accurate predictions than a single model, and significantly improves the efficiency of software development and maintenance.

Description

Technical Field

[0001] The present invention relates to the technical fields of network security and software engineering, and particularly to an instant identification method and system for high-risk vulnerabilities based on machine learning. Background Art

[0002] In the current field of network security, the timely discovery and repair of software vulnerabilities are crucial for preventing security attacks and data breaches. As software systems become increasingly complex, traditional vulnerability detection methods, including manual reviews and rule-based automated tools, have been unable to effectively cope with newly emerging vulnerabilities and growing security threats. These traditional methods are often time-consuming and inefficient, and have obvious limitations when dealing with large-scale and complex codebases.

[0003] In recent years, the application of machine learning in the field of vulnerability detection has attracted wide attention. By analyzing historical vulnerability data, machine learning methods can identify potential vulnerability patterns and features, thereby predicting and identifying new vulnerabilities. However, existing machine learning-based vulnerability detection methods face several major challenges: insufficient quality and diversity of datasets, limited model generalization ability, weak real-time detection and response capabilities, and insufficient model interpretability.

[0004] In the existing field of vulnerability detection, for example, the publication number is CN116595530A, and the name is "An intelligent contract vulnerability detection method combining adversarial transfer learning and multi-task learning" applied and published by the National Intellectual Property Administration on August 15, 2023, aiming to improve the accuracy and efficiency of vulnerability detection. This method extracts general and private features by constructing a multi-task learning framework and uses adversarial transfer learning to alleviate the mutual interference between these features. However, this method has some limitations in practical applications and is relatively complex to implement. The multi-task learning framework and adversarial transfer learning mechanism require a carefully designed neural network structure, making the tuning and maintenance of the model more difficult. Secondly, although this method can reduce the interference between features, its generalization ability and adaptability may be limited when dealing with new or unknown types of vulnerabilities. In addition, its requirements for real-time performance and flexibility may be difficult to meet in a rapidly changing network security environment, especially in the security scenario of smart contracts that require rapid response.

[0005] Therefore, there is an urgent need for an efficient, accurate, and reliable method to improve the instant identification and response of software vulnerabilities. This method needs to be able to process and analyze large-scale complex data, have strong real-time monitoring and response capabilities, and be able to continuously adapt and learn as new vulnerability patterns emerge. At the same time, it is also necessary to ensure the security of data and models to ensure that no new security risks are introduced during the entire vulnerability detection process. Summary of the Invention

[0006] To overcome the deficiencies of the prior art, the present invention proposes a method and system for instant identification of high-risk vulnerabilities based on machine learning. By combining static and dynamic analysis, as well as natural language processing techniques, the structural, behavioral, and semantic features of the code are extracted comprehensively. Such technical features enable the system to apply automated and deep learning means to more accurately identify potential vulnerabilities and security threats. Thereby, the accuracy and comprehensiveness of vulnerability detection are improved, the risks of vulnerability omission and false alarm are reduced, and the security and reliability of software products are ensured.

[0007] To achieve the above object, the present invention provides a method for instant identification of high-risk vulnerabilities based on machine learning, including:

[0008] Step S1: Use the Python crawler library Scrapy to collect code data from vulnerability databases (such as CVE, NVD) and code repositories such as GitHub; use the natural language processing library NLTK to preprocess the collected code data, and use a code mutation tool such as PIT Mutation Testing Framework to enhance the data set.

[0009] Step S2: Apply SonarQube for static code analysis, use GDB or Valgrind for dynamic code analysis, and use TensorFlow or PyTorch to build a machine learning model; use SonarQube to extract code quality metrics, collect runtime data through GDB, and finally train a CNN model through TensorFlow;

[0010] Step S3: Use Scikit-learn for algorithm implementation, such as random forest and support vector machine; train and test multiple machine learning models in Scikit-learn (including but not limited to support vector machine (SVM), decision tree, random forest, logistic regression, deep neural network, etc. These models are trained and tested in Scikit-learn, and each model learns to identify the patterns of vulnerabilities from the data according to its specific algorithm and feature extraction method), and then use model fusion techniques (such as stacking) to combine the predictions of these machine learning models;

[0011] Step S4: Integrate the trained and fused models (referring to the machine learning models trained and optimized in Step S3, which are integrated into CI / CD tools such as Jenkins. These machine learning models are used to perform real-time vulnerability scanning and analysis when code is committed to a version control system such as Git. The models here are usually the final versions optimized through training and ensemble learning and have the ability to be directly applied to real-time data) into CI / CD tools such as Jenkins or Travis CI; when code is committed to a version control system (such as Git), automatically trigger the models in Jenkins for real-time scanning and analysis;

[0012] Step S5: Use the Adversarial Robustness Toolbox to generate adversarial samples, thereby enhancing the security of machine learning models and improving the robustness of machine learning models under adversarial attacks;

[0013] Step S6: Use LIME or SHAP for model interpretation; use LIME to analyze the contributing factors of individual predictions, and use SHAP for the interpretation and visualization of global model performance;

[0014] Step S7: Use an online learning library such as River to update the model; regularly use the River library to update the model parameters to cope with new types of vulnerabilities and code patterns.

[0015] Further, Step S1 is specifically as follows:

[0016] Step S11: First, select a certain number of sample codes from the dataset. These codes should contain various types of software vulnerabilities.

[0017] Step S12: Use a specialized code mutation tool (such as the PIT Mutation Testing Framework) to perform mutation operations on the selected sample codes.

[0018] Step S13: Generate different code mutants by modifying certain elements in the code (such as operators, conditional statements, return values, etc.).

[0019] Step S14: Run the mutants and check whether their behaviors meet the expectations, ensuring that the mutants are functionally similar to the original code but structurally different.

[0020] Step S15: Merge the verified mutants into the original dataset to increase the diversity and complexity of the dataset.

[0021] Further, Step S2 specifically includes:

[0022] Step S21: Process the code text using NLP techniques (such as a lexical analyzer and a syntax analyzer) to extract information such as vocabulary and syntax structures; use an NLP library (such as NLTK or spaCy) to analyze code comments and documentation to extract semantic information of the code.

[0023] Step S22: Use a static analysis tool (such as SonarQube) to analyze the structure, complexity, possible vulnerabilities, code style, etc. of the code, and extract features such as function definitions, class structures, variable usage, etc. At the same time, extract code quality metrics, where the code quality metrics include the complexity of the code, programming standards adhered to, and potential error patterns.

[0024] Step S23: Run the code and use a tool (such as Valgrind) to monitor the runtime behavior of the program and collect runtime data such as memory usage, execution path, response time, etc.

[0025] Step S24: Combine the results of NLP and static and dynamic analyses to form a comprehensive feature set.

[0026] Step S25: Select a machine learning model suitable for processing code analysis data, such as a convolutional neural network (CNN) to analyze the code structure, or a recurrent neural network (RNN) to process sequence information in the code.

[0027] Step S26: Convert the extracted features into a format that the model can process, such as converting the code structure into a vector or matrix form.

[0028] Step S27: Use a deep learning framework (such as TensorFlow or PyTorch) to train the model.

[0029] Step S28: Provide a large amount of labeled training data to enable the model to learn patterns for identifying vulnerabilities from the semantic information of the code, the results of code structure and complexity analysis, runtime behavior data, and code quality metrics.

[0030] Step S29: Optimize the model, such as adjusting the number of layers and neurons in the neural network, etc., to improve the accuracy and efficiency of the model, and use a validation dataset to test the model performance to ensure its effectiveness on new data.

[0031] Further, step S3 specifically includes:

[0032] Step S31: Prepare a labeled dataset, where each sample has a label (for example, whether there is a vulnerability), and perform data cleaning and preprocessing, such as feature standardization and missing value handling.

[0033] Step S32: Select a supervised learning algorithm suitable for the task, such as Support Vector Machine (SVM), Random Forest, Logistic Regression, etc.

[0034] Step S33: Use the selected algorithm to train a model on the training data. This usually involves adjusting the model parameters to minimize the prediction error. For example, for SVM, the goal is to find a hyperplane that can maximize the margin between classes.

[0035] Step S34: Evaluate the model performance on an independent test dataset using methods such as cross-validation.

[0036] Furthermore, Step S4 is specifically as follows:

[0037] Step S41: Deploy the trained model as a service, which can be a local server or a cloud service. Ensure that this service can receive code data and return vulnerability detection results.

[0038] Step S42: Create a new process or task in CI / CD tools (such as Jenkins, Travis CI, CircleCI, etc.).

[0039] Step S43: Configure the process to be automatically triggered when code is committed to the version control system (such as Git). Add a step in the process to call the deployed model service to perform real-time scanning of the committed code.

[0040] Step S44: Configure the CI / CD tool to handle the return results of the model service. For example, if a vulnerability is detected, an issue report or warning can be automatically created.

[0041] Step S45: Set up a notification mechanism, such as notifying relevant developers when a vulnerability is detected, and provide an interface or system that enables developers to provide feedback on the detection results.

[0042] Step S46: Implement a feedback system that allows users to report the accuracy of vulnerability detection, including false positives and false negatives. Regularly analyze the collected user feedback to identify common false positive or false negative patterns.

[0043] Step S47: Adjust and optimize the model based on the user feedback data. This may involve retraining the model or adjusting its parameters, redeploying the optimized model to the service, and updating the relevant configurations in the CI / CD process.

[0044] Step S48: Continuously monitor the model performance and user feedback to ensure that the model continuously adapts to new code patterns and vulnerabilities.

[0045] Furthermore, Step S5 includes:

[0046] Step S51: Generate adversarial samples using specialized tools (such as Adversarial Robustness Toolbox). These samples mislead machine learning models by making small, calculated adjustments to the original data, ensuring that these samples appear similar to the original samples to human observers but can deceive the model.

[0047] Step S52: Add these adversarial samples to the training set and retrain the model using the enhanced training set so that it can recognize and correctly process these adversarial samples.

[0048] Step S53: Evaluate the performance of the model on a test set containing adversarial samples and repeat the above steps as needed until the model can effectively recognize adversarial samples.

[0049] Step S54: Select a suitable encryption algorithm, such as AES (Advanced Encryption Standard) or RSA, to protect data security and implement encryption during data storage and transmission. For example, encrypt sensitive data stored in the database and ensure the use of secure protocols such as HTTPS during data transmission.

[0050] Step S55: Manage encryption keys securely. Ensure that only authorized users and systems can access the keys, update the keys regularly, and implement strict data access control measures. Ensure that only authorized individuals and systems can access or process encrypted data.

[0051] Further, Step S6 includes:

[0052] Step S61: Select a suitable interpretable AI tool, such as LIME (Local Interpretable Model-agnostic Explanations) or SHAP (Shapley Additive exPlanations).

[0053] Step S62: Apply the selected tool to the trained machine learning model. For example, use LIME to analyze the decision-making process of the model for a specific prediction.

[0054] Step S63: Generate an explanation report showing the main features considered by the model during prediction and their contribution degrees, and analyze these explanations to understand the decision-making logic of the model.

[0055] Step S64: Use visualization tools to present the explanation results to users, such as showing the importance of features through charts or graphs.

[0056] Step S65: Regularly generate performance reports of the model, including key metrics such as accuracy, recall, F1 score, etc., and include details of model training and testing in the report, such as the datasets used, model types, training parameters, etc.

[0057] Step S66: Present the performance report in a user-friendly manner, such as making the information easy to understand through charts and graphs.

[0058] Step S67: Provide a mechanism for users to give feedback on the report and their feelings about the model's performance. Regularly update the performance report based on user feedback and changes in the model's performance to keep the information up-to-date and relevant.

[0059] Furthermore, Step S7 includes:

[0060] Step S71: Select machine learning algorithms that can handle streaming data, such as Online Stochastic Gradient Descent, Online Decision Trees, etc.

[0061] Step S72: Design the system to continuously receive and process new data streams. This may include collecting data in real time from the production environment or obtaining data from regularly updated data sources. As new data arrives, continuously update the model's parameters. This means the model can continuously learn and adapt based on the latest data.

[0062] Step S73: Dynamically adjust the learning rate and other hyperparameters according to the model's performance on new data to optimize performance, update and adjust to address new threats

[0063] Step S74: Regularly analyze the latest trends and threat intelligence in the security field to identify potential new vulnerabilities and attack patterns.

[0064] Step S75: Update the training dataset based on the results of the trend analysis, including adding samples representing new threats.

[0065] Step S76: Retrain the model using the updated dataset to ensure the model can identify and address new types of vulnerabilities. Continuously monitor the model's performance in the actual environment, especially its ability to detect new threats. Establish a feedback mechanism that allows users to report the accuracy of vulnerability detection, and then continue to optimize the model.

[0066] A machine learning-based high-risk vulnerability instant identification system, including:

[0067] A data processing and enhancement module for collecting data from vulnerability databases and code repositories using the Python crawler library Scrapy; preprocessing the collected code and vulnerability descriptions using the natural language processing library NLTK, and enhancing the dataset using code mutation tools;

[0068] The feature extraction and analysis module is used to perform static code analysis using SonarQube, then conduct dynamic code analysis, and build a machine learning model; extract code quality metrics using SonarQube, collect runtime data through GDB, and finally train a machine learning model using TensorFlow to automatically learn code features;

[0069] The model training and integration module is used to implement algorithms using Scikit-learn; train and test a machine learning model in Scikit-learn, and then combine the predictions of machine learning models using model fusion techniques;

[0070] The deployment and real-time monitoring module is used to integrate the trained machine learning model into CI / CD tools; perform real-time scanning and analysis when code is committed to the version control system;

[0071] The security and privacy protection module is used to generate adversarial samples using the Adversarial Robustness Toolbox and perform data encryption using OpenSSL; test and improve the robustness of the machine learning model using the Adversarial Robustness Toolbox and encrypt sensitive data using OpenSSL;

[0072] The explanation and visualization module is used to analyze the contributing factors of individual predictions using LIME and perform the explanation and visualization of the global model performance using SHAP;

[0073] The continuous learning and adaptation module is used to update the machine learning model using an online learning library; regularly update the parameters of the machine learning model to cope with new types of vulnerabilities and code patterns;

[0074] Compared with the prior art, the beneficial effects of the present invention are:

[0075] 1. The present invention provides a method and system for instant identification of high-risk vulnerabilities based on machine learning. By combining static and dynamic analysis, as well as natural language processing techniques, the structural, behavioral, and semantic features of code are extracted in all aspects. Such technical features enable the system to apply automated and deep learning means to more accurately identify potential vulnerabilities and security threats. Thereby, the accuracy and comprehensiveness of vulnerability detection are improved, the risks of vulnerability omission and false alarm are reduced, and the security and reliability of software products are guaranteed.

[0076] 2. The present invention provides a method and system for instant identification of high-risk vulnerabilities based on machine learning, integrating the model training and real-time monitoring functions into the CI / CD process, making the vulnerability detection process automated and real-time. The application of ensemble learning technology combines the advantages of multiple models, providing more accurate predictions than a single model. This technical configuration enables the development team to quickly discover and fix vulnerabilities at the early stage of code submission, significantly improving the efficiency of software development and maintenance.

[0077] 3. The present invention provides a method and system for instant identification of high-risk vulnerabilities based on machine learning. Through adversarial sample training and data encryption technology, the security of the system is enhanced, enabling it to better resist external attacks and protect sensitive data. The continuous adaptation mechanism of online learning and trend analysis ensures that the model can promptly adapt to newly emerging threats and attack patterns. This technical arrangement not only improves the instant response ability of the model but also guarantees the long-term security and reliability of the system, enhancing users' trust in the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0079] Figure 1 It is a schematic diagram of the step process of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0080] The following will more clearly and completely elaborate on the technical solutions of the present invention by describing the preferred embodiments of the present invention in conjunction with the drawings.

[0081] The professional terms involved in the present invention are specifically as follows:

[0082] Python: Python (a popular programming language)

[0083] Scrapy: Scrapy (an open-source web crawler framework)

[0084] CVE: CVE (Common Vulnerabilities and Exposures, a public vulnerability database)

[0085] NVD: NVD (National Vulnerability Database)

[0086] GitHub: GitHub (a code hosting platform)

[0087] NLTK: Natural Language Toolkit

[0088] PIT Mutation Testing Framework: PIT Mutation Testing Framework (an automated testing tool for Java applications)

[0089] SonarQube: SonarQube (a code quality management platform)

[0090] GDB: GDB (GNU Debugger, a program debugging tool)

[0091] Valgrind: Valgrind (a memory debugging tool)

[0092] TensorFlow: TensorFlow (an open - source machine learning library)

[0093] PyTorch: PyTorch (an open - source machine learning library)

[0094] Scikit - learn: Scikit - learn (an open - source machine learning library)

[0095] Jenkins: Jenkins (an automation server mainly used for continuous integration and continuous delivery)

[0096] Travis CI: Travis CI (a service for continuous integration)

[0097] Adversarial Robustness Toolbox: Adversarial Robustness Toolbox (a library for enhancing the security of machine learning models)

[0098] OpenSSL: OpenSSL (a powerful Secure Sockets Layer cryptographic library)

[0099] LIME: LIME (Local Interpretable Model - agnostic Explanations, a tool for explaining the decisions of machine learning models)

[0100] SHAP: SHAP (Shapley Additive exPlanations, a tool for explaining machine learning models)

[0101] River: An online machine learning library

[0102] These tools and libraries play a crucial role in building a machine - learning - based vulnerability detection system, enabling the system to effectively identify and respond to various vulnerabilities.

[0103] As Figure 1 shown, the present invention is as follows:

[0104] Step S1: Use the Python crawler library Scrapy to collect code data from vulnerability databases (such as CVE, NVD) and code repositories such as GitHub; use the natural language processing library NLTK to preprocess the collected code data, and use code mutation tools such as PIT Mutation Testing Framework to enhance the dataset.

[0105] Step S2: Apply SonarQube for static code analysis, use GDB or Valgrind for dynamic code analysis, and use TensorFlow or PyTorch to build a machine learning model; use SonarQube to extract code quality metrics, collect runtime data through GDB, and finally train a CNN model (i.e., one of the machine learning models) through TensorFlow;

[0106] S2 provides a feature set for training the machine learning model to identify and predict possible vulnerabilities in the code, and this data helps the model understand the behavior of the code and potential risk points.

[0107] And S3 involves the actual model training and testing process.

[0108] Step S3: Use Scikit-learn for algorithm implementation, such as random forest and support vector machine; train and test multiple models in Scikit-learn, and then use model fusion techniques (such as stacking) to combine the predictions of these models;

[0109] Step S4: Integrate the trained and fused models into CI / CD tools such as Jenkins or Travis CI; when the code is submitted to the version control system (such as Git), automatically trigger the model in Jenkins for real-time scanning and analysis;

[0110] Step S5: Use the Adversarial Robustness Toolbox to generate adversarial samples, and use OpenSSL for data encryption; use the Adversarial Robustness Toolbox to test and improve the robustness of the model, and use OpenSSL to encrypt sensitive data;

[0111] Step S5 involves enhancing the robustness of the model using the Adversarial Robustness Toolbox, which directly affects the performance of the model integrated into the CI / CD tool in S4. Meanwhile, the model enhancement measures in S5 provide support for the accuracy of model interpretation in S6.

[0112] Step S6: Use LIME or SHAP for model interpretation; analyze the contributing factors of individual predictions using LIME, and use SHAP for the interpretation and visualization of the global model performance;

[0113] The analysis results in Step S6 are used to improve the interpretability and transparency of the model, helping developers and security analysts understand how the model makes vulnerability predictions. This helps enhance trust in the model's predictions and make adjustments and optimizations if necessary.

[0114] The global model refers to the comprehensive model used by the entire system, including deep learning and other machine learning methods; it covers all the models used in the entire process from data preprocessing to prediction output.

[0115] Step S7: Update the model using an online learning library such as River; regularly update the model parameters using the River library to cope with new types of vulnerabilities and code patterns.

[0116] In Step S1, data collection and preprocessing are carried out by combining Python crawlers and the natural language processing library NLTK, and a code mutation tool (such as the PIT Mutation Testing Framework) is used to enhance the dataset. A more abundant and diverse data source is provided, enhancing the representativeness of the training data, thereby improving the application performance of the model in real-world problems.

[0117] In Step S2, SonarQube is used for comprehensive static code analysis, combined with GDB or Valgrind for dynamic analysis, and then a machine learning model (such as based on TensorFlow) is constructed. Combining the static and dynamic analysis results provides more comprehensive code features, helping the machine learning model learn and identify potential vulnerabilities in the code more accurately.

[0118] In Step S3, multiple algorithms (such as random forest, support vector machine, etc.) are implemented using Scikit-learn, and model fusion techniques such as stacking are adopted. The fusion of multiple models improves the accuracy and robustness of predictions, enabling the system to more effectively identify various types of vulnerabilities.

[0119] In step S4, the trained model is integrated into CI / CD tools such as Jenkins or Travis CI. This enables automatic vulnerability detection and instant feedback during the CI / CD phase of software development. The automation and timeliness of vulnerability detection are achieved, significantly improving the efficiency and security of software development and maintenance.

[0120] In step S5, the Adversarial Robustness Toolbox is combined to generate adversarial samples for testing, and OpenSSL is used to encrypt the data, enhancing the security of the model and the data. By conducting adversarial sample testing on the model and encrypting the data, the robustness of the model and the security of the data are improved, reducing the risk of being attacked.

[0121] In step S6, model interpretability analysis is performed using model interpretation tools such as LIME and SHAP.

[0122] In this way, the interpretability of the model is improved, helping developers and security analysts understand the decision-making process of the model, and increasing the credibility and transparency of the model.

[0123] In step S7, an online learning library such as River is used to continuously update the model to adapt to new vulnerabilities and code patterns. This ensures that the model can continuously learn and adapt to newly emerging vulnerability types and attack methods, maintaining the timeliness and effectiveness of the model.

[0124] Furthermore, step S1 is specifically as follows:

[0125] Step S11: First, a certain number of sample codes are selected from the dataset, and these codes should contain various types of software vulnerabilities.

[0126] Step S12: A specialized code mutation tool (such as the PIT Mutation Testing Framework) is used to perform mutation operations on the selected sample codes.

[0127] Step S13: Different code mutants are generated by modifying certain elements in the code (such as operators, conditional statements, return values, etc.).

[0128] Step S14: The mutants are run and their behavior is checked to ensure that they are functionally similar to the original code but structurally different.

[0129] Step S15: The verified mutants are merged into the original dataset to increase the diversity and complexity of the dataset.

[0130] Furthermore, step S2 specifically includes:

[0131] Step S21: Process the code text using NLP techniques (such as a lexical analyzer and a syntax analyzer) to extract information such as vocabulary and grammatical structures; analyze the code comments and documentation using an NLP library (such as NLTK or spaCy) to extract the semantic information of the code.

[0132] Step S22: Use a static analysis tool (such as SonarQube) to analyze the structure, complexity, possible vulnerabilities, code style, etc. of the code, and extract features such as function definitions, class structures, variable usage, etc. At the same time, extract code quality metrics, where the code quality metrics include the complexity of the code, the programming standards adhered to, and potential error patterns.

[0133] Step S23: Run the code and use a tool (such as Valgrind) to monitor the runtime behavior of the program and collect runtime data such as memory usage, execution path, response time, etc.

[0134] Step S24: Combine the results of NLP and static and dynamic analysis to form a comprehensive feature set.

[0135] Step S25: Select a machine learning model suitable for processing code analysis data, such as a convolutional neural network (CNN) to analyze the code structure, or a recurrent neural network (RNN) to process the sequential information in the code.

[0136] Step S26: Convert the extracted features into a format that the model can process, such as converting the code structure into a vector or matrix form.

[0137] Step S27: Use a deep learning framework (such as TensorFlow or PyTorch) to train the model.

[0138] Step S28: Provide a large amount of labeled training data to enable the model to learn patterns for identifying vulnerabilities from the semantic information of the code, the results of code structure and complexity analysis, runtime behavior data, and code quality metrics.

[0139] Step S28 is the subsequent stage of Steps S26 and S27.

[0140] In Step S26, the extracted features (including the results of NLP analysis, the outputs of static and dynamic analysis) are converted into a format that a deep learning model can process, such as vectors or matrices. Step S27 then uses a deep learning framework (such as TensorFlow or PyTorch) to train the model with these formatted features. In Step S28, by providing a large amount of labeled training data (including labels indicating the presence or absence of vulnerabilities), the deep learning model can learn to identify patterns of vulnerabilities from these features.

[0141] Step S29: Optimize the model, such as adjusting the number of layers and neurons in the neural network, to improve the accuracy and efficiency of the model. Test the model performance using the validation dataset to ensure its effectiveness on new data.

[0142] Further, step S3 specifically includes:

[0143] Step S31: Prepare a labeled dataset where each sample has a label (e.g., whether there is a vulnerability), and perform data cleaning and preprocessing, such as feature standardization and missing value handling.

[0144] Step S32: Select a supervised learning algorithm suitable for the task, such as Support Vector Machine (SVM), Random Forest, Logistic Regression, etc.

[0145] Step S33: Train the model using the selected algorithm on the training data. This usually involves adjusting the model parameters to minimize the prediction error. For example, for SVM, the goal is to find a hyperplane that can maximize the margin between classes.

[0146] Step S34: Evaluate the model performance on an independent test dataset using methods such as cross-validation.

[0147] Further, step S4 is as follows:

[0148] Step S41: Deploy the trained model as a service, which can be a local server or a cloud service. Ensure that this service can receive code data and return vulnerability detection results.

[0149] Step S42: Create a new process or task in CI / CD tools (such as Jenkins, Travis CI, CircleCI, etc.).

[0150] Step S43: Configure the process to be automatically triggered when code is committed to the version control system (such as Git). Add a step in the process to call the deployed model service to perform real-time scanning of the committed code.

[0151] Step S44: Configure the CI / CD tool to handle the return results of the model service. For example, if a vulnerability is detected, an issue report or warning can be automatically created.

[0152] Step S45: Set up a notification mechanism, such as notifying relevant developers when a vulnerability is detected, and provide an interface or system that enables developers to provide feedback on the detection results.

[0153] Step S46: Implement a feedback system that allows users to report the accuracy of vulnerability detection, including false positives and false negatives. Regularly analyze the collected user feedback to identify common false positive or false negative patterns.

[0154] Step S47: Adjust and optimize the model based on the data feedback from users. This may involve retraining the model or adjusting its parameters, redeploying the optimized model to the service, and updating the relevant configurations in the CI / CD process.

[0155] Step S48: Continuously monitor the model performance and user feedback to ensure that the model continuously adapts to new code patterns and vulnerabilities.

[0156] Furthermore, Step S5 includes:

[0157] Step S51: Use specialized tools (such as Adversarial Robustness Toolbox) to generate adversarial samples, which mislead machine learning models by making small, calculated adjustments to the original data, ensuring that these samples appear similar to the original samples to human observers but can deceive the model.

[0158] Step S52: Add these adversarial samples to the training set and retrain the model using the augmented training set so that it can identify and correctly handle these adversarial samples.

[0159] Step S53: Evaluate the performance of the model on a test set containing adversarial samples, and repeat the above steps as needed until the model can effectively identify adversarial samples.

[0160] Step S54: Select a suitable encryption algorithm, such as AES (Advanced Encryption Standard) or RSA, to protect the security of data and implement encryption during data storage and transmission. For example, encrypt sensitive data stored in the database and ensure the use of secure protocols such as HTTPS during data transmission.

[0161] Step S55: Manage encryption keys securely. Ensure that only authorized users and systems can access the keys, update the keys regularly, and implement strict data access control measures. Ensure that only authorized individuals and systems can access or process encrypted data.

[0162] Furthermore, Step S6 includes:

[0163] Step S61: Select a suitable explainable AI tool, such as LIME (Local Interpretable Model-agnostic Explanations) or SHAP (Shapley Additive exPlanations).

[0164] Step S62: Apply the selected tool to the trained machine learning model. For example, use LIME to analyze the decision-making process of the model for specific predictions.

[0165] Step S63: Generate an explanation report to show the main features considered by the model during prediction and their contribution degrees, and analyze these explanations to understand the decision logic of the model.

[0166] Step S64: Use visualization tools to present the explanation results to users, such as showing the importance of features through charts or graphs.

[0167] Step S65: Regularly generate a performance report of the model, including key metrics such as accuracy, recall rate, F1 score, etc., and include details of model training and testing in the report, such as the dataset used, model type, training parameters, etc.

[0168] Step S66: Present the performance report in a user-friendly way, such as making the information easy to understand through charts and graphs.

[0169] Step S67: Provide a mechanism for users to give feedback on the report and their feelings about the model performance. Regularly update the performance report according to user feedback and changes in model performance to keep the information up-to-date and relevant.

[0170] Furthermore, step S7 includes:

[0171] Step S71: Select machine learning algorithms that can handle streaming data, such as Online Stochastic Gradient Descent, online decision trees, etc.

[0172] Step S72: Design the system to continuously receive and process new data streams. This may include collecting data in real time from the production environment or obtaining data from regularly updated data sources. As new data arrives, continuously update the model parameters. This means that the model can continuously learn and adapt based on the latest data.

[0173] Step S73: Dynamically adjust the learning rate and other hyperparameters according to the performance of the model on new data to optimize performance, update and adjust to cope with new threats

[0174] Step S74: Regularly analyze the latest trends and threat intelligence in the security field to identify possible new vulnerabilities and attack patterns.

[0175] Step S75: Update the training dataset according to the results of trend analysis, including adding samples representing new threats.

[0176] Step S76: Retrain the model using the updated dataset to ensure that the model can identify and respond to new types of vulnerabilities. Continuously monitor the performance of the model in the actual environment, especially its ability to detect new threats. Establish a feedback mechanism to allow users to report the accuracy of vulnerability detection, and then use this feedback to continue optimizing the model.

[0177] A machine learning-based high-risk vulnerability instant recognition system, including:

[0178] A data processing and enhancement module, which is used to collect data from vulnerability databases and code repositories using the Python crawler library Scrapy; preprocess the collected code and vulnerability descriptions using the natural language processing library NLTK, and enhance the dataset using code mutation tools;

[0179] Automatically collect vulnerability data and source code and apply code mutation algorithms to increase data diversity;

[0180] A feature extraction and analysis module, which is used to perform static code analysis using SonarQube, then perform dynamic code analysis to build a machine learning model; use SonarQube to extract code quality metrics, collect runtime data through GDB, and finally train a machine learning model through TensorFlow to automatically learn code features;

[0181] Combine static and dynamic analysis tools to extract code features and use natural language processing technology to analyze code comments and documentation;

[0182] A model training and integration module, which is used to implement algorithms using Scikit-learn; train and test machine learning models in Scikit-learn, and then use model fusion technology to combine the predictions of machine learning models;

[0183] Adopt multiple supervised learning algorithms to train the model and combine the outputs of multiple models to improve the prediction accuracy;

[0184] A deployment and real-time monitoring module, which is used to integrate the trained machine learning model into CI / CD tools; perform real-time scanning and analysis when the code is submitted to the version control system;

[0185] Integrate the model into the CI / CD process for real-time code detection, monitor the model performance, and provide real-time feedback;

[0186] A security and privacy protection module, which is used to generate adversarial samples using the Adversarial Robustness Toolbox and perform data encryption using OpenSSL; use the Adversarial Robustness Toolbox to test and improve the robustness of machine learning models and encrypt sensitive data using OpenSSL;

[0187] Use adversarial samples to improve the model robustness and implement data encryption and access control policies;

[0188] Interpretation and visualization module, which is used to analyze the contributing factors of individual predictions using LIME and to interpret and visualize the global model performance using SHAP;

[0189] Continuous learning and adaptation module, which is used to update the machine learning model using an online learning library; regularly update the machine learning model parameters to deal with new types of vulnerabilities and code patterns; implement real-time model updates and online learning and adjust and update the model according to the latest trends.

[0190] As a specific embodiment, a large software development company faces continuous software security challenges and needs to quickly identify and fix vulnerabilities to protect customer data and avoid potential security risks.

[0191] The implementation steps are as follows:

[0192] 1. Data processing and enhancement

[0193] Use automated tools to collect historical vulnerability data and open-source code from public vulnerability databases and GitHub, ensuring the extensiveness and diversity of the dataset and providing rich learning materials for the model.

[0194] 2. Feature extraction and analysis

[0195] Use static code analysis tools to extract structural features, and at the same time analyze code comments and documentation through NLP to capture semantic information, combining the structural and semantic features of the code and improving the comprehensiveness of feature extraction.

[0196] 3. Model training and integration

[0197] Train multiple models, such as SVM and random forest, and then fuse these models through an ensemble learning method. Integrating different models improves the overall prediction accuracy and robustness.

[0198] 4. Deployment and real-time monitoring

[0199] Integrate the trained model into the company's CI / CD process to achieve real-time scanning and vulnerability detection of code, making vulnerability detection automated and instant, and greatly reducing the response time.

[0200] 5. Security and privacy protection

[0201] Regularly train the model using adversarial samples and at the same time implement strong data encryption measures. Enhance the model's defense ability against complex attacks and ensure data security.

[0202] 6. Continuous learning and adaptation

[0203] Implement the online learning ability of the model, regularly analyze new security trends and update the dataset. Maintain the timeliness and adaptability of the model to cope with evolving security threats.

[0204] Results and Evaluation:

[0205] The system successfully identified and reported several new vulnerabilities in real time, reducing the occurrence of security incidents. The development team reported that the model accurately identified most vulnerabilities, significantly improving development efficiency and software security. Regular performance reports and continuous learning mechanisms ensured the continuous improvement and update of the system.

[0206] This case demonstrates how to identify and address vulnerabilities in real time during the software development process through a comprehensive machine learning system. The modular design of the system allows each part to focus on its function while maintaining overall coordination and efficiency. By continuously learning and adapting to new threats, the system maintains high efficiency and accuracy, significantly enhancing software security.

[0207] As a specific implementation case, the company provides online financial services and has a large user database and complex transaction processing system. To prevent data leakage and security vulnerabilities, the company implemented a machine learning-based vulnerability detection system proposed in this solution.

[0208] Implementation Steps

[0209] 1. Data Processing and Enhancement

[0210] Use Python crawlers to collect vulnerability information from open-source vulnerability databases (such as CVE); generate mutated versions on the internal codebase through code mutation techniques (such as using the PIT tool). Create a comprehensive dataset containing real-world and synthetic vulnerabilities to provide rich training materials.

[0211] 2. Feature Extraction and Analysis

[0212] Use SonarQube for static analysis of the internal codebase to extract code quality metrics; use NLP techniques to analyze code comments and extract semantic information. Combine the static and semantic features of the code to improve the accuracy of vulnerability identification.

[0213] 3. Model Training and Integration

[0214] Use random forest and neural network algorithms to train multiple models on the processed data; adopt ensemble learning techniques (such as boosting) to integrate these models. Complement the advantages of different models to enhance the overall vulnerability detection ability.

[0215] 4. Deployment and Real-time Monitoring

[0216] Integrate the trained model into the Jenkins CI / CD process to automatically trigger vulnerability detection upon code submission and provide instant feedback. Achieve the automation and real-time nature of vulnerability detection, reducing the need for manual review.

[0217] 5. Security and Privacy Protection

[0218] Regularly use the Adversarial Robustness Toolbox to generate adversarial examples for testing and updating the model; implement AES encryption for all sensitive data. Protect the model from potential adversarial attacks and ensure data security.

[0219] 6. Continuous Learning and Adaptation

[0220] Implement an online learning strategy to enable the model to self-update based on the latest transaction data; regularly monitor external security dynamics to update the training dataset. Ensure that the model can adapt to newly emerging vulnerabilities and attack patterns.

[0221] Results and Evaluation

[0222] The system successfully identified 95% of potential vulnerabilities within the first three months, including several severe security vulnerabilities, preventing possible security incidents. Developers found that the system significantly reduced the burden of manual code review and improved development efficiency. Customers were satisfied with the security measures taken by the company, and the level of trust increased.

[0223] By implementing this system, the fintech company not only enhanced the security of its software products but also optimized the development process and strengthened customer trust. This case demonstrates how to effectively integrate multiple technical means to build a powerful and adaptable vulnerability detection system.

[0224] The above specific implementation manners only describe the preferred implementation manners of the present invention and do not limit the protection scope of the present invention. Without departing from the design concept and spirit scope of the present invention, various deformations, substitutions, and improvements made by those of ordinary skill in the art to the technical solutions of the present invention based on the written description and drawings provided by the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for instant identification of high-risk vulnerabilities based on machine learning, characterized in that, Including: Step S1: Use the Python crawler library Scrapy to collect code data from vulnerability databases and code repositories; Preprocess the collected code data using the natural language processing library NLTK, and enhance the dataset using a code mutation tool; Step S2: Apply SonarQube for static code analysis, then perform dynamic code analysis to build a machine learning model; Use SonarQube to extract code quality metrics, and collect runtime data through GDB for training the machine learning model; Step S3: Use Scikit-learn for algorithm implementation; Train and test the machine learning model in Scikit-learn, and then use model fusion techniques to combine the predictions of the machine learning model; Step S4: Integrate the trained and fused machine learning model into the CI / CD tool; When the code is submitted to the version control system, the machine learning model automatically performs real-time scanning and analysis; Step S6: Use the Adversarial Robustness Toolbox to generate adversarial samples, thereby enhancing the security of the machine learning model and improving the robustness of the machine learning model under adversarial attacks; Step S7: Use LIME to analyze the contributing factors of individual predictions, and use SHAP for the interpretation and visualization of the global model performance; Step S8: Use an online learning library to update the machine learning model; Regularly update the machine learning model parameters to cope with new types of vulnerabilities and code patterns.

2. The instant identification method for high-risk vulnerabilities based on machine learning according to claim 1, wherein, Step S1 is specifically as follows: Step S11: First, select sample code from the dataset. The sample code contains various types of software vulnerabilities; Step S12: Use a code mutation tool to perform mutation operations on the selected sample code; Step S13: Generate different code mutants by modifying elements in the code; Step S14: Run the mutants and check whether their behaviors meet the expectations, ensuring that the mutants are functionally similar to the original code but different in structure; Step S15: Merge the verified mutants into the original dataset to increase the diversity and complexity of the dataset.

3. The instant high-risk vulnerability identification method based on machine learning according to claim 1, wherein Step S2 specifically includes: Step S21: Use NLP techniques to process the code text and extract information; Use the NLP library to analyze code comments and documentation to extract the semantic information of the code; Step S22: Use static analysis tools to analyze the structure, complexity, possible vulnerabilities, and code style of the code, and extract features; Among them, the features include function definitions, class structures, and variable usage; At the same time, extract code quality metrics, where the code quality metrics include the complexity of the code, the programming standards adhered to, and potential error patterns; Step S23: Run the code and use tools to monitor the runtime behavior of the program and collect runtime data; Step S24: Merge the results of NLP and static and dynamic analysis to form a comprehensive feature set; Step S25: Select a machine learning model for processing code analysis data to analyze the code structure; Step S26: Convert the extracted features into a format that the machine learning model can handle; Step S27: Train the machine learning model using a deep learning framework; Step S28: Provide a large number of labeled training data to enable the machine learning model to learn patterns for identifying vulnerabilities from semantic information of the code, results of code structure and complexity analysis, runtime behavior data, and code quality metrics; Step S29: Optimize the machine learning model to improve its accuracy and efficiency, and test the performance of the machine learning model using a validation dataset to ensure its effectiveness on new data.

4. The instant high-risk vulnerability identification method based on machine learning according to claim 1, wherein Step S3 specifically includes: Step S31: Prepare a labeled dataset where each sample has a label, and perform data cleaning and preprocessing; Step S32: Select a supervised learning algorithm; Step S33: Use the selected supervised learning algorithm to train the machine learning model on the training data; Step S34: Evaluate the performance of the machine learning model on an independent test dataset using a cross-validation method.

5. The method for instant identification of high-risk vulnerabilities based on machine learning according to claim 1, wherein Step S4 is as follows: Step S41: Deploy the trained machine learning model as a service, ensuring that this service can receive code data and return vulnerability detection results; Step S42: Create a new process or task in the CI / CD tool; Step S43: Make it trigger automatically when the code is submitted to the version control system, and add a step to call the deployed model service in the process to perform real-time scanning of the submitted code; Step S44: Configure the CI / CD tool to handle the return results of the model service; Step S45: Set up a notification mechanism to provide an interface or system that enables developers to provide feedback on the detection results; Step S46: The system allows users to report the accuracy of vulnerability detection, including false positives and false negatives, regularly analyze the collected user feedback, and identify common false positive or false negative patterns; Step S47: Adjust and optimize the machine learning model based on the user feedback data, redeploy the optimized machine learning model to the service, and update the relevant configurations in the CI / CD process; Step S48: Continuously monitor the performance of the machine learning model and user feedback to ensure that the machine learning model continuously adapts to new code patterns and vulnerabilities.

6. The instant high-risk vulnerability identification method based on machine learning according to claim 1, wherein Step S5 includes: Step S51: Use a specialized tool to generate adversarial samples. The adversarial samples mislead the machine learning model by adjusting the original data, ensuring that these samples appear similar to the original samples to human observers but can deceive the machine learning model; Step S52: Add these adversarial samples to the training set and retrain the machine learning model using the enhanced training set so that it can identify and correctly handle these adversarial samples; Step S53: Evaluate the performance of the machine learning model on a test set containing adversarial samples, and repeat the above steps as needed until the machine learning model can effectively identify adversarial samples; Step S54: Select an encryption algorithm to protect data security, implement encryption during data storage and transmission, and ensure the use of a secure protocol during data transmission; Step S55: Securely manage the encryption keys to ensure that only authorized users and systems can access the keys, and regularly update the keys, implementing strict data access control measures.

7. The method for instant identification of high-risk vulnerabilities based on machine learning according to claim 1, wherein Step S6 includes: Step S61: Select an explainable AI tool; Step S62: Apply the selected explainable AI tool to the trained machine learning model; Step S63: Generate an explanation report to show the features considered by the machine learning model during prediction and their contribution degrees, and analyze these explanations to understand the decision logic of the machine learning model; Step S64: Use a visualization tool to display the explanation results to the user; Step S65: Regularly generate a performance report of the machine learning model, including key metrics such as accuracy, recall, and F1-score, and include details of the training and testing of the machine learning model in the report; Step S66: Display the performance report in a user-friendly manner; Step S67: Provide a mechanism for users to give feedback on the report and their feelings about the performance of the machine learning model, and regularly update the performance report according to the user feedback and changes in the performance of the machine learning model to keep the information up-to-date and relevant.

8. The method for instantaneously identifying high-risk vulnerabilities based on machine learning according to claim 1, characterized in that Step S7 includes: Step S71: Select a machine learning algorithm for processing streaming data; Step S72: Design a system to continuously receive and process new data streams, and continuously update the parameters of the machine learning model as new data arrives; Step S73: Dynamically adjust the learning rate and other hyperparameters according to the performance of the machine learning model on new data to optimize performance, and update and adjust to cope with new threats; Step S74: Regularly analyze the latest trends and threat intelligence in the security field to identify possible new vulnerabilities and attack patterns; Step S75: Update the training dataset according to the results of the trend analysis, including adding samples representing new threats; Step S76: Retrain the machine learning model using the updated dataset to ensure that the machine learning model can identify and cope with new types of vulnerabilities, continuously monitor the performance of the machine learning model in the actual environment, including the detection ability of new threats, allow users to report the accuracy of vulnerability detection, and then continue to optimize the machine learning model.

9. An instant identification system for high-risk vulnerabilities based on machine learning, characterized in that, Includes: A data processing and enhancement module for collecting code data from a vulnerability database and a code repository using the Python crawler library Scrapy; Preprocess the collected code data using the natural language processing library NLTK and enhance the dataset using a code mutation tool; A feature extraction and analysis module for performing static code analysis using SonarQube, then performing dynamic code analysis to build a machine learning model; using SonarQube to extract code quality metrics and collecting runtime data through GDB for training the machine learning model; A model training and integration module for implementing algorithms using Scikit-learn; training and testing the machine learning model in Scikit-learn, and then combining the predictions of the machine learning model using model fusion techniques; A deployment and real-time monitoring module for integrating the trained and fused machine learning model into a CI / CD tool; when code is submitted to the version control system, the machine learning model automatically performs real-time scanning and analysis; Security and Privacy Protection Module, which is used to generate adversarial examples using the Adversarial Robustness Toolbox, thereby enhancing the security of machine learning models and improving the robustness of machine learning models under adversarial attacks; Explanation and Visualization Module, which is used to analyze the contributing factors of individual predictions using LIME and perform the explanation and visualization of the global model performance using SHAP; Continuous Learning and Adaptation Module, which is used to update the machine learning model using an online learning library; regularly update the parameters of the machine learning model to cope with new types of vulnerabilities and code patterns.

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