347 results about "Trust score" patented technology

The invention provides a right and interest exchange verification system, belongs to the technical field of right and interest management systems, and effectively deals with complex right and interest fraud through multi-modal dynamic behavior trust chain construction. The system comprises a multi-modal behavior acquisition module, a real-time acquisition input behavior, equipment and environment characteristics, a federated behavior modeling module, a cross-platform construction dynamic behavior baseline library and trust score output module, a scene risk assessment module, a hierarchical verification strategy supporting module, a dynamic scheduling execution module, and an intelligent resource allocation and auditing module. Data desensitization and feature extraction are realized through lightweight edge calculation; federated learning fuses local and global credit scores, and combines a dynamic attenuation model and scene perception weight adjustment; the multi-modal cross validation is used for coping with different risk levels; the intelligent resource scheduling resists the DDoS attack; the system realizes balance between high-precision fraud identification and user experience through behavior continuity protection, confrontation sample detection and interpretable scoring atlas.

The invention discloses a data security dynamic evaluation system and a protection method, belongs to the technical field of information security, and is used for solving the problem of terminal equipment access control and behavior risk dynamic collaborative protection. The method comprises the following steps: generating fingerprints through terminal equipment features, verifying authorization, inputting access feature slices into a time sequence model by authorized terminal equipment, generating a trust score based on cosine similarity of a behavior sequence and a prediction sequence, and constructing a Markov model to dynamically select an authentication mode according to the trust score; calculating a risk index by combining network and geographic information, and distributing the risk index to a real or virtual environment; and the unauthorized terminal equipment distributes the virtual environment after registration. Differentiated monitoring is carried out, a time sequence library is established in the virtual environment, and doubt scores are generated through sequence matching; the state deviation of the real environment is calculated through a hidden Markov model, and the risk score is generated by fusing the multi-dimensional features. And based on the result management authority, the suspicion terminal equipment isolates and shrinks the authority, the compliance terminal equipment authenticates and migrates, and the access is regulated and controlled according to the minimum privilege principle and the time window mechanism.

The invention provides a digital culture product right confirmation and data sharing method and system based on a block chain, and relates to the technical field of digital culture, and the method comprises the steps: generating a comprehensive trust score through a self-adaptive trust evaluation model by fusing timeliness, integrity and consistency trust scores, and constructing a self-evolution data trust network and an encrypted data synchronization channel. After smart contract verification, the access authority of the data requester is determined according to the trust score, access features are extracted through a bidirectional long-short term memory network and a multi-head attention mechanism, a proof chain node is constructed, a mapping relation with an authority confirmation certificate is established by using a graph neural network, and the access contribution degree is calculated through homomorphic encryption and federated learning; and finally, performing income distribution according to a sectional income distribution curve. According to the invention, the problems of difficult right confirmation and low data sharing credibility of digital culture products are effectively solved, and safe and controllable data sharing and income distribution are realized.

The embodiment of the invention provides a security authentication method, device and system for a test platform, and a storage medium, and relates to the technical field of network security. The method comprises the following steps: performing fingerprint acquisition on virtual and real equipment to generate a unique identifier, dynamically slicing based on a software defined network to form an isolated test environment, and injecting a zero-trust access strategy associated with the fingerprint; and based on the zero-trust access policy and the device fingerprint, allocating a minimum permission through an attribute-based access control model, continuously evaluating device behaviors to calculate a dynamic trust score, and adaptively updating the permission according to the trust score. By constructing a full-process security certification system and through multi-dimensional mechanism cooperation, dynamic risk perception and timely strategy adjustment, fine-grained control of virtual and real equipment access in an industrial control scene is realized, the dynamic adaptability and risk resistance of a test platform in a complex network environment are improved, and the test efficiency is improved. Equipment access safety, authority distribution minimization and behavior monitoring continuity are ensured, and the safety risk of the industrial control system is reduced.

The invention provides a government affair cloud cross-department data security sharing method and device, and relates to the technical field of data security. The method comprises the following steps: a main chain of a block chain performs first-level verification on a requester based on a VerifyCredate contract and an access policy white list set by a department to which government affair data belongs; the access strategy is a dynamic attribute base access strategy generated by a department to which the government affair data belongs based on a dynamic attribute encryption technology; under the condition that the first-level verification is passed, generating an access credential, synchronizing the access credential to a side chain of the block chain, generating a zero-knowledge proof by the side chain according to the operation log, and initiating a zero-knowledge proof verification request to the main chain to perform second-level verification; and under the condition that the main chain passes the second-level verification, determining a cross-domain trust score between the government affair data requesting department and the department to which the government affair data belongs based on the digital certificate submitted by the requester by adopting a federal model, and performing third-level verification according to the cross-domain trust score. According to the invention, the data sharing security can be improved.

The invention discloses an intelligent warehousing system based on supply chain management, and particularly relates to the technical field of warehousing identification and supply chain information processing, and the system comprises an information collection module, a trigger judgment module, a data verification module, a risk assessment module and a state grading module. Collecting target carrier identification data through a multi-source device, performing credibility scoring according to stability, consistency and a historical confidence level, and judging whether to enter a verification process or not; in the verification stage, an identification consistency index and a supply chain synchronization credibility index are generated, and a risk score is output by the model; implementing credible marking, image checking or multi-source reconstruction according to the scoring level, and realizing identification result hierarchical control and operation closed loop; according to the method, the identification vector is constructed through multi-source acquisition, so that the state of the target carrier is accurately acquired; the identification processing efficiency is improved by adopting a hierarchical judgment mechanism; a differential response strategy is executed based on the risk score, a recognition credibility closed loop is constructed, and the recognition accuracy and operation safety of the system are effectively improved.

Systems, apparatuses, methods, and computer program products are disclosed for providing a device trust dashboard. An example method includes generate a device profile for a first user device associated with a user account of a user. The example method further includes determining a device trust score and an authorization rule set for the first user device and updating the device profile for the first user device. The example method further includes receiving a device trust dashboard request and performing a verification routine for the user. The example method further includes providing the device trust dashboard in an instance the user is successfully verified.

Interactive search trust assessment systems and techniques are described. In some examples, an interactive search trust assessment system receives a prompt associated with a search for information about an object. The prompt is based on a user input and data from a data structure. The interactive search trust assessment system processes the prompt using a trained machine learning model to generate a response. The response is responsive to the prompt. The response includes the information about the object retrieved from the data structure as a result of the search. The interactive search trust assessment system generates a response trust score associated with the response. The response trust score is based on one or more trust scores associated with the information about the object retrieved from the data structure. The interactive search trust assessment system outputs the response based on the response trust score exceeding a threshold.

The invention provides a federated learning backdoor defense method based on singular value decomposition and model weight amplification, and the method comprises the steps: carrying out the normalization processing of a model updating parameter locally trained by a client, and obtaining a normalized model updating parameter; on the basis of the normalized model updating parameters, model updating parameters after dimension reduction are obtained; performing clustering algorithm processing on the model updating parameters after dimension reduction to obtain clustered clusters; obtaining cluster model parameters based on the clustered clusters; combining the cluster model parameters into a cluster model parameter matrix; performing singular value decomposition on the cluster model parameter matrix to obtain a singular vector; obtaining a trust score through the singular vector; obtaining global model update parameters based on the trust score; calculating by utilizing the global model updating parameters to obtain a global model; and adding Gaussian noise to the global model through a differential privacy mechanism to obtain a final global model. According to the method, backdoor attacks can still be effectively resisted under the scene that the client data sets are non-independent and identically distributed.

The invention provides a block chain dynamic trust evaluation system and method based on artificial intelligence, and the method comprises the steps: carrying out the time sequence modeling of the transaction data of a block chain node and the consensus historical record of the node participation consensus, obtaining the behavior credibility of the block chain node, calculating the resource contribution degree of each transaction node, and carrying out the calculation of the resource contribution degree. Fusing the behavior credibility and each resource contribution degree to obtain a consensus score of each transaction node; constructing a node graph neural network of the block chain based on the transaction topological relation and the interaction frequency between the transaction nodes, detecting a plurality of malicious nodes in the block chain by using the node graph neural network, and further evaluating a consensus influence coefficient of the current malicious node aggregation on the block chain node consensus; and performing punitive correction on the consensus score of each malicious node through the consensus influence coefficient to obtain a final trust score of each transaction node in the block chain. Based on the above scheme, punishment correction of malicious node aggregation in blockchain node trust evaluation can be realized.

The embodiment of the invention provides a virtual power plant terminal access control system based on dynamic permission, and the system comprises an identity authentication module which is configured to collect hardware invariant features and firmware signature information of terminal equipment and generate an encryption certificate; the trust evaluation module is in communication connection with the identity authentication module and is configured to continuously monitor communication behavior characteristics and service instruction characteristics of the terminal and output a dynamic trust score; the dynamic authority management module is in data connection with the trust evaluation module and is configured to adjust the access authority level in real time according to the mapping relation of the dynamic trust score in a preset authority level interval; the strategy engine module is configured to generate an access control instruction when abnormal behavior characteristics are detected; and the behavior monitoring module interacts with the strategy engine module and the dynamic authority management module and is configured to execute blocking operation and feed back an execution result to the log auditing module. And the hardware invariant features and the dynamic behavior features are subjected to fusion evaluation, so that the terminal credibility quantification precision is improved.

The invention provides a large model dynamic protection method and system based on a zero-trust architecture. The method comprises the steps that a security proxy gateway receives an access request; authenticating an initiating main body of the access request, collecting context information and transmitting the context information to a strategy decision point; the strategy decision point calculates a trust score in real time based on a dynamic trust evaluation model and performs real-time evaluation in combination with an access control strategy to generate a dynamic authorization judgment result; if the access is allowed, forwarding the access request to a large language model server, and performing input security filtering; the large language model server generates response content and performs output security filtering; and returning the final response subjected to the output security filtering to the initiating main body through the security proxy gateway. According to the method, a multi-layer protection framework is constructed, a dynamic trust evaluation model is introduced, and a content filtering layer is deployed, so that continuous permission verification, risk adaptive control and full-link content security protection are realized, and the service security of a large model is effectively guaranteed.

The embodiment of the invention discloses an identity authentication method and device based on federal learning, a medium and a program product. The method comprises the following steps: constructing a local training data set by acquiring an original behavior number of a local user, training a local model based on the data set, acquiring a local model parameter set, and performing selective encryption transmission on the parameter set to generate an encrypted parameter packet; performing homomorphic encryption aggregation on the encryption parameter packets of the plurality of clients to generate an aggregation encryption parameter set, and performing differential privacy protection and bit-by-bit scrambling processing on the aggregation encryption parameter set to obtain a global model parameter set; the method comprises the following steps: acquiring a real-time feature vector set by collecting real-time behavior data of a user, performing feature matching on the vector set and a global model parameter set to acquire a trust score, and acquiring an identity authentication result of each client user in combination with a preset score threshold. According to the method, the identity authentication of each client user can be completed on the premise of performing privacy protection on each data in the federal learning process.

A system and method are disclosed for making real-time access control decisions based on a trust score computed from multiple factors across users, devices, applications, and infrastructure. The trust score is used to provide differentiated and least privileged access to sensitive corporate data, thereby improving security posture and reducing the risk of data breaches. The system exposes the trust score to users, enabling them to check their and their device's security posture and improve it on their own. Additionally, the system provides an intuitive interface for security administrators to write policies based on the trust score, eliminating the need for role proliferation and simplifying access control management.

The embodiment of the invention provides an access request security assessment method and device, a resource server, a storage medium and a program product, and relates to the technical field of cloud native security. According to the method, entity attributes, access context, behavior features and environment features in an access request are obtained after identity verification is passed, and the features are input into a pre-trained long-short memory network model to dynamically generate trust scores; therefore, the trust evaluation of the access request is changed from the traditional static rule judgment to an intelligent scoring mechanism based on the multi-dimensional dynamic characteristics. After the trust score exceeds the score threshold and the target policy rule matched with the access request is found in the policy rule base, the access request is processed based on the target policy rule, and refined and dynamic security management of the access request is realized through combination of a multi-dimensional intelligent scoring mechanism and a policy rule matching mechanism; therefore, the security and the intelligent level of access control in the cloud native environment are improved.

The invention provides a network security risk assessment system and method based on artificial intelligence, relates to the technical field of network information security, and adopts innovative fusion of a dynamic collaborative aggregation concept (DSA) and a trusted game motivation mechanism (TGIM) to realize efficient cross-organization collaboration on the premise of completely protecting data privacy. According to the DSA algorithm, model parameters are dynamically weighted and aggregated by using a data utility factor (DUF) and a threat association degree quantity (TCM), a high-quality global model can be generated without sharing original data, the risk of data leakage is greatly reduced, the TGIM designs a trust score and an exponential return function based on the game theory, high-quality contribution is stimulated, malicious behaviors are effectively prevented, cooperation fairness is ensured, and the method has the advantages of being high in robustness and high in reliability. The decentralized cooperation mode significantly improves the ability of multi-organization cooperation to cope with complex network threats, seamlessly combines privacy protection with efficient cooperation, provides a brand new security assessment normal form for sensitive industries such as finance and medical treatment, and has wide application potential.

The invention provides a satellite internet endogenous security grading identity authentication method based on dynamic trust evaluation, which comprises the following steps: a user registration stage: collecting and storing all identity information of a satellite internet user and information of entity equipment used for login; in a user login stage, performing identity authentication on the user according to a security level identity authentication process corresponding to the user login authentication identity, and allowing the user to log in after successful authentication; and determining an initial trust value based on the user identity information, obtaining a dynamic trust score based on the dynamic behavior and the initial trust value, and judging that the identity is abnormal when the dynamic trust score is lower than a set security threshold.

The invention discloses a dynamic access control method and device of a baseboard management controller, equipment and a storage medium, and relates to the technical field of server hardware security management.The method comprises the steps that when an access request is detected in a trusted operation environment, multi-dimensional context attributes associated with the access request are collected; performing dynamic trust evaluation based on the multi-dimensional context attribute, and generating a dynamic trust score corresponding to the access request; and executing an access control decision corresponding to the access request according to the dynamic trust score. Compared with a traditional static trust model, the dynamic trust evaluation is carried out based on the multi-dimensional context attribute under the trusted operation environment of starting verification of the baseboard management controller, and the differentiated access control decision is executed according to the dynamic trust score, so that the access risk is accurately identified, and the reliability of the system is improved. The security vulnerability of'permanent trust in one-time authentication 'in the prior art is avoided, and the access security of the substrate management controller is improved.

Systems, devices, and methods are described herein for calculating a trust score. The trust score may be calculated between entities including, but not limited to, human users, groups of users, organizations, or businesses / corporations. A system trust score may be calculated for an entity by combining a variety of factors, including verification data, a network connectivity score, publicly available information, and / or ratings data. A peer trust score targeted from a first entity to a second entity may also be calculated based on the above factors. In some embodiments, the peer trust score may be derived from the system trust score for the target entity and may take into account additional factors, including social network connections, group / demographic info, and location data. Finally, a contextual trust score may be calculated between the first and second entities based on a type of transaction or activity to be performed between the two entities.

Systems and methods for assessing trustworthiness of customer accounts in retail and e-commerce systems using machine learning are disclosed. In some embodiments, a disclosed method includes: receiving, from a computing device, a trust assessment request regarding a customer account; generating at least one trust-related feature or quantitative attribute associated with the customer based on historical transaction data and relevant account information of the customer; computing, using at least one machine learning model, trust score data of the customer based on the at least one trust-related feature; and transmitting, in response to the trust assessment request, the trust score data of the customer to the computing device.

Described herein are methods and systems for evaluating datasets through a multi-faceted scoring approach that assesses data quality across multiple dimensions. A trust score for a dataset may be generated by a scoring engine and displayed on a user interface, providing a comprehensive assessment of the dataset's readiness for use in artificial intelligence applications. The trust score incorporates multiple dimensions including diversity, timeliness, accuracy, security, discoverability, and LLM-readiness, offering users quantitative insights into dataset quality. This scoring system enables organizations to identify high-quality datasets suitable for AI model training, reducing the risk of poor model performance due to inadequate data. The visualization of trust scores through intuitive interfaces allows data scientists, analysts, and other stakeholders to quickly assess and compare datasets, facilitating more informed decision-making in AI development processes.

A system for zero-trust cybersecurity access control using continuous identity verification (100), comprising: a Policy Decision and Enforcement Orchestrator (1) configured to receive an access request from a user device and enforce a session with minimal privileges; an engine for continuous identity verification (2) configured to generate a time-dependent identity trust score by continuously verifying the user identity during an active session; a device state and telemetry collector (3) configured to acquire device state parameters, runtime signals and network telemetry and to generate a state value; a module for detecting behavioral risks and anomalies (4) that is configured to create behavioral profiles and detect anomalies based on user activity patterns, contextual signals and the history of resource access; a cryptographic authentication and secure token module (5) configured to issue and update a short-lived, bound access token associated with at least the identity trust value and the state value; and an adaptive response and microsegmentation module (6) configured to dynamically adjust access permissions, network segmentation and session privileges in real time, based on an aggregated risk assessment derived from modules (2) to (4), where the system (100) continuously reassesses trustworthiness during the session and selectively allows, restricts, requires enhanced authentication or terminates the session based on the aggregated risk assessment.

The invention relates to the technical field of data processing and traceability, and discloses a traditional Chinese medicinal material full-link traceability anomaly detection method based on graph propagation and trust scoring, which comprises the following steps of: obtaining multi-source heterogeneous data of a traditional Chinese medicinal material full link, and constructing a traceability graph containing a traditional Chinese medicinal material circulation entity node and an associated edge relationship; performing parallel detection on the to-be-detected nodes in the traceability map by using a sub-model library to obtain original abnormal scores of multiple dimensions; and calculating an adaptive fusion weight according to the feature adaptation degree of the sub-model and the to-be-detected node. The adaptive fusion weight is introduced in the local anomaly refining step, so that the system can automatically improve the weight of the sub-model sensitive to the high-risk feature, and the system can dynamically recognize and preferentially acquire the optimal model for the current data feature; the problem that the traditional static fusion method is insufficient in robustness when facing diversified abnormal modes is avoided.

The invention discloses a zero-trust-based dynamic password access management method, system and device and a medium, and the method specifically comprises the steps: collecting the context information of an access request, and dynamically adjusting the intensity of multi-factor authentication based on the context information to verify the identity of a user; after the identity verification is passed, combining user attributes, resource attributes and environment attributes which are obtained in real time, inputting the user attributes, the resource attributes and the environment attributes into a user behavior portrait model to carry out real-time risk assessment and authorization calculation, generating and granting a minimum necessary permission set to the user, and obtaining an authorization decision result; dynamically generating a temporary access key according to the user role and the resource sensitivity in the authorization decision result; and continuously monitoring user access behaviors, and inputting the user access behaviors into the deep behavior analysis model for real-time trust score calculation. According to the invention, a dynamic password access management system based on zero trust is constructed, the access security and management flexibility are comprehensively improved, and various security threats are effectively resisted.

The invention discloses a data transmission security protection system for an edge computing server, which relates to the technical field of edge computing and network security, is deployed in an edge computing environment comprising a plurality of edge computing nodes, and comprises five modules: a behavior fingerprint construction module, a data processing module and a security protection module, dynamically updated behavior fingerprints are generated through a lightweight convolution auto-encoder; the dynamic trust chain management module constructs a dynamic trust chain network and generates a comprehensive trust score based on node historical interaction data; the collusion attack detection module identifies a collusion attack in combination with trust chain topology and node behavior consistency analysis; the self-adaptive isolation and recovery module isolates malicious nodes and migrates tasks to guarantee service continuity; according to the edge computing security protection method, protection is converted into active immunity from passive feature matching, collusion attacks are effectively defended, data transmission security is guaranteed, and the edge computing security protection method is suitable for edge computing security protection.

The invention discloses a privacy computing arrangement method, system and device based on dynamic trust and a medium, and relates to the technical field of data security. The method comprises the following steps: receiving a data processing request, and collecting context data including user trust, environmental risk and data sensitivity; calculating a dynamic trust score based on the context data, and dynamically selecting a target privacy calculation technology according to the score; scheduling tasks based on the selected technology and deploying a guard agent in the computing container; running state data is periodically collected through a guard agent to serve as heartbeat for verification, and if verification fails, key interruption and memory erasing and fusing tasks are triggered; after calculation is completed, packaging a result and an access control strategy into a security capsule; and in response to the capsule opening request, decrypting the rendering result in the memory after verifying that the terminal environment conforms to the strategy. According to the method, full-life-cycle dynamic control from calculation request to result use is realized, and the problem that authorization is out of control is solved.

The invention provides a drug inventory dynamic monitoring method and system based on edge calculation. The drug inventory dynamic monitoring method comprises the steps that drug parameters and environment data of all intelligent drug cabinets and warehouse areas and biological characteristics of operators are collected; determining a behavior log of the operator according to the operation action and the medicine change data, and generating a digital signature of the operator through the biological characteristics; constructing lightweight block chain nodes of the intelligent medicine cabinets and the warehouse areas, and packaging and chaining behavior logs and digital signatures through inter-node consensus of all the block chain nodes to generate an operation voucher; according to the average compliance rate and the operation voucher, carrying out trust evaluation on the compliance condition during operation to obtain a trust score; and comparing the trust score with a trust threshold, and if the trust score is lower than the trust threshold, locking the high-risk drug operation authority of the operator. By adopting the scheme of the invention, real-time credible evidence storage and dynamic evaluation can be carried out through behaviors of an operator, so that timely interception and early warning of high-risk medicine operation are realized.

The invention discloses a task processing method and device based on multiple agents and a storage medium. The method comprises the following steps: acquiring performance index data corresponding to a plurality of agents; based on the performance index data corresponding to the multiple agents, real-time trust scores corresponding to the multiple agents are determined, and the trust scores are used for indicating the reliability degree of the corresponding agents; constructing an optimization function based on the real-time trust scores, the capability matching degrees, the real-time workloads and the task security risk coefficients corresponding to the multiple agents; determining a target agent from a plurality of agents by taking the maximum function value of the optimization function as a target; and distributing the to-be-executed task to the target agent. According to the method and the device, the technical problems of low task allocation and execution accuracy and poor safety in a multi-agent system are solved.

The invention discloses a network access control method and device, equipment and a storage medium, and relates to the technical field of network security, and the method comprises the steps: receiving a network resource access request sent by a client, and determining a trust evaluation factor used for carrying out the trust evaluation of the client, and a current trust score; estimating a variation coefficient of a trust evaluation factor based on the number of accesses of the current access request and the current trust score; estimating a conflict coefficient of each trust evaluation factor based on the access frequency and the trust score, and determining an objective weight of each evaluation factor based on the conflict coefficient and the variable coefficient; fusing the subjective weight and the objective weight to obtain a comprehensive weight; and calculating the sum of the products of all the comprehensive weights and the current trust score to obtain a total trust score, if the total trust score exceeds a preset threshold, allowing access, and if the total trust score does not exceed the preset threshold, forbidding access. According to the method, the trust evaluation result can be more accurate, so that accurate network access control is realized.