75 results about "Vulnerability assessment" patented technology

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.

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.

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.

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 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.

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.

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.

The application discloses a kind of information system security vulnerability risk early warning method based on big data, comprising the following steps: step S1, the index library is established to enterprise internal various levels various types of information systems, network equipment, application software, and safety related data is collected;Step S2, integrate the security intelligence on Internet and third party vulnerability database, obtain the latest security vulnerability information, and carry out classified storage;Step S3, the original data collected is cleaned and preprocessed;Step S4, big data analysis and mining;Step S5, security vulnerability assessment and classification;Step S6, early warning trigger and notification;Step S7, establish security decision support system, provide reasonable security decision suggestion, the third party vulnerability database is collected, stored and analyzed in the application, realize the accurate detection and evaluation of potential vulnerability of business system, network and application program etc. each aspect, and timely security risk early warning and repair suggestion.

The invention discloses a robustness test method for a retrieval enhancement generative software vulnerability evaluation system, and belongs to the technical field of software engineering and information security. The method comprises the following steps: S1, for a vulnerability code to be evaluated, acquiring a related original example set by utilizing a retrieval module; s2, analyzing and generating candidate variants which keep semantic consistency based on the abstract syntax tree; s3, selecting a confrontation example set through a comprehensive scoring function and a beam search strategy; and S4, constructing a prompt context by utilizing an adversarial example to interfere with vulnerability severity evaluation of the large language model, and particularly inducing high-risk / key vulnerability degradation to be misjudged as medium-risk / low-risk. The testing method can effectively reveal potential security risks caused by a retrieval enhancement mechanism, and is used for quantitatively evaluating the robustness of a vulnerability evaluation system.

Disclosed herein are systems and method for detecting vulnerabilities in devices within a network. In one exemplary aspect, a method includes: intercepting a plurality of packets in a network with a plurality of devices; identifying a subset of packets corresponding to a first device of the network; extracting and embedding a plurality of features representing device behavior from the subset of packets into an input vector; executing a machine learning algorithm that receives the input vector and outputs a device type of the first device, wherein the machine learning algorithm is trained using a training dataset including network traffic information labelled with device types and a list of attributes of each respective device type; performing, for the first device, a security scan specific to the device type; and in response to detecting a vulnerability based on the security scan, generating a vulnerability assessment report for the first device.

The invention discloses a network equipment vulnerability assessment method, and relates to the technical field of network equipment vulnerability assessment, and the method comprises the steps: selecting a target cross path, an auxiliary measurement path I and an auxiliary measurement path II from network paths with cross points, and if the target cross path and the auxiliary measurement path I and the auxiliary measurement path II have partial cross point coincidence, determining that the target cross path and the auxiliary measurement path I and the auxiliary measurement path II are overlapped; if the target cross path and the auxiliary measurement path II have partial cross sink coincidence, and the auxiliary measurement path I and the auxiliary measurement path II have partial cross sink coincidence, performing statistics on second to-be-measured risk feature data to which the cross sink belongs under the condition; and if the target cross path and the auxiliary measurement path I or the auxiliary measurement path II have cross point coincidence and the target cross path, the auxiliary measurement path I and the auxiliary measurement path II are in a complete non-coincidence relation, performing statistics on third to-be-measured risk feature data, and performing comprehensive vulnerability estimation value evaluation on the to-be-measured network path according to the three to-be-measured risk feature data. According to the invention, the vulnerability mining accuracy can be improved.

Systems and methods are described herein for performing vulnerability assessment on partially functional software applications (e.g., software applications currently at a phase in the development cycle prior to a user acceptance testing phase). By doing so, the system may detect vulnerabilities, if any, more easily based on the fewer functional components of the application. Additionally or alternatively, curing any vulnerabilities will require fewer modifications to the application's software, architecture, and / or intended functionality (as these characteristics are also earlier in their development cycle).

Methods, system, and non-transitory processor-readable storage medium for regex engine system are provided herein. An example method includes a Continuous Integration / Continuous Delivery (CI / CD) pipeline that triggers a regex engine system to scan source code. The regex engine system scans the source code for a plurality of regular expressions, and determines a regular expression vulnerability assessment associated with the plurality of regular expressions. The regex engine system generates a regular expression vulnerability assessment report comprising the regular expression vulnerability assessment.

Method and apparatus for analyzing operational processes to detect vulnerabilities are provided. The method includes generating a documented workflow by processing a plurality of unstructured documents associated with an operational process, generating an implemented workflow by analyzing source code that implements the operational process in a computing system, generating an executed workflow by processing real-world execution data for the operational process, analyzing the documented workflow, the implemented workflow, and the executed workflow to identify one or more inconsistencies among the workflows and one or more exploits within the operational process, and generating one or more recommended actions to remedy at least one of the one or more identified inconsistencies or the one or more identified exploits.

The application discloses a power transmission and transformation foundation flood vulnerability assessment method and system based on an optimized neural network, relates to the technical field of power facility disaster assessment, and comprises the following steps: obtaining structure parameters and flood boundary information, combining scour dynamic time sequence characteristics to form working condition data; mapping the working condition data to a two-dimensional phase space, and identifying a to-be-supplemented working condition set with insufficient sample coverage; constructing a physical constraint term based on a fluid-solid-seepage coupling equation, generating working condition data by using a physical information generative adversarial network to form a training data set; constructing a vulnerability assessment network and iteratively training the network; based on maximum Lyapunov index monitoring result distribution, when the distribution tends to be concentrated, constructing a negative curvature direction to update network parameters according to a loss function second-order gradient; inputting real-time flood load into the updated network, and outputting an instability risk grade after forward modeling and checking by the coupling equation. The application solves the problems of insufficient sample coverage and loose combination of physical mechanism and data driving, and realizes high-precision risk assessment.

The invention provides an ecological geology vulnerability evaluation method based on an SRP model. The method comprises the following steps: acquiring ecological environment data of a to-be-evaluated region; constructing an SRP model composed of ecological sensitivity, ecological restorability, ecological pressure and ecological function integrity; determining evaluation indexes corresponding to ecological sensitivity, ecological restorability, ecological pressure and ecological function integrity based on the ecological environment data; and based on the evaluation indexes, performing ecogeological vulnerability evaluation by adopting an analytic hierarchy process. According to the method, the structure, the service function and the stability of the ecological system are comprehensively reflected, so that the evaluation system is more scientific and comprehensive, and the regional ecological geology vulnerability condition can be accurately revealed.

The invention discloses a novel power distribution system vulnerability assessment method and system based on a dynamic weight. The method comprises the following steps: establishing a power distribution system vulnerability assessment index system, and determining an initial weight through an analytic hierarchy process (MSDM-AHP) based on a Muslim diagram; collecting safety monitoring data in real time; updating the weight of each index through a dynamic weight adjustment mechanism based on real-time data: calculating the current threat level of each index, dynamically adjusting the weight when the current threat level exceeds a trigger threshold value, and performing normalization processing; and finally, calculating a comprehensive vulnerability factor through a TOPSIS algorithm by using the obtained dynamic weight, and then calculating a comprehensive risk value based on the vulnerability factor, the asset value and the influence consequence to quantify a network security evaluation result of the novel power distribution system. According to the method, the defects of lag and stiffness of a traditional static evaluation model are overcome, and real-time, self-adaptive and accurate evaluation of the network security state of the novel power distribution system is realized.

This application discloses a river functional vulnerability assessment method, equipment, medium, and product, relating to the technical fields of water conservancy projects and river ecological assessment. The method comprises: obtaining basic river information; determining indicator data for river functional vulnerability assessment; constructing a judgment matrix for the indicator data using the analytic hierarchy process (AHP); calculating a weight vector for the indicator data based on the AHP; quantifying and assigning scores to the indicator data based on the basic river information and river flood control standards to obtain a scoring result; and calculating a comprehensive functional score for the river based on the weight vector and the scoring result. The comprehensive functional score is used to assess river functional vulnerability. By introducing the AHP and a multi-indicator scoring system, this application constructs a comprehensive evaluation system for river flood control, navigation, water supply, and ecology. This system can more scientifically and comprehensively reflect river functional vulnerability and provide a reference for river management and governance.