20 results about "Vulnerability evaluation" patented technology

Systems and methods provide agentless security assessment for workloads and cloud posture control across multi-cloud environments. Discovery modules are configured with collection intervals to ingest posture control data including assets, identities, configurations, activities, network flows, and build-time artifacts. A multi-cloud configuration inventory maintains current and historical states and produces misconfiguration and identity-activity findings. For workload vulnerability evaluation, an external snapshot manager obtains point-in-time root-disk state without installing an in-workload agent. A file system data processor derives operating system and package metadata, and a detector matches the metadata against a vulnerability feed refreshed on a recurring basis to identify vulnerabilities. Identified vulnerabilities are correlated with misconfiguration and activity findings to generate prioritized risk exposures. Results are stored per workload and presented through graphical user interfaces that display risk levels, timelines, alerts, and guided remediation, enabling continuous, low-overhead security coverage for cloud workloads and configurations.

The application provides a vulnerability evaluation method based on multi-model rejection sampling and reinforcement learning alignment, and belongs to the technical field of computers, and solves the technical problem of poor vulnerability evaluation effect.The technical scheme comprises the following steps: S1: constructing a vulnerability data set, preprocessing source code, extracting vulnerability description, source code and corresponding labels; S2: through a multi-model rejection sampling strategy; S3: supervised fine-tuning training, optimizing the understanding ability of the model to vulnerability characteristics; S4: based on a group relative strategy optimization method, improving the reliability and consistency of the model output; S5: inputting the double-mode information of the target vulnerability into the trained model to obtain the final result.The application constructs a high-quality data set through multi-model rejection sampling, optimizes the model output in combination with a reinforcement learning alignment mechanism, and significantly improves the accuracy of vulnerability evaluation.

The present application relates to the technical field of power grid dispatching, and specifically provides a power distribution network vulnerability assessment and collaborative dispatching method, device and medium, the method comprising: based on the historical data and prediction error distribution of new energy output in the power distribution network, a Monte Carlo simulation is used to sample and generate multiple scenes with occurrence probability; according to the multiple scenes, a two-stage stochastic programming model is constructed with the comprehensive minimization of power distribution network operation cost and conditional risk value as the target; wherein the two-stage stochastic programming model includes a day-ahead pre-decision stage, a real-time balancing stage and a risk measurement item; the two-stage stochastic programming model is solved to obtain the vulnerability assessment result and collaborative dispatching scheme of the power distribution network within the target time. The present application method significantly improves the economy and safety of the power distribution network operation under the high proportion of new energy access scene through deep integration of uncertainty modeling and risk quantification technology.

This application belongs to the fields of software security, program analysis, and big data technology. Specifically, it discloses a method, apparatus, and device for constructing a multi-granularity vulnerability assessment dataset. The method includes: for multiple data sources, crawling raw data and performing data cleaning, deduplication, and cross-data source entity linking to obtain a unified vulnerability entity; based on the unified vulnerability entity, using abstract syntax tree analysis and in-process data dependency and control dependency analysis to obtain code slices and patch pair samples of different granularities; performing multi-dimensional annotation of metadata information to obtain annotated samples with vulnerability information, context integrity level, and compilation information; based on the annotated samples, obtaining a positive sample set and a negative sample set through positive sample filtering and negative sample construction; and performing data fusion, dynamic filtering, and structured encapsulation to construct a target vulnerability assessment dataset. This application enables the effective construction of vulnerability assessment datasets.

The application provides a program instruction vulnerability evaluation method based on bit-level cloning, comprising the following steps: loading a target program to be evaluated as an input into an evaluation system; the evaluation system analyzes dynamic instruction groups of the program, determines clonable bit positions of each dynamic instruction group, including clonable SDC bit positions and clonable non-SDC bit positions; performing fine-grained bit-level cloning and fault injection on the clonable bit positions and other bit positions respectively to determine SDC vulnerability of each dynamic instruction. The application has the beneficial effects that: through the combination of bit-level cloning and fault injection, repeated operations are reduced, cost is lowered, bit-level accurate analysis is realized, differentiated evaluation of complex programs is supported, and quantitative basis is provided for high-reliability software design.

The application discloses a fault-tolerant quantum architecture design method, device, storage medium and electronic equipment, wherein the fault-tolerant quantum architecture design method comprises the following steps: obtaining hardware configuration parameters of a target quantum computing architecture, and constructing a fault tree model based on the hardware configuration parameters; performing vulnerability evaluation on the fault tree model, generating a vulnerability quantification index, and determining a candidate component set requiring deployment of a resilience mechanism according to the vulnerability quantification index; configuring a corresponding resilience strategy for each component in the candidate component set, and generating a graceful degradation planning table based on the resilience strategy; inputting the fault tree model, the resilience strategy and the graceful degradation planning table into a formal verification engine to output a reliability certificate; and generating an authenticated resilience architecture netlist through a hardware description language according to the reliability certificate. The application can solve the technical problems that the quantum computing system lacks systematic fault-tolerant design, the fault response capability is insufficient, and the reliability index cannot be verified in the prior art.

The application discloses a power grid fault range fast identification method based on MRF and vulnerability evaluation, and relates to the technical field of power grid operation and fault diagnosis. The application comprises topological modeling of a power grid to form a power grid topology graph, wherein the topological modeling comprises abstracting primary equipment of the power grid into a node set and abstracting electrical connection parameters of the power grid into an edge set. The application introduces a multi-dimensional vulnerability evaluation system, combines node structure vulnerability and node state vulnerability, and comprehensively evaluates the vulnerability of the power grid. Through in-depth analysis of the power grid topology, the change of electrical quantity is considered, and the structural and state characteristics of the nodes and branches of the power grid are fully combined, so that the accurate evaluation of the overall vulnerability of the power grid is ensured. The in-depth analysis of the power grid topology makes the fault identification not only limited to the change of electrical quantity, but also fully reflects the dynamic state of the power grid under various complex situations, thereby improving the accuracy and reliability of the power grid fault diagnosis.

The application provides a vulnerability evaluation method for a multi-energy system in a region, comprising: acquiring topological structure data, measurement data and historical data of a multi-energy system in a region to be evaluated; performing simulation scene simulation and power flow calculation on the multi-energy system in the region to be evaluated according to the topological structure data, the measurement data and the historical data, to obtain a plurality of groups of vulnerability evaluation indexes; composing an evaluation index matrix according to a plurality of groups of vulnerability evaluation indexes in a preset format; calculating a correlation coefficient of each index and an optimal index according to the evaluation index matrix, and calculating a comprehensive evaluation result according to the correlation coefficient; and outputting the comprehensive evaluation result as a final vulnerability evaluation result of the multi-energy system in the region. The application can accurately calculate vulnerability indexes of the multi-energy system in the region under various operation scenes and configuration schemes, and perform vulnerability sorting on different configuration schemes, thereby improving the safety and stability of the system.

This invention provides a vulnerability assessment method for large-scale critical information infrastructure, comprising: collecting interaction event information of different large-scale critical information infrastructures; generating a temporal dynamic hypergraph based on the interaction event information; constructing a temporal hypergraph encoder, and training the temporal hypergraph encoder by combining a temporal dynamic hypergraph, a counterfactual hypergraph, and a self-supervised learning strategy; determining soft labels for node vulnerability based on dynamic propagation simulation and topological robustness analysis algorithms, wherein different soft labels represent different vulnerabilities; constructing a target inference model by combining the dynamic embedding representation of nodes output by the temporal hypergraph encoder with the soft labels, wherein the target inference model is used to determine the soft labels of each node in the temporal dynamic hypergraph, and generating a vulnerability ranking of multiple nodes based on the soft labels; and calling the target inference model to process the interaction event information of the infrastructure to be assessed to obtain the node vulnerability ranking of the corresponding target inference model.

The present application relates to the technical field of image processing, and particularly relates to a coronary CTA image analysis system for plaque vulnerability evaluation, which comprises a frame construction unit for constructing a center line guide frame corresponding to the center point of each image layer. A feature extraction and screening unit is used for extracting a discrete pipe wall feature set including the coordinates of each feature node in each image layer, the density attenuation coefficient and the high-density barrier identifier, and screening the candidate feature nodes from the discrete pipe wall feature set. A space correction unit is used for mapping the candidate feature nodes in the previous image layer to the current image layer to obtain the curvature correction projection coordinates. A connection optimization unit is used for determining the optimal matching path of the candidate feature nodes between adjacent image layers. An entity reconstruction unit is used for reconstructing the target plaque entity according to the optimal matching path. A vulnerability evaluation unit is used for generating the vulnerability evaluation result of the target plaque entity. The present application realizes the vulnerability evaluation of the plaque structure in the coronary CTA image.

The application discloses a building vulnerability assessment method and system based on AI and GIS, a seismic risk analysis model is constructed by acquiring preset building seismic risk data and geographic space data set of a research area, a building damage index and a building vulnerability assessment index are acquired, and a vulnerability assessment model is constructed, a GeoIME ecosystem is constructed based on the seismic risk analysis model, the vulnerability assessment model and an LLM model, a GeoIME GPT system is constructed, vulnerability and risk assessment of building data of the research area are performed according to the GeoIME GPT system, and evaluation results are obtained, potential vulnerable buildings are identified by AI, global disaster resistance capability is improved, the GeoIME GPT system improves disaster management efficiency and realizes risk assessment automation, and a transparent reasoning path of the GeoIME-GPT enables users to trace back each analysis step, and uncertainty is converted from implicit risk into controllable variables.

This invention discloses a method and apparatus for earthquake disaster risk assessment. The method includes: acquiring historical comprehensive earthquake intensity, distribution information of active seismic fault zones, and peak ground acceleration (PGA) of a target area; calculating earthquake occurrence parameters of the target area based on the historical comprehensive earthquake intensity, the distribution information of active seismic fault zones, and the PGA; acquiring historical disaster case data of the target area; calculating the seismic vulnerability of disaster-bearing bodies based on the historical disaster case data of the target area using a preset disaster-bearing body vulnerability assessment model; and conducting an earthquake disaster risk assessment of the target area based on the earthquake occurrence parameters and the seismic vulnerability of disaster-bearing bodies using a preset seismic risk assessment model. This method fully considers the possibility of earthquake occurrence and the severity of disaster losses, providing a scientific basis for formulating effective disaster prevention and mitigation measures, thereby significantly reducing potential losses.

The present disclosure belongs to the technical field of nuclear power and specifically relates to an attack path prediction method and device based on asset analysis and graph convolutional neural network. The present disclosure realizes rapid positioning and prediction of attack paths and improves the foresight of network security defense by modeling asset vulnerability, constructing an attack graph and utilizing a graph convolutional neural network for reasoning. Through vectorization modeling of asset vulnerability, the present disclosure converts traditional discrete vulnerability evaluation into continuous feature representation, realizing accurate quantification from coarse-grained CVSS score to multi-dimensional vulnerability features. The graph convolutional neural network is introduced for attack path prediction, which can effectively capture high-order adjacency relationships and nonlinear dependence features between assets.

The application relates to the technical field of financial risk early warning, and specifically discloses an intelligent financial risk early warning method and system based on management decision, which performs fund chain vulnerability evaluation and analysis on enterprise financial operation data to obtain fund chain vulnerability evaluation data; performs third-party agency credit evaluation data introduction processing based on the fund chain vulnerability evaluation data to obtain third-party credit evaluation data; performs enterprise fund chain vulnerability dynamic simulation according to the third-party credit evaluation data to obtain fund chain vulnerability evolution track data; performs fund chain fracture risk conduction effect quantification based on the fund chain vulnerability evolution track data to obtain a fund chain fracture risk conduction effect index; and performs financial risk precursor factor identification according to the fund chain fracture risk conduction effect index to obtain financial risk precursor factor learning data. The application not only significantly improves the accuracy and real-time performance of fund chain vulnerability identification, but also provides necessary technical support for constructing an intelligent enterprise financial risk control system.

The application discloses a power network node vulnerability evaluation method based on a BP neural network, and comprises the following steps: building a power training network and a power test network model; acquiring an electrical characteristic matrix; constructing a characteristic data extraction algorithm; acquiring a vector dimension unified electrical characteristic matrix; acquiring an electrical abstract characteristic matrix of a power network node; defining a node electrical characteristic label; constructing a node characteristic attribute; constructing a to-be-normalized data set and normalizing; building a BP neural network training model; inputting the power test network into the BP neural network training model to obtain a test value after inverse normalization processing; and acquiring a priority protection node set to apply key protection; the method comprehensively considers a power network topological structure and electrical characteristics, extracts electrical characteristic dimensions uniformly, is suitable for high-dimensional, nonlinear and large-scale complex power grid vulnerability evaluation, is universal for all power grids, quickly and accurately obtains a priority protection set, and can effectively improve the safe and stable operation efficiency of a power system.

The application discloses a geological disaster-oriented long-distance pipeline risk intelligent assessment system and belongs to the technical field of infrastructure safety and geological disaster prevention and treatment. The system comprises a dynamic segmentation module, a geological disaster-prone area identification module, a pipeline vulnerability assessment module, a vulnerability and failure consequence weight fusion module, a comprehensive risk calculation module and a risk explanation and updating module. The system can realize fine identification of the risk along the line, interpretable risk contribution analysis and dynamic updating based on monitoring data, can output key risk sections, dominant factors and sectional risk indexes, and is suitable for geological disaster risk assessment and management of oil, natural gas, communication, water supply and drainage and other shallowly buried pipelines.

This invention discloses an efficient statistical analysis method for multi-source data based on a distributed parallel architecture, specifically relating to the field of multi-source data statistical technology. The method includes the following steps: before executing parallel computation, a chaos-aware antifragile scheduling preparation is performed to dynamically generate a self-healing task scheduling strategy; based on this strategy, the dataset and analysis tasks are dynamically allocated to a selected cluster of computing nodes, and computation is executed synchronously using a distributed parallel framework, with dynamic adjustments to task and data flow; the output results of all nodes are aggregated to generate statistical analysis reports and visualization charts, and a graded risk warning signal is generated based on the report data and preset rules. This invention, through a chaos-aware antifragile scheduling mechanism, proactively detects system vulnerabilities and dynamically adjusts task allocation, effectively improving the operational stability and resilience of the distributed system. Simultaneously, intelligent scheduling and continuous monitoring based on real-time vulnerability assessment ensure the efficient and reliable completion of massive data analysis tasks.