20 results about "Vulnerability prediction" patented technology

The invention discloses a big data vulnerability monitoring and data maintenance method and system based on cloud computing. The method comprises the following steps: S1, constructing a spatio-temporal joint feature set; s2, constructing a dynamic trust graph based on the spatio-temporal joint feature set; s3, performing iterative updating on the dynamic trust graph by using an improved trust propagation algorithm; s4, predicting a multi-step path and diffusion possibility of the vulnerability diffused from the source node to other nodes by adopting a self-evolution diffusion Transform model; s5, a multi-head time sequence perception attention mechanism is introduced into the self-evolution diffusion Transform model, diffusion parameters are adjusted, and a vulnerability diffusion trend chart is generated; s6, performing reverse correction on the dynamic trust graph according to the vulnerability diffusion trend graph; s7, constructing a cloud native sandbox environment, and performing vulnerability verification; and S8, generating an intelligent repair plan in combination with the historical patch knowledge base. According to the method, an improved trust propagation algorithm and a self-evolution diffusion Transform model are fused, and a cloud environment vulnerability prediction verification closed loop is realized.

The invention belongs to the technical field of network security, and particularly relates to a network security penetration test platform which comprises a data acquisition layer, a data analysis layer, a vulnerability utilization layer, a report generation layer and a user interaction layer. And comprehensive penetration testing of network security in a supply chain attack scene is realized. Potential vulnerabilities of all links of the supply chain can be accurately mined, real attack evaluation vulnerability is simulated, a targeted security protection scheme is provided for enterprises, the security protection capability of the enterprise supply chain network is effectively improved, and the problems that an existing penetration test platform is insufficient in supply chain attack coverage, low in vulnerability mining efficiency and the like are solved.

The invention discloses an actuator vulnerability analysis method for a non-unique identifiable information physical system, and the method comprises the steps: combing an attack scene and parameters, constructing a vulnerability analysis target model set based on the information of attackers and defenders, extracting the determined line components of all possible system model parameters through matrix analysis and operation, and carrying out the vulnerability analysis. And constructing a criterion matrix of a plurality of sub-problems. And based on the property of the criterion matrix, determining the minimum number of the attack executors, and obtaining an optimal attack executor set of the sub-problem by taking whether the criterion matrix is in a full rank or not as a judgment standard through combined traversal. And generating an intersection of the optimal solutions of all the sub-problems as a global optimal solution. And constructing a simulation system to verify the defense effectiveness, and effectively attacking all states of the system. Attack and defense cognition depth alignment is achieved, and the problem that fragile point pre-judgment is not accurate is solved.

This application provides a vulnerability prediction method, apparatus, electronic device, and storage medium, relating to the field of network security technology, and capable of solving the problem of low security and stability in data centers. The method includes: acquiring log data from the data center, the log data carrying event content and the event level corresponding to the event content; determining vulnerability log data based on a vulnerability database and the log data, the event level corresponding to the vulnerability event content in the vulnerability log data being the vulnerability level; inputting the vulnerability event content from the vulnerability log data into a vulnerability prediction model, outputting first predicted event content, the first predicted event content having a correlation with the vulnerability event content, the event level corresponding to the first predicted event content being the vulnerability level; and the vulnerability prediction model being trained based on the vulnerability event content and the predicted event content. This application improves the efficiency and accuracy of vulnerability detection in data centers.

Aspects related to predicting zero-day vulnerabilities using anomaly detection and neural network algorithms are provided. A prediction platform may train an unsupervised algorithm for identifying suspicious packets and a prediction model for generating suspicion scores and behavior patterns based on network traffic information. The platform may segment information of packets of network traffic information into a plurality of segments. The platform may compare the segments with zero-day vulnerability information to identify known zero-day vulnerabilities. The platform may use the unsupervised algorithm to identify suspicious packets that do not correspond to known zero-day vulnerabilities. The platform may generate suspicion scores and behavior patterns for suspicious packets. The platform may further train the prediction model based on behavior patterns associated with certain suspicion scores to generate vulnerability scores. The platform may generate vulnerability scores for suspicious packets using the model. The platform may output zero-day vulnerability predictions based on the vulnerability scores.

The invention relates to a vulnerability repairing method and device and computer equipment. The method comprises the following steps: monitoring a target system; under the condition that the current version of the target system is monitored to have vulnerabilities, pulling a repair program of the target system from a software repository; and performing vulnerability repair on the target system by adopting the repair program to obtain a repair version of the target system. Wherein the software repository comprises a plurality of vulnerability repair programs, and the vulnerability repair programs are obtained according to vulnerability prediction results of historical versions of the target system by the vulnerability prediction model; the vulnerability prediction model is obtained based on a historical version repair record of the target system. By adopting the method, the system bug repairing efficiency can be improved.

The application relates to an intelligent contract vulnerability detection system based on graph convolution and global attention, which comprises a contract graph construction module, a relationship-aware graph convolution local feature extraction module, a Transformer global dependence extraction module and an intelligent contract vulnerability classification and prediction module. The contract graph construction module constructs a heterogeneous contract graph containing control flow relationships, data flow relationships and rollback interaction relationships; the relationship-aware graph convolution local feature extraction module extracts node local structure features through multi-relationship graph convolution; the Transformer global dependence extraction module captures cross-function, cross-path and long-distance implicit dependence; and the intelligent contract vulnerability classification and prediction module performs classification mapping and graph-level aggregation on the final representation of the node, and outputs contract vulnerability prediction probability and classification results. The application effectively solves the problems that mixed propagation of heterogeneous semantics is easy to cause feature interference, it is difficult to depict complex program relationships, and long-distance dependence modeling capability is insufficient.

The invention provides a network security spatial data vulnerability monitoring method based on a neural network algorithm, and relates to the technical field of network security. Inputting the to-be-tested vulnerability data into a constructed final vulnerability prediction model to obtain a vulnerability classification result and a vulnerability prediction result; and according to the vulnerability classification result and the vulnerability prediction result, monitoring the life vulnerability of the data asset life cycle in the network space. According to the invention, the problem of easy missing detection or false detection of network security monitoring in the prior art is solved.

The application discloses a bridge earthquake vulnerability prediction method and system based on machine learning, comprising: obtaining a set of earthquake parameters, constructing a nonlinear mapping model using a deep neural network, predicting vulnerability probability based on the nonlinear mapping model to obtain preliminary vulnerability probability data; obtaining bridge structure data to construct a bridge geometric model, analyzing the segmented material heterogeneity of the bridge geometric model, and if the heterogeneity exceeds a preset threshold, calculating segmented response data under seismic action; based on the segmented response data, analyzing the spatial difference vulnerability probability distribution of the bridge as a whole to obtain fine prediction results; if the deviation between the fine prediction results and the preliminary vulnerability probability exceeds a preset threshold, adjusting the nonlinear mapping model, outputting the updated vulnerability probability distribution, and obtaining the final vulnerability prediction results. The application significantly improves the accuracy and reliability of bridge earthquake vulnerability prediction, and has important significance for improving the seismic safety of bridge structures.

The invention relates to a cloud host vulnerability scanning method and device, computer equipment, a readable storage medium and a program product. The method comprises the following steps: in response to a vulnerability scanning instruction, obtaining software bill of material data of a target cloud host, and storing the software bill of material data in a graph structure; performing feature extraction on the software bill of material data to obtain multi-dimensional feature data; inputting the multi-dimensional feature data into a pre-trained graph neural network model to obtain a vulnerability prediction result, the training data of the graph neural network model including historical multi-dimensional feature data marked with a vulnerability type tag and an abnormal degree tag, and the prediction result including at least one of a vulnerability type, a vulnerability number and a vulnerability confidence; and based on the prediction result, generating and outputting a vulnerability scanning report of the target cloud host. By adopting the method, the comprehensiveness, accuracy and efficiency of vulnerability scanning can be improved.

The invention discloses an intelligent contract vulnerability detection method based on a large language model and deep learning. The method comprises the following steps: 1, obtaining and preprocessing an intelligent contract vulnerability data set; step 2, performing core code segment extraction by using ChatGPT, and performing feature extraction on the core code segment through an improved CodeBert model; 3, inputting the risk annotation data set into an intelligent contract vulnerability detection training network for training; 4, inputting the classification feature vectors into a vulnerability prediction model for vulnerability prediction; 5, evaluating the vulnerability prediction model by using the test data set; 6, performing vulnerability risk assessment according to a vulnerability prediction result; and 7, according to a vulnerability risk assessment result, providing a security repair suggestion for the smart contract and verifying a repair effect. According to the method, the big language model and the improved CodeBert model are combined, and automatic detection, evaluation and repair of the intelligent contract vulnerability are realized.

The present invention provides a cross-project vulnerability detection model based on domain adaptation. It learns project code features through a deep neural network and adopts a domain adaptation method to reduce the data distribution difference between the source project and the target project. The steps include: selecting the source project according to the software code metric similarity analysis; parsing the project source code to obtain the abstract syntax tree and performing code preprocessing; building a deep learning model for model pretraining; learning the deep feature representation of the source project and the target project based on a multi-domain dataset; using the semi-supervised metric transfer learning framework (Semi-Supervised Metric Transfer Learning, SSMTL) to perform domain adaptation processing on the deep features of the source project and the target project; training a classifier based on labeled data and performing vulnerability prediction on the data in the target project. This solution is applicable to source projects with sufficient labeled vulnerability data. By utilizing the vulnerability detection model, the vulnerability detection efficiency of the target project is improved, and labor costs are reduced.

This invention provides a method, apparatus, device, medium, and program product for predicting base station vulnerabilities, relating to the field of computer technology. The method includes: preprocessing base station alarm data and performance data using base station resource data; inputting the preprocessed alarm data into a first time-series prediction model to output a first probability that the base station has vulnerabilities; inputting the preprocessed performance data into a second time-series prediction model to output a second probability that the base station has vulnerabilities; and determining the base station vulnerability information based on the first and second probabilities. This invention introduces intelligent AI model capabilities, multi-dimensionally selecting alarm data strongly correlated with base station vulnerabilities and base station performance data aggregated from a service usage perspective that directly characterizes user experience to form a vulnerability mining data system. The combination of these two systems generates a more accurate and automated base station vulnerability mining capability, thereby improving the accuracy and efficiency of base station vulnerability prediction.

The embodiment of the application provides a vulnerability risk prediction method, device, equipment and medium, to solve the problem of low vulnerability prediction efficiency and inability to accurately determine the risk level of the vulnerability in the prior art. In the method, the to-be-detected SBOM is matched with a vulnerability database to determine a target vulnerability existing in the to-be-detected SBOM and a target component associated with the target vulnerability; a first score corresponding to the severity of the target vulnerability is determined; a second score corresponding to a target dependency relationship between the project and the target component is determined; a third score corresponding to a target reference frequency of the project referencing the target component is determined; the risk score of the target vulnerability is determined according to the first score, the second score and the third score; and whether the target vulnerability is processed preferentially is determined based on the risk score of the target vulnerability, thereby improving the vulnerability risk prediction efficiency, multi-dimensionally evaluating the vulnerability risk, and more accurately determining the vulnerability risk level.

The application provides a vulnerability detection model training method, a script vulnerability detection method and related devices, and relates to the field of machine learning. In the method, an electronic device obtains vulnerability prediction loss of a sample script set according to vulnerability prediction information of the sample script set by a current first to-be-trained model; obtains a mutation degree of the sample script set, and updates the current first to-be-trained model according to the mutation degree and the vulnerability prediction loss, wherein the mutation degree represents the complexity of the sample script set. The above steps are iterated until an iteration stop condition of the vulnerability detection model is met. In this way, in the training process of the first to-be-trained model, the parameters of the network are automatically adjusted according to the complexity of the sample script set, so that the trained vulnerability detection model has good generalization ability and can adapt to the dynamic changes and complexity of scripts.

The invention discloses a bridge earthquake vulnerability prediction method and system based on machine learning, and the method comprises the steps: obtaining an earthquake parameter set, employing a deep neural network to construct a nonlinear mapping model, carrying out the vulnerability probability prediction based on the nonlinear mapping model, and obtaining initial vulnerability probability data; the method comprises the steps of obtaining bridge structure data to construct a bridge geometric model, analyzing segmented material heterogeneity of the bridge geometric model, and if the heterogeneity exceeds a preset threshold value, calculating segmented response data under the earthquake action; based on the segmented response data, analyzing spatial difference vulnerability probability distribution of the whole bridge, and obtaining a fine prediction result; and if the deviation between the fine prediction result and the initial vulnerability probability exceeds a preset threshold, adjusting the nonlinear mapping model, and outputting the updated vulnerability probability distribution to obtain a final vulnerability prediction result. According to the method, the accuracy and reliability of bridge earthquake vulnerability prediction are remarkably improved, and the method has important significance in improving the earthquake safety of a bridge structure.

The invention provides a method and equipment for automatically predicting and repairing software vulnerabilities. The method comprises the following steps: performing data preprocessing on software system multi-source data corresponding to a target software system to obtain target feature data; inputting the target feature data into a software vulnerability prediction model to output vulnerability prediction result data; if the target software system has the target vulnerability, querying a vulnerability causal path from the vulnerability causal knowledge graph, and positioning a target root cause node of the target vulnerability; and according to the target root cause node of the target vulnerability, obtaining repair code data corresponding to the target vulnerability based on the vulnerability repair template or a large language model used for predicting repair codes. The method can be used for solving the problems that an existing method is passive in detection mode, insufficient in diagnosis depth and low in repair automation degree.

This invention discloses a vulnerability exploitation reasoning method and system based on knowledge graph embedding, belonging to the field of network security technology. The method includes: extracting triples from a network security knowledge graph and constructing triple feature vectors based on entity semantic vectors and triple structural features; selecting from triples based on the triple feature vectors to obtain anchor triples for each relation type; randomly selecting a head entity or tail entity from the anchor triples as an anchor entity, and obtaining the entity representation vector of the entity based on the relation type associated with the entity in the network security knowledge graph and the anchor entity; learning relation vectors in the network security knowledge graph by combining the entity representation vectors to generate a vulnerability exploitation reasoning model; and performing vulnerability prediction or attack pattern prediction tasks based on the vulnerability exploitation reasoning model. This invention enables efficient and automated reasoning of the vulnerability types and potential attack patterns of newly released vulnerabilities.

The present invention discloses a Docker image scanning method based on static analysis. The method extracts features from a large number of Docker image construction history records through a manual analysis method and establishes a security feature library. Feature matching is performed on the image to be tested based on the security feature library, and security issues of the image are detected based on the feature information and a whitelist mechanism. At the same time, the number of software vulnerabilities in the image is detected through an image scanning tool, and a function is fitted according to a scatter plot of time distribution to establish a vulnerability prediction model. Finally, the security issues of the Docker image are comprehensively analyzed based on the information of the two, and the image scanning results are fed back. The method combines the image scanning tool and the custom scanning method to analyze the image software vulnerabilities. The image scanning tool analyzes the image software vulnerabilities, and a function is fitted according to the scatter plot of the vulnerability number distribution to establish an image vulnerability prediction model.

This invention relates to the field of power grid node identification technology, and provides a method, device, system, and medium for identifying vulnerable nodes in a power grid. The implementation scheme is as follows: Based on the node hidden feature vectors of each node, branch hidden feature vectors between each node are constructed; based on the node hidden feature vectors, branch hidden feature vectors, and the true vulnerability labels of nodes and branches, a node-branch regression model is trained to obtain the trained node-branch regression model; the feature vectors of each node to be identified are used as input to the trained node-branch regression model to obtain the node vulnerability prediction value of each node to be identified and the branch vulnerability prediction value of the branches between each node to be identified; based on the node vulnerability prediction values ​​and the branch vulnerability prediction values, each node to be identified is identified to obtain the critical vulnerable nodes of the power grid. This invention can improve the accuracy of identifying critical vulnerable nodes affecting system security.