406 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.

Devices and techniques are generally described for security threat mitigation for generative machine learning models. In some examples, first prompt data including first data associated with a first natural language input and a first span may be determined. An LLM may determine first plan data using the first prompt data. The first plan data may include a call to the first API. A first classifier model may determine a first trust score for the first span. A first attention score may be determined for the first span and the first action plan. Second plan data may be generated based on at least one of the first trust score and the first attention score or the second trust score and the second attention score.

A system and method are provided for routing traffic through a network to ensure load balancing and avoid untrustworthy nodes. Based on network data (e.g., telemetry data), a machine learning model generates trust scores, which are used for routing decisions by determining preferred routes from a source to a destination. The trust scores for nodes along a potential route can be combined into a cumulative trust score. The potential route with the lowest cumulative trust score (i.e., most trustworthy) is preferred, when all other factors are equal. Traffic is routed along the preferred routes, until their capacity is exceeded. Then to achieve load balancing, traffic flows are extended to the next most preferred routes (e.g., the next lowest cumulative trust score), and so forth. When traffic flows include a mix of sensitive and non-sensitive data, the sensitive data is preferentially directed along the most preferred routes.

The present disclosure provides a method and system to perform switching and handover between one or more intelligent conversational agents. The system receives a first set of data in real-time. The system collects a second set of data in real-time. The system fetches one or more queries from a plurality of users for a mega bot. The system analyses the first set of data, the second set of data and the one or more queries using one or more machine learning algorithms. The system selects a suitable intelligent conversational agent from the one or more intelligent conversational agents having a trust score above a threshold level. The system switches between the one or more intelligent conversational agents in the mega bot interacting with the plurality of users based on a plurality of aspects of corresponding query of the one or more queries and a plurality of factors.

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.

A method for providing cyber security measures in a shared network estimates in a probabilistic model, a likelihood of a cybersecurity event occurring in a system. Based on the estimated likelihood selectively applies some but not all of the cyber security measures. Based constraints of system resources available for operations a combination discretionary security measures are selected for execution. During runtime of the system, security risk of an aspect of the system is periodically evaluated causing reconfiguration of the security measures for execution based on the estimated security risk and system resources available for operations. Timed automata corresponding to security threats are assigned a risk score associated with the security threat based on one or more states of the automata. An aggregation of multiple risk scores produces a trust score for the overall system. A number of security measures may be selected and allocated based on the trust score.

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.

This disclosure describes techniques that include assessing trust in a system based on subjective factors. In one example, this disclosure describes a method that includes determining, by the computing system, a prerequisite score for a network entity in a computer network; determining, by the computing system, a subjective score for the network entity based on one or more subjective factors for the network entity, wherein each of the one or more subjective factors for the network entity corresponds to a subjective characteristic of the network entity; determining, by a computing system, a trust score for the network entity based on the prerequisite score and the subjective score; and modifying, by the computing system, operation of the computer network based on the trust score for the network entity

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.

Methods and systems are included for evaluating trust in resources of a zero-trust computing environment. A request is received from a user for access to a resource of the zero-trust environment. A trust score is calculated for the resource based on: users requesting access to the resource, hardware components used to request access to the resource, software applications used to request access to the resource, networks used to request access to the resource and data exposed by providing the requested access to the resource. The request from the user is evaluated based on policies of the zero-trust environment that govern access to the resource, where policies grant or deny access to the resource based in part on the calculated trust score of the resource.

Systems and methods control access to resources in a zero-trust computing environment. Requests for access to resources are tracked, including tracking determinations to grant or deny requested access. Based on the tracked requests, an identity tenant trust score is calculated based on users that request access to a resource and based on a number of such requests that are granted and denied. A hardware tenant trust score is calculated based on hardware that requests access to the resource and based on a number of such requests that are granted and denied. A software tenant trust score is calculated based on software applications that request access to the first resource and further and based on a number of such requests that are granted and denied. The identity tenant trust score, hardware tenant trust score and software tenant trust score are aggregated to generate a trust score for the resource.

Systems and methods are provided for controlling access to resources in a zero-trust computing environment. Requests for access to resources of the zero-trust environment are tracked, including tracking determinations to grant or deny access to resources of the environment. A trust score is calculated for a resource based on users requesting access to the resource, hardware components requesting access to the resource, software applications requesting access to the resource, networks used to request access to the resource and data exposed by providing the requested access to the resource. The trust score of the resource is adjusted upwards based on determinations to grant access to the resource and downwards based on determinations to deny access to the resource. Request for access to the resource are evaluated based on policies of the zero-trust environment that govern access to the resource, based in part on the adjusted trust score of the resource.

The invention relates to the field of Internet of Things information security, in particular to an edge computing gateway security authentication system and method based on trusted computing and asymmetric encryption, and the system comprises a master station module, an edge device module, a terminal device module and a block chain storage module. The master station module generates dynamic trust scoring data and authentication challenge data, and negotiates a session key with the terminal equipment module; the edge equipment module is responsible for acquiring and verifying terminal equipment data and transmitting encrypted service data; the terminal equipment module generates authentication response data through the trusted platform, encrypts service data and transmits the encrypted service data to the edge equipment module; the block chain storage module records trust chain data, authentication history and abnormal behaviors. Through dynamic trust scoring, block chain storage and a multi-level encryption mechanism, the problems of insufficient dynamism and relatively low security in the prior art are effectively solved, and the authentication accuracy and system reliability of the Internet of Things edge computing gateway are improved.

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.

System and methods provide a visualization for use in configuring controlled access to resources in a zero-trust computing environment. A request from a user for access to a resource of the zero-trust environment is received. When visualization of the request is enabled, a visualization of the request is initiated. An identity tenant trust score for the resource is calculated based on a record of users requesting access to the first resource. A hardware tenant trust score is calculated based on a record of hardware requesting access to the resource. A software tenant trust score is calculated based on a record of software requesting access to the resource. The visualization that is generated displays the identity tenant trust score, the hardware tenant trust score and the software tenant trust score associated with the request.

Systems and methods provide controlled access to resources in a zero-trust computing environment. Requests for access to resources of the zero-trust environment are tracked, including identifying policies evaluated in response to requests for access to resources. Based on the tracked requests, policies are identified that were satisfied in granting access to resources and that were not satisfied in denying access to resources. Request for access to resources are evaluated based on a trust score for a resource that is calculated based on users requesting access to the resource, hardware requesting access to the resource, software applications requesting access to the resource, networks used to request access to the resource and data exposed by providing the requested access to the resource. The trust score for the resource is adjusted upwards based on the policies satisfied in granting requested access, and adjusted downwards based on policies not satisfied in denying requested access.

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.

A method, computer system, and computer program product are provided for selectively deploying code modules for issue identification and remediation tasks. A plurality of code modules is obtained, wherein each code module includes instructions for issue identification and remediation. A trust score for each code module of the plurality of code modules is determined, wherein the trust score includes a first trust score component for issue identification and a second trust score component for issue remediation, and wherein the trust score is based on a source of each code module selected from a group of a human-generated source and an artificial intelligence model-generated source. A particular code module of the plurality of code modules is deployed based on the trust score of the particular code module satisfying a threshold value. The trust score for the particular code module is updated based on results of deploying the particular code module.

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 discloses a multi-level security authentication method and system, and relates to the technical field of the Internet of Things, and the method comprises the steps: enabling a new node to verify the anti-quantum signature legality and behavior feature trust score of an identity certificate through a neighbor node according to a dynamic authentication path, and outputting a device layer authentication result; according to the equipment layer authentication result, selecting a verification node in an authentication path to carry out identity recheck and trust score weighted updating on a new node to obtain a neighbor layer authentication result and a whole network trust score; based on the neighbor layer authentication result, the core node verifies the whole network trust score and anti-quantum signature legality of the new node through the dynamic authentication atlas, and performs updating and anomaly detection on the dynamic atlas; through anti-quantum signature verification and behavior feature trust scoring, the ability of new node authentication to resist quantum computing attack and malicious node disguise is enhanced.

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.