96 results about "Vulnerability assessment" patented technology

Security vulnerability analysis may be performed using policy inference. Application code may have operations that are labeled according to the respective functions that they perform. Some operations may be labeled according to a knowledge database of known operations while others may be inferred through similarity to known operations. The knowledge database may be associated with libraries of programmatic interfaces. Once components of the application code are labeled, a vulnerability database may be that identifies potential vulnerabilities based on data sources, data sinks and threat mitigation operations. Using the labeled operations, one or more potential vulnerabilities may be identified based on labeled data sources and data sinks. The application may then be evaluated for potential security threats based on the identified potential vulnerabilities.

The present invention discloses a full - probability risk quantitative assessment method for the landslide of a muck yard considering the three - dimensional spatial variability of muck, which includes the following steps: conducting borehole exploration of the muck yard site to obtain muck yard data; obtaining random field parameters characterizing the spatial variability of muck; simulating the three - dimensional spatial variation characteristics of muck; based on the limit equilibrium method, conducting the three - dimensional slope stability assessment of the muck yard and estimating the potential landslide volume; simulating the possible migration ranges under various landslide volumes; statistically collecting the life and property data of downstream disaster - bearing bodies, and based on the vulnerability assessment formulas for buildings and personnel, estimating the vulnerability of disaster - bearing bodies according to the landslide intensity; establishing a full - probability risk assessment formula for the muck yard landslide, and conducting quantitative risk assessment and risk rating. The present invention considers the unique spatial variation characteristics of muck, analyzes the stability, potential sliding position and volume from a three - dimensional perspective, and combines with the quantitative vulnerability assessment method to achieve accurate risk assessment.

The invention discloses a new energy transmission and distribution collaborative consumption intelligent control method and system, and relates to the technical field of new energy, and the method comprises the steps: collecting real-time monitoring data of each node in a power system, and carrying out the preprocessing; constructing a power grid complex network model according to the adjacent matrix to calculate the power weight of each power transmission line; and storing all data into a relational database and managing the data. According to the method, the nodes and the edges of the power system are accurately modeled, the adjacent matrix is constructed, the power flow of a power grid topological structure and a power transmission line is comprehensively reflected, the load and the operation state of the power grid are accurately evaluated, and errors in a traditional method are avoided; the propagation process of the fault in the power grid is simulated in real time, and the fault propagation rate and range are predicted, so that multi-stage early warning is realized, the accuracy of power grid vulnerability assessment is improved, and the stability and emergency response capability of the power grid are enhanced.

The invention is suitable for the technical field of payment data system vulnerability assessment, and provides a payment data security assessment method and system.The method comprises the steps that after vulnerability scanning and penetration testing operation is conducted on a payment data storage system, a plurality of scanned security defects are determined, and acquiring a preliminary risk priority evaluation index allocated to each security defect, and acquiring a historical operation log of the payment data storage system. According to the method, the mechanism of double correction factors is introduced, so that the repair priority evaluation of the potential risk vulnerabilities in the payment data storage system is optimized. Firstly, in combination with a hysteresis effect correction factor and a secret key updating frequency deviation correction factor, the accuracy and timeliness of vulnerability repair are effectively improved;

A computer implemented method processes vulnerabilities. A processor set identifies a vulnerability and a description of the vulnerability in a vulnerability report. The processor set extracts a set of key exploit conditions for the vulnerability from the description of the vulnerability. The processor set generates a set of different code snippets for the set of key exploit conditions. The processor set searches a set of repositories for a match between the set of components and the set of different code snippets to form a search result. The processor set identifies vulnerability assessment information relating to an applicability of the vulnerability in response to the match between the set of components and the set of different code snippets in the search result.

Mechanisms are provided for assessing control checks and components of a vulnerability management system (VMS) for a computing infrastructure. A security vulnerability risk gap assessment of the VMS is executed to generate result data for a plurality of control checks. For each control check, the mechanisms: classify the control check into a maturity level having a corresponding maturity level value; classify each of a plurality of vulnerability assessment security control rating (VASCR) elements into a predetermined classification having corresponding classification values for the control check; and combine the maturity level value and the VASCR element classification values to generate a prioritization score. A graphical user interface output is generated comprising a representation of a prioritized ranked listing of control checks based on the prioritization scores associated with each of the control checks in the plurality of control checks.

The invention discloses a disaster prevention and control refined division method based on map data. The method comprises the following steps: step S10, subdividing an evaluation unit based on map data, landform data and administrative division; step S20, comprehensively selecting prevention and treatment alternative areas through risk assessment, vulnerability assessment and disaster assessment; step S30, constructing an index system from three dimensions of dangerousness, exposure and vulnerability; s40, performing index quantification based on the map data and the investigation and survey data, and calculating the weight of each index in combination with the information entropy; step S50, calculating a comprehensive index of each evaluation unit by adopting a Jenks natural fracture method; and step S60, finely delimiting disaster prevention and control key areas in combination with actual management department experience and comprehensive indexes. The method is convenient for later-period management, avoids a large amount of repeated work, and has relatively high objectivity and reference value.

The present disclosure includes techniques for performing automated vulnerability assessment. The technique includes receiving a designation associated with a Common Vulnerabilities and Exposures (CVE) and retrieving one or more attributes describing the CVE. The technique also includes aggregating, by one or more distributed data acquisition operations, CVE data associated with the CVE, wherein the one or more distributed data acquisition operations electronically capture data from a plurality of networked information sources in parallel, and generating, via a first machine learning model, a prompt data structure based on the CVE designation, the one or more attributes, and the CVE data. The technique further includes transmitting the prompt data structure to a second machine learning model that generates a vulnerability assessment, generating one or more CVE reports based on the vulnerability assessment, and performing an automated remediation action based on the one or more CVE reports.

The invention discloses a software supply chain vulnerability assessment method based on multi-Agent collaboration and dynamic hypergraph learning, which is used for opening a link between vulnerability assessment and repair and realizing full-life-cycle collaboration linkage. For the problem of vulnerability assessment and repair splitting, the method covers the core capabilities of static analysis (SAST), dynamic detection (DAST), interactive analysis (IAST), intelligence analysis and the like. Evaluation results of various agents are output through standardized vulnerability portraits, and a subsequent repair strategy is driven to be automatically generated and executed, so that whole-process closed-loop linkage and fine control of'discovery-evaluation-repair-verification 'of vulnerabilities are realized.

The invention relates to the technical field of information security, in particular to a cloud security vulnerability management platform based on AI. Comprising a centralized management module, a distributed event collector network, an automatic vulnerability assessment system, an intelligent repair suggestion module, an interactive user interface, a self-adaptive vulnerability protection mechanism, a continuous learning and updating mechanism, a multi-level identity verification system, an automatic security test tool set and an extensible plug-in architecture. The AI-based cloud security vulnerability management platform provided by the invention is more comprehensive, intelligent and flexible, so that the problems are solved, and the security of a cloud environment and the use experience of a user are improved.

The invention provides an asset vulnerability repairing method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of security scanning. The repairing method comprises the following steps: identifying potential asset vulnerabilities of an asset association graph at the current moment based on a vulnerability detection fusion model; based on a multi-dimensional vulnerability evaluation model, evaluating a vulnerability score of the potential asset vulnerability at the current moment; and based on the vulnerability score and a repair strategy optimization model, determining a repair plan of the potential asset vulnerability at the current moment. The problems of long time consumption, low efficiency and low timeliness in the process of finding vulnerabilities and taking effective defense measures in related technologies are at least solved. The method is suitable for vulnerability scanning and repairing scenes.

Systems and methods for proactively monitoring the inherent cyber-tech risk of software and hardware components are disclosed. In one embodiment, a method for proactively monitoring a cyber risk of a computer program may include: (1) receiving, by a product / version risk assessment computer program executed by an electronic device and from a user computer program executed by a use electronic device, an identification of a plurality of proposed components to include in the computer program; (2) retrieving, by the product / version risk assessment computer program, vulnerability information for each of the plurality of proposed components, wherein the vulnerability information identifies a security vulnerability for the proposed component; (3) generating, by a product / version risk scoring computer program, a risk score for the computer program under development based on the vulnerability information; and (4) returning, by the vulnerability assessment computer program, the risk score to the user computer program.

Disclosed are configurations to enable reverse engineering and characterizing machine learning algorithms through controlled data manipulation. A target machine learning system is analyzed by obtaining compatible data, applying data poisoning techniques to induce controlled responses, and generating a unique model signature that quantifies the system's response patterns. The model signature is compared against a codebook of known algorithm signatures to identify the underlying algorithm type. The codebook is built and maintained by applying systematic data manipulations, such as data poisoning techniques, to known machine learning algorithms and recording their characteristic responses. Multiple data poisoning techniques may be applied sequentially, with features extracted from the system's responses assembled into multi-dimensional feature vectors. This approach enables identification and vulnerability assessment of machine learning systems without requiring access to their internal structures or source code, supporting both offensive operations to identify vulnerabilities and defensive operations to enhance robustness.

The invention discloses a vulnerability assessment and reinforcement method based on multi-agent cooperative reasoning and related equipment. The method comprises the following steps: a, obtaining reasoning data including reasoning facts and rules; b, taking the penetration test host as an initial node, taking a test target as a termination node, and generating a potential attack path through an attack path reasoning agent; c, removing repeated paths by using an attack path deduplication agent; d, performing prefix sub-path recursive reasoning on the residual paths by the sub-path exploration agent to generate extended attack sub-paths; e, removing repeated paths in the extended sub-paths through a sub-path duplicate removal agent; f, replacing the initial node with the reasoning initial node of the remaining extended sub-paths, recursively executing the steps b to f, exiting the current recursion stage when no new extended sub-path is added, and terminating recursion until no new potential attack path is added, so as to obtain a final attack path set; and g, recording vulnerability utilization frequency in the set, and selecting vulnerability reinforcement according to the vulnerability utilization frequency. According to the method, the problems of insufficient understanding of massive network configuration information, attack path combination explosion and repair decision optimization under service constraints can be solved, and dynamic deduction and active defense of network attack paths are realized.

The invention provides a multi-modal long text vulnerability classification method based on transfer learning and loss adjustment, belongs to the technical field of computers, and solves the technical problems that an existing method is low in classification accuracy and poor in generalization ability. Comprising the following steps: S1, preprocessing a data set; s2, dividing a training set, a verification set and a test set; s3, splicing the bimodal information of the vulnerability description and the code information into long text input, and designing a sliding window segmentation fusion method; s4, finely adjusting the pre-training language model to complete a vulnerability assessment task, and adopting an early stop strategy; s5, migrating knowledge learned in the evaluation task to a vulnerability classification task; s6, designing a dynamic loss function adjustment mechanism to carry out weight adjustment; and S7, inputting test set data into the trained model, and performing vulnerability classification prediction. The method has the beneficial effect that the performance of the vulnerability classification task is improved through transfer learning and loss adjustment.

The present invention discloses a method for evaluating the progressive collapse vulnerability of structures by combining static and dynamic probabilistic analyses, comprising the following steps: (1) obtaining the design parameters affecting the progressive collapse resistance of the structure; a sample set of design parameters; (2) constructing corresponding static and dynamic progressive collapse deterministic numerical models; establishing corresponding sub-models according to the sample set; (3) performing Pushdown analysis; (4) performing non-linear displacement time history analysis under different load intensities; (5) fitting to obtain the probability density function of the progressive collapse resistance of the structure and calculating the corresponding mean value and standard deviation; (6) fitting to obtain the probability density function of the progressive collapse load effect of the structure and calculating the corresponding mean value and standard deviation; (7) obtaining the scatter points of the progressive collapse vulnerability of the structure; (8) obtaining the progressive collapse vulnerability curve of the structure; the present invention obtains accurate vulnerability assessment results.

A method may include querying, via a processing system, a database comprising a list of cybersecurity threats associated with operational technology (OT) devices within an industrial system. The method may also include identifying OT devices associated with the list of cybersecurity threats, generating scripts configured to confirm that the OT devices are associated with at least one cybersecurity threat of the list of cybersecurity threats, and sending the scripts to the one or more OT devices. The method may then involve determining that the OT devices are associated with the at least one cybersecurity threat based on responses from the OT devices generated based on the scripts, generating instructions for resolving the at least one cybersecurity threat based on a generative artificial intelligence (AI) system, and sending the instructions to one or more devices.

This invention discloses a software vulnerability assessment method, system, and medium based on contrastive learning. The software vulnerability assessment method of this invention includes: training a feature extraction model using a contrastive learning-based training method, wherein the training method includes learning to extract feature vectors of vulnerability attributes by comparing positive and negative samples of vulnerability attributes; extracting feature vectors from the vulnerability attributes of the vulnerability to be assessed using the trained feature extraction model; and classifying the feature vectors using a vulnerability classifier to obtain the classification result of the vulnerability to be assessed. This invention aims to improve the accuracy of software vulnerability assessment by utilizing contrastive learning in the feature extraction process to obtain more effective high-dimensional vulnerability feature representations.

This invention belongs to the field of fan control technology. It provides a laboratory exhaust gas-induced high-altitude jet fan control system, comprising: a multi-source data acquisition module for real-time acquisition of multi-dimensional time-series data related to laboratory exhaust gas emission safety, including fan operating parameters, building environment parameters, exhaust gas source equipment status parameters, and meteorological environment parameters; and a predictive maintenance and early warning module based on the multi-dimensional time-series data. By integrating multi-source data perception, chronic risk trend prediction, system vulnerability assessment based on digital twins, and graded resilience control strategies, a closed loop of "perception-prediction-assessment-decision-execution" is constructed. This achieves early identification, proactive early warning, and adaptive control of coupled risks between "equipment-building-environment-personnel," thereby enhancing the proactive safety capability to prevent toxic exhaust gases from entering the breathing zone of personnel.

The present application relates to the field of power system application technology, and specifically to a graph-theory-based power CPS vulnerability assessment method and system, the method comprising: constructing an unweighted network topology model of a power CPS network, obtaining each node of the power CPS network, the edges between nodes, and the load voltage data of each node; obtaining the load anomaly of each node of the power CPS network; obtaining the voltage recovery responsiveness of each node of the power CPS network; obtaining the operation robustness of each node of the power CPS network; obtaining each community in the power CPS network and the associated communities of each community; obtaining the voltage mutation time sequence of each node in the power CPS network; obtaining the degree of cascading failure of each community in the power CPS network; obtaining the cascading criticality of each community in the power CPS network; obtaining the power CPS network vulnerability factor, and evaluating the power CPS vulnerability. The present application reduces the risk of damage to the safety of the power system.

The invention discloses a method for evaluating vulnerabilities by combining vulnerability codes and vulnerability description texts and using a cross-task attention mechanism. The method comprises the following steps: firstly, for a vulnerability code part, converting the vulnerability code part into a control flow diagram, and generating a plurality of execution paths from the control flow diagram; for the vulnerability description text part, some key vulnerability elements are extracted from the vulnerability description text part; then, considering that seven evaluation tasks have certain relevance, when feature representation of codes and texts is learned, a cross-task attention mechanism is introduced to capture relevance features among the tasks; and finally, carrying out weighted summation on the probability predicted by the classifiers at the code level and the text level to obtain a prediction result of final vulnerability assessment.

A private network is scanned to identify devices, and profiling identified devices for vulnerabilities. A score is determined from a Common Vulnerability Scoring System (CVSS) database for each vulnerability individually that characterizes severity. A score is determined for a collection of vulnerabilities. Exponential tapering functions curb an influence of large numbers of low priority threats on the collection score. The collection threat score increases with severity of the collection of vulnerabilities.

This invention belongs to the technical field of computer network security and relates to a method and system for assessing vulnerabilities in network communication transmission nodes. The method includes: matching vulnerabilities based on static attributes of communication nodes, analyzing their exploitation prerequisites and impacts to generate an asset knowledge base; analyzing network topology and access control permissions to generate potential communication topologies; collecting information such as session connections and process activities, and encapsulating it into critical state transition events; determining whether the transition events meet the vulnerability exploitation prerequisites and updating the logical state; generating dynamic attack paths based on the updated logical state and communication topology, constructing a complete attack graph, and calculating a network-wide vulnerability risk score; simulating intervention measures for nodes with risk exposure values ​​greater than a preset threshold, and generating defense hardening strategies based on changes in the risk score. This invention addresses the problem of insufficient accuracy and timeliness in risk assessment results.

The present invention discloses a bridge seismic vulnerability assessment method based on an influence line correction model, belonging to the technical field of bridge seismic vulnerability assessment, and comprising the following steps: S1: finite element model correction; S2: seismic vulnerability assessment. The present invention uses the measured bridge structure strain influence line as the target parameter and utilizes an artificial neural network to correct the bridge finite element model; performs seismic vulnerability analysis on the optimized model of the corrected bridge finite element model, imports 20 seismic motion records and 1 artificial seismic wave, extracts the displacement response of the bridge structure under different peak ground accelerations, plots the structural exceedance probability, and calculates the vulnerability matrix. Furthermore, an expression for the structural seismic damage index is constructed based on the β probability distribution, and a seismic damage comparison curve is plotted using the expected value of the seismic damage index, thereby achieving an accurate evaluation of the seismic resistance of existing bridge structures.

This vulnerability determination device being an example of an information processing device has: a deployment unit that deploys an attack content into a content provision device providing content via a server device having a cache function; a registration unit that registers, into a cache of the server device, the attack content deployed in the content provision device; and a determination unit that determines vulnerability to a threat which causes a browser to download the attack content via the cache of the server device.

The invention discloses an intelligent power grid vulnerability analysis method and system, and relates to the technical field of power grid analysis, and the method comprises the following steps: constructing an intelligent power grid graph structure based on a topological structure, business attributes and physical attributes of an intelligent power grid; performing graph representation learning to obtain a plurality of representation vectors of different dimensions; performing fusion, nonlinear transformation and mapping on the plurality of representation vectors to obtain a score of each node, performing descending sorting on different scores, and taking nodes corresponding to the first n scores as key nodes; different attack simulation evaluations are carried out on the key nodes, structural evaluation is carried out on the key nodes through vulnerability analysis indexes, and importance evaluation is carried out on the key nodes through service attributes; and weighting the first score, the second score and the third score to obtain a vulnerability score of the smart grid. According to the method, comprehensive analysis is carried out from multiple dimensions, and the comprehensiveness and accuracy of power grid vulnerability assessment are improved.

The invention discloses a rigid-body-like medical equipment earthquake vulnerability evaluation method considering multi-motion-state coupling, and aims to solve the problem that the earthquake vulnerability of wheeled medical equipment under the influence of factors such as strong shock, rolling, sliding-swinging coupling and non-uniform friction is difficult to accurately evaluate only depending on classic rigid body sliding or overturning criteria. Medical equipment is regarded as a rigid body, the motion state of the rigid body at each moment in the earthquake process is determined according to geometric parameters and supporting information of the rigid body, the gravitational acceleration and the earthquake oscillation time history, and the motion states comprise fixed foot support sliding, trundle rolling, swinging and sliding-swinging coupling. The maximum translation displacement and the maximum rotation angle of the rigid body are obtained based on the motion state, the damage state of the rigid body is judged according to the maximum translation displacement and the maximum rotation angle, nonlinear incremental dynamic analysis is conducted on the rigid body, an earthquake vulnerability function is obtained, the larger the median value of the earthquake vulnerability function is, the lower the earthquake vulnerability of the rigid body is, and otherwise, the higher the earthquake vulnerability of the rigid body is. The invention belongs to the field of shock resistance of medical instruments.

The invention provides a lightweight software vulnerability assessment method based on particle swarm optimization and knowledge distillation, belongs to the technical field of computers, and solves the technical problems of low computing resource consumption and low reasoning speed of an existing large code model in software vulnerability assessment. According to the technical scheme, the method comprises the following steps: (1) constructing a corpus containing vulnerability source code information as a basic data set for vulnerability assessment; (2) searching an optimal framework of the student model through a particle swarm optimization algorithm so as to achieve balance between model performance and resource consumption; (3) extracting knowledge from the large teacher model by using a knowledge distillation technology and transmitting the knowledge to a smaller student model so as to keep the accuracy of vulnerability assessment; and (4) executing a vulnerability assessment task of the lightweight model on the resource-limited equipment. The method has the beneficial effects that the high-accuracy vulnerability assessment capability is kept while the size of the model is remarkably reduced.

A system to accelerate the introduction of new products in AI and cloud computing through early design development, comprising: an AI-driven design optimization module configured to analyze initial design inputs and provide real-time recommendations to optimize system architecture and performance; a cloud-based collaborative development module configured to facilitate real-time collaboration between cross-functional teams with version control, instant feedback mechanisms, and cloud storage integration; an automated simulation and validation module configured to perform AI-based simulations and tests on synthetic data for early-stage performance validation and fault detection; a digital twin and prototyping module configured to create virtual replicas of AI models, cloud applications, and hardware components for real-time testing prior to physical implementation; a predictive analytics and decision support module configured to forecast design challenges, resource requirements, and market viability using AI-driven predictive modeling; an agile development and iteration module configured to support iterative product development through rapid prototyping, continuous testing, and dynamic feature adjustments based on performance feedback; an automated regulatory compliance and security assessment module configured to perform real-time regulatory compliance checks and security and vulnerability assessments for AI and cloud computing products; a cloud infrastructure and deployment module configured to enable seamless integration with cloud platforms for containerization, resource allocation, and scalable deployment of AI-driven applications; a learning module configured to analyze user feedback, performance metrics, and post-launch data using machine learning algorithms to deliver adaptive improvements; and continuous feedback a marketplace integration and marketing module configured to assess product maturity, optimize pricing models, and facilitate AI-driven demand forecasting for market success.

A multi-dimensional vulnerability assessment method for cold-region bridge structures under the impact of drift ice relates to the technical field of bridge vulnerability assessment. The method includes generating a grid format for the finite element model parameters of the bridge structure, randomizing and optimizing the parameters, statistically analyzing the observed data of drift ice loads, constructing a probability distribution model, constructing a pier damage factor matrix, constructing a probability distribution model for the bearing capacity of the piers after a disaster, obtaining a probability distribution function for the damage factor of the piers under the action of drift ice loads, constructing a probability distribution function for the bearing capacity conditions of the piers after a disaster, and conducting damage status judgment and assessment and vulnerability assessment. For double-span continuous beam bridges, by considering the random variability of the bridge structure parameters in the finite element model, the accuracy of the vulnerability assessment can be significantly improved. Combined with the multi-dimensional characteristics of drift ice impact, the complexity of the impact behavior is fully characterized, which is more efficient and systematizable, and is helpful for predicting the impact resistance of bridge structures and evaluating their overall safety performance.