676 results about "Security validation" patented technology

The invention relates to the technical field of factory management. The intelligent factory management method based on digital twinning comprises the following steps: performing dynamic identification and analysis processing on a multi-source heterogeneous communication protocol of factory equipment to obtain a standardized data stream; constructing a three-dimensional digital twinborn model comprising an equipment state and a technological process; generating a virtual control instruction according to an analog control strategy in the three-dimensional digital twin model; sandbox security verification processing is carried out on the virtual control instruction, and a compliance instruction set is screened out; converting the compliance instruction set into a protocol format supported by the target equipment, generating a reverse control instruction and issuing the reverse control instruction to the physical equipment; performing closed-loop feedback optimization processing according to the difference between the execution result of the physical equipment and the prediction result of the three-dimensional digital twinborn model, so as to solve the problem that the digital twinborn model lacks real-time sensor data injection and dynamic behavior constraint embedding, ensure that the model can reflect the real state of the equipment, and improve the real-time performance of the equipment. And the reliability and the execution security of the virtual control instruction are improved.

The invention discloses a CAPEC vulnerability management system based on a large language model, and belongs to the technical field of network security. The system comprises three modules: a vulnerability-CAPEC dynamic mapping module jointly encodes vulnerability description and code context through a bimodal large language model, accurately associates vulnerability logic with a CAPEC attack mode in combination with comparative learning and knowledge graph construction, and breaks through semantic limitation of traditional rule matching; the adversarial repair code generation module is used for generating high-robustness repair codes through adversarial training and syntax tree verification by fusing CAPEC relieving suggestions and code features on the basis of the association result, so that the secondary vulnerability risk is remarkably reduced; and the full-process automatic verification and DevOps integration module performs multi-dimensional security verification such as symbolic execution, fuzzy testing and the like on the generated repair code, and deeply integrates a development tool chain to realize real-time pushing and closed-loop management of a repair scheme.

A value chain network control tower system comprises a processor and memory configured to execute a know your model system that manages the complete lifecycle of Al models in enterprise environments. The know your model system performs model intake and registration actions including model documentation collection, registration procedures, metadata collection, input / output interface standardization, legal and licensing validation checks, and security validation. The system conducts comprehensive model evaluation and risk assessment actions by analyzing foundational properties, task performance, safety and risk management, alignment and compliance characteristics, operational metrics, and tooling transparency capabilities. The know your model system executes model deployment actions through automated environment validation, predeployment approval processes, and controlled production deployment with continuous monitoring.

The invention discloses an edge computing assisted wireless communication system, which relates to the technical field of edge computing, and comprises an edge computing node cluster which is deployed at a base station and an access point and provides distributed computing and storage capability; the resource sensing module is used for monitoring computing resources, communication resources and task queue states of the edge nodes in real time; the dynamic scheduling controller is used for dynamically deciding a task unloading path based on reinforcement learning and game theory algorithms, wherein the task unloading path comprises local processing, edge node cooperative processing, cloud unloading and global time delay and energy consumption optimization; the cross-layer communication interface supports multi-protocol fusion of 5G NR and Wi-Fi 6, and realizes low-delay data interaction between edge nodes; and the security verification module is used for ensuring credibility and integrity of data between edge nodes through a lightweight block chain and a TEE (Trusted Execution Environment). According to the invention, through a hybrid decision framework of reinforcement learning and game theory, the problem that a single algorithm in a traditional scheme cannot give consideration to dynamic environment adaptability and multi-node benefit coordination is solved.

The invention belongs to the field of artificial intelligence, and discloses a scheduling strategy selection large model training method based on reinforcement learning, and the method comprises the steps: constructing a virtualized cluster environment; generating training data; carrying out large model base pre-training; learning environment interaction is reinforced; performing priority experience playback training; performing near-end strategy optimization; carrying out multi-expert strategy distillation; policy security verification is carried out; carrying out progressive online deployment; and feedback driving continuous optimization is carried out. According to the method, the resource topology, the task portrait and the dynamic load feature are decomposed to the special agent, and structured semantic tags such as a high-bandwidth demand task and a critical overheat node are generated, so that the heterogeneous resource state recognition accuracy is improved by 40%, and the multi-dimensional feature fusion efficiency is improved by 35%.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an intelligent decision-making method, device, equipment and medium based on security boundary constraint. Limiting a security boundary according to a macroscopic decision direction, retrieving updated knowledge from the dynamic knowledge graph and generating a multi-modal decision priority analysis result fused with the updated knowledge, determining a target decision in the security boundary based on the multi-modal decision priority analysis result, and performing security verification by comparing the target decision with the security boundary. And generating a target decision that the security verification is passed. According to the method, multi-modal decision priority analysis is carried out by combining unified feature representation and updated knowledge, and target decisions are screened and verified in a security boundary, so that effective fusion and dynamic knowledge enhancement of multi-source information are realized, and the accuracy and security of decisions in a complex environment are improved.

The invention provides a security verification method for a large model service operation environment, a medium, equipment and a product, and relates to the technical field of computers, large model services are deployed in a container group created in a virtual machine, and the virtual machine is deployed in a hardware-based trusted execution environment, so that the security verification of the large model service operation environment is realized. The method comprises the following steps: acquiring environment measurement corresponding to a running environment of a large model service through a remote attestation service deployed in a hardware-based trusted execution environment, the environment measurement at least comprising a first measurement corresponding to the trusted execution environment in which a virtual machine is located and a container measurement under a container stack dimension corresponding to a container group; the remote attestation service generates a remote attestation report according to the environmental metrics to enable a user to validate the security of the operating environment through the remote attestation report. According to the invention, not only can the attack from the IaaS layer be shielded, but also the credibility verification under the dimension of the virtual machine and the credibility verification under the dimension of the container stack can be provided, so that the credibility of the user to the service provided by the service provider is improved.

The invention relates to the technical field of quantum key distribution, and discloses a communication protocol security verification method based on quantum key distribution, which comprises the following steps of: establishing a quantum communication channel framework, loading protocol parameters to generate an initial security verification model, configuring a security state transition rule, and performing cross-layer data fusion in combination with quantum entity parameters; and obtaining a whole-process safety tracking model. And executing security path deduction through the model, generating a key exchange trajectory chain, constructing security trajectory simulation data, establishing a quantum conflict resolution mechanism, training and generating a security policy optimization model, and outputting security governance parameters. Security increment simulation information is generated based on security governance parameters and the like, a multi-target decision model is constructed in combination with protocol evolution constraint conditions, optimal security path parameters are adjusted and generated, a security verification strategy is verified and updated through a real-time quantum mapping network, and a whole-process security matching target is achieved. According to the method, the comprehensiveness, the accuracy and the dynamic adaptability of quantum communication protocol security verification are effectively improved.

The present disclosure describes apparatuses and methods for implementing secure verification of a physical unclonable function (PUF). In various aspects, a PUF verifier generates a PUF reagent value by obtaining a key from a PUF and a message value useful for PUF verification. The PUF verifier computes a digest value of the PUF key and the message value and selects a portion of the hash digest as a PUF reagent value. The PUF verifier writes the PUF reagent value to a non-volatile memory to enable subsequent verification of the PUF. The PUF verifier may also generate error-correction code information for the PUF reagent value and write this information to the non-volatile memory to enable error correction. Upon device hardware reset, the PUF verifier can securely verify PUF operation by generating a PUF key hash digest and comparing the hash digest with the PUF reagent value without exposing the PUF key.

The invention discloses a streaming knowledge injection and adversarial self-optimization large language model training method and system, and the method comprises the steps: collecting the newest knowledge of an authoritative information source in real time, converting the newest knowledge into a structured constraint rule through a semantic analyzer, and dynamically updating a knowledge base; based on the updated knowledge base, adopting a PPO algorithm to optimize a generator, actively constructing a high-risk adversarial sample, and forcing the model to expose security vulnerabilities; after user input and adversarial samples are input into the model, total loss is calculated through constraint detection, gradient updating is blocked if the total loss exceeds a threshold value, and otherwise, a multi-level safety verification stage is started; and finally, fusing a security verification result and the adversarial loss, updating a total loss function and cooperatively adjusting model parameters to form a continuous self-optimization training cycle. According to the method, compliance is guaranteed through streaming knowledge injection, vulnerabilities are actively mined in combination with adversarial training, verification precision is improved by means of multi-level detection and dynamic threshold adjustment, and safety and reliability of a large language model in a complex scene are enhanced.

The invention relates to the technical field of cloud security verification, in particular to a cloud security verification method and system, electronic equipment and a computer readable storage medium. The method comprises the steps of generating and isolating an AKSK which is an access key pair for identity verification, simulating an attack behavior in combination with the AKSK, and performing security capability verification based on the simulated attack behavior. According to the method, the AKSK is generated to simulate the attack in the real cloud environment, it is ensured that the test scene highly restores the real attack path, and the technical problem that the traditional security test mainly depends on static policy analysis (such as IAM policy grammar check) and artificial penetration test and cannot simulate the complete attack path after AKSK leakage in the real attack chain is solved.

The invention discloses an automatic driving safety key simulation scene generation method based on adversarial generation and co-evolution. The method comprises the following steps: receiving a basic traffic scene described by a natural language, generating an antagonistic element scene containing security threats by using a large language model in combination with a traffic safety knowledge base, and analyzing the antagonistic element scene into an executable scene script; constructing a multi-agent confrontation collaboration diagram based on the meta-scene, and recognizing a key background vehicle through a cross-timing attention mechanism in combination with a time mask and time decay mechanism; and performing disturbance optimization on the key background vehicle trajectory to generate an automatic driving test scene. According to the method, a scientific and systematic solution with engineering operability is provided for safety verification of the automatic driving system when the automatic driving system faces real traffic challenges such as multi-source intervention and dynamic collaborative threat, and the method has wide adaptation capability and important industrial popularization value.

The invention relates to the technical field of power plant data processing, in particular to a virtual power plant integrated management system, which comprises a data acquisition module for acquiring voltage fluctuation characteristics and power climbing rate parameters of energy equipment in real time; the equipment fingerprint database construction module generates an equipment fingerprint code comprising a dynamic response time-lag coefficient and an adjustment margin quantized value; the dynamic clustering module is used for dividing simulation sub-models of regional topology constraints based on the geographic position of the equipment and a power regulation threshold value; the multi-time scale prediction module is used for generating source load prediction data by fusing meteorological parameters through a space-time diagram convolutional network; the digital twin simulation module outputs an energy storage charging and discharging threshold value and a demand response priority strategy; and the cross-domain collaboration module adopts a double-chain block chain architecture to realize security verification and data integrity binding of federated learning feature parameters. According to the invention, through equipment characteristic digital modeling, regional co-simulation and multi-energy flow coupling regulation and control, the resource aggregation precision and response real-time performance in a new energy strong fluctuation scene are improved.

The invention relates to the technical field of data disaster recovery, and provides a disaster recovery drill and disaster switching method and system, and the method comprises the steps: automatically recognizing multi-source heterogeneous database assets through a rule engine and a machine learning algorithm, and constructing a disaster recovery topological graph; a disaster recovery strategy is generated and optimized by adopting an intelligent algorithm based on the disaster recovery topological graph, and the disaster recovery strategy is dynamically adjusted in combination with real-time service requirements; fusing the historical disaster recovery data and the real-time monitoring data to reconstruct a disaster recovery strategy; executing data are collected in real time in the drilling process, and a disaster recovery strategy is optimized through a closed-loop feedback mechanism; monitoring a whole process state through a visual interface and displaying key indexes; based on the multi-dimensional evaluation model, quantitatively analyzing the drilling result, and determining a target disaster recovery strategy; and a security verification mechanism is adopted to authorize the key operation, a disaster recovery scene scheme is matched according to the fault type, and an automatic execution unit is controlled to complete disaster switching based on the target disaster recovery strategy. According to the invention, the disaster recovery switching accuracy in a complex scene is improved.

The invention provides a data access security verification method, which comprises the following steps of: acquiring multi-dimensional feature data of an access request, and cleaning and formatting the feature data to obtain a standardized feature data set; if the abnormal mode is detected, marking the abnormal mode as a suspected counterfeit request record; for the suspected counterfeit request record, collecting state information of the suspected counterfeit request record in each node of a propagation path to obtain a state information set; according to the state information set, analyzing the state consistency of the suspected counterfeiting request record in each node, and determining whether a counterfeiting behavior suspicion exists or not; if it is determined that the forgery behavior is suspected, generating a corresponding exception report; according to the exception report, determining a specific node position where an exception occurs, acquiring propagation trajectory data of related nodes, and generating a trajectory log file; and according to the final detection report, updating a feature database and optimizing the classification model.

The invention provides a personnel safety dynamic early warning method and system for distribution network line operation, and relates to the technical field of intelligent power grid management, and the method comprises the steps: calling an operation work ticket which comprises a work task, operation personnel information, safety measure requirements, grounding wire installation information, and a regulation and control instruction; text semantic structuring preprocessing is executed, and three-dimensional security features are extracted; security statement extraction is carried out, and a structured task template is established; when the positioning information of the target operating personnel is identified to accord with the area range of the target area, activating the target area; establishing a time sequence working data set based on ground wire intelligent sensing and operation tool intelligent sensing; and performing security verification, and generating a security dynamic early warning. According to the invention, the technical problem that the response speed and accuracy of safety early warning are reduced because the safety assessment of the operation task is not accurate enough due to the lack of an intelligent information extraction and task verification mechanism in the distribution network line operation in the prior art is solved.

A security verification method, apparatus, medium, device and product for a large model service relate to the technical field of computers, receive an encryption service request, acquire a private key of a client from a key management service running in a trusted execution environment through an encryption and decryption service running in the trusted execution environment, decrypt the encryption service request based on the private key, and verify the security of the encryption service request. The method comprises the following steps: decrypting a service request of a user, sending the decrypted service request to a large model service to obtain a reasoning result of the large model service, encrypting the reasoning result through a public key of a client, and returning the encrypted reasoning result to the client, so that the service request of the user can be visible in a plaintext in a trusted execution environment; the security of the user data is greatly ensured, and the problem of user data leakage can be avoided through the public and private key pair of the user dimension. In addition, the key management service and the encryption and decryption service both run in the trusted execution environment, so that attacks from an IaaS layer can be shielded.

The invention discloses a manufacturing task autonomous negotiation and execution method based on a large language model agent, and the method comprises the steps: constructing a production scheduling agent, an equipment management agent, a material distribution agent and a quality control agent, analyzing a natural language task instruction through the production scheduling agent, and decomposing the natural language task instruction into subtasks; each agent calculates a utility value based on the load rate, the resource matching degree, the estimated completion time and the historical success rate, and performs structured negotiation to achieve a task allocation consensus; a prediction-check-rollback architecture is adopted to generate an action instruction sequence, the sequence is compiled into a time Petri network transition sequence, and reachability verification is carried out based on hard security constraints; production environment data is collected in real time to trigger anomaly detection and re-negotiation, and a formalized security verification and causal anti-factual reasoning parameter updating mechanism is introduced. According to the method, unstructured instruction understanding, autonomous task planning, multi-agent collaborative decision and closed-loop optimization are realized, and the problems of real-time performance, safety and interpretability of a large language model in manufacturing control are solved.

The invention provides a power network data security encryption method and system based on deep learning, and the method comprises the steps: obtaining an encryption demand data set of a target power network, carrying out the multi-level feature extraction processing of the encryption demand data set, and obtaining the spatial and temporal distribution features and correlation features of the encryption demand data; and based on a preset deep learning strategy network, performing dynamic encryption strategy matching processing on the spatial-temporal distribution characteristics and the associated characteristics, generating a dynamic encryption strategy of the encrypted data sequence, performing encryption operation on the encrypted data sequence according to the dynamic encryption strategy, generating an encrypted power network data set, and sending the encrypted power network data set to a server. And encrypting the power network data set, transmitting the encrypted power network data set to a data storage node of a target power network, performing security verification processing on the encrypted power network data set, generating an encryption strategy optimization instruction, and triggering encryption strategy adaptive updating. According to the invention, the real-time security of power data transmission is ensured.

The invention discloses a security verification method based on an ACME protocol, a security verification system based on the ACME protocol, a computer program product, a CA server, ACME client equipment and a computer readable storage medium. The method comprises the steps that a to-be-verified ACME client sends a certificate request to the CA server based on a target ACME account; if the CA server does not query an effective client authorization record of the to-be-verified ACME client based on the to-be-verified identification information, selecting at least one target domain name from the at least one verified domain name, and re-verifying the ownership of the to-be-verified ACME client to the at least one target domain name; and if the verification of the ownership is passed, the CA server generates an effective client authorization record for the to-be-verified ACME client based on the to-be-verified identification information, and issues a certificate for the to-be-verified ACME client based on the generated effective client authorization record and the effective domain name authorization record of the at least one verified domain name. The security of certificate issuing can be improved.

A method of protecting a model, which relates to a field of computer, a field of artificial intelligence, and may be applied to an AI model protection scenarios. The method includes: generating a WASM file for providing a runtime environment for a target model, the WASM file containing a corresponding model inference algorithm and security verification algorithm, wherein the security verification algorithm is configured to perform at least one security verification operation to protect the target model, the at least one security verification operation is selected from: a verification of a host environment; a verification of an integrity of the WASM file; a verification of an integrity of the model file generated corresponding to an original model file of the target model; a timeout verification of a specified inference process during a model inference process; or a timeout verification of an entire inference process during the model inference process.

The invention relates to the technical field of electric power communication security, in particular to a power grid data secure transmission system and method based on quantum cryptography and a medium, comprising: a power grid data access module extracts identities of a power grid data request end and a target response end; the quantum key distribution module obtains an original shared key pre-stored by the two communication parties, and generates a quantum key according to the one-time session password and the original shared key; the communication security management module analyzes the shared key information from the quantum key received by the target response end to obtain analyzed shared key information, and performs communication security verification on the original shared key and the analyzed shared key information; and the power grid security communication module encrypts the to-be-transmitted power grid data when the communication security verification result is that the verification is passed, and transmits the power grid encrypted data to the target response end. Through cooperation mechanisms such as quantum key dynamic distribution and security verification, security protection of power grid data transmission is realized, and power grid data communication security is improved.

The invention belongs to the technical field of power network inspection, and particularly relates to a power network security inspection system which comprises a multi-source sensing module, an edge calculation module, a zero-trust security verification module, a core analysis module, an execution module and a feedback optimization module. Through an edge-cloud collaborative architecture, sensors, network flow and video data are fused in real time, the detection efficiency is improved, a physical equipment state and zero-trust dynamic authority control are introduced, 'physical-digital 'dual verification is realized, real threats and false alarms are marked, algorithm parameter adjustment is guided, and a feedback optimization module continuously reduces the false alarm rate through a closed loop mechanism; full-dimension safety protection of the power network is realized through modular design, dynamic branch processing and a closed-loop optimization mechanism; the threat level and the risk assessment result are dynamically adjusted through the environmental risk correction factor, the risk misjudgment rate is reduced, and the fault prediction accuracy in extreme weather is improved.

The invention relates to the technical field of communication transmission, and particularly discloses an all-in-one machine encryption communication transmission method and system based on a dynamic strategy, and the method comprises the steps: collecting hardware state parameters, network environment parameters and communication content characteristics of an all-in-one machine and a target communication opposite end in real time; calculating a risk index of the current communication process based on the network environment parameters of the all-in-one machine and the target communication opposite terminal; matching an encryption algorithm sequence, a key updating frequency and a transmission protocol according to hardware state parameters, risk indexes and communication content characteristics of the all-in-one machine and a target communication opposite end, performing segmented encryption and multi-channel parallel transmission on communication data of the all-in-one machine, and performing security key distribution in combination with a distributed key management mechanism to obtain a security key; performing encrypted communication security verification based on the security key to obtain communication verification data; the security of the communication process of the all-in-one machine is improved, and the risks of data leakage and man-in-the-middle attack are prevented.

The invention relates to the technical field of medical RFQ accelerators, and particularly discloses a medical RFQ accelerator multi-safety redundancy linkage protection system and method, and the method comprises the steps: collecting the working state image data of an accelerator, and generating a three-dimensional fusion feature map containing heat distribution, electromagnetic radiation intensity and mechanical vibration; a multi-dimensional safety index evaluation vector is output by using a multi-mode Transform model; constructing a dynamic safety boundary prediction model, and generating a real-time safety threshold curve of each subsystem according to the evaluation vector; performing parameter optimization by adopting a federated learning framework; constructing a non-tampering security event traceability chain by using a block chain; constructing a multi-stage protection strategy of the medical RFQ accelerator; and verifying the redundancy control channel through a cross-layer security verification mechanism, and finally executing a protection action. According to the invention, multiple safety redundancy protection of the medical RFQ accelerator is realized, the risk of false shutdown is reduced, and the requirement of high reliability of clinical radiotherapy is met.

The invention discloses an off-line virtual laboratory and digital intelligent application iteration method and system based on an intelligent agent. The method comprises the following steps: constructing an off-line virtual laboratory consistent with a site in a non-production environment, and keeping the consistency of data and configuration through a synchronization mechanism; performing automatic scheduling on working condition arrangement, simulation deduction and rule checking by utilizing an intelligent agent; carrying out process deduction by adopting a mode of combining data driving and similar working condition retrieval, and completing index calculation and safety verification; and full-life-cycle management is carried out on the digital intelligent application, and cross-site rapid copying is realized through template packaging. According to the system, low-risk and high-efficiency iterative verification and popularization of digital intelligent application can be realized on the premise of not interfering field production, and the safety and engineering level of industrial intelligence landing are remarkably improved.

The invention discloses a non-inductive detection system for DNS server security based on browser search, and belongs to the technical field of network security detection. The system is deployed in a browser, the browser serves as a security verification node and a network sensing terminal, and the system comprises a receiving module, a legality approval and credibility screening module, a multi-layer verification module and a health state monitoring and dynamic risk assessment module. The multi-layer verification module comprises an authenticity verification module, a multi-path request and consistency comparison module and a comparison analysis and redundancy screening module; according to the system disclosed by the invention, when a user uses a browser to input a keyword to access a network, the security protection of the conversion process from the keyword to the URL is realized, and the non-inductive detection of the local DNS server is realized. According to the system, through multi-layer verification and consistency comparison, more potential attacks and data tampering risks can be identified, and a full-link security protection system from a user side to a network infrastructure is constructed.

The invention provides an agro-ecological member data security management method and system, which is applied to the technical field of data security management, and is characterized in that client data abstracts, server data abstract comparison, chain structure storage, multiple independent subject confirmation vouchers and multiple security verification mechanisms are introduced; the method effectively solves the problem of integrity and non-tampering verification of the agro-ecological member data in a multi-subject, long-period and dynamic updating environment, ensures the truth and credibility of the data, and can effectively solve the problem of integrity and non-tampering verification of the agro-ecological member data in the multi-subject, long-period and dynamic updating environment. And the truth and credibility of the data are ensured.

A safety verification method for a distributed large model service, a medium, a device and a product relate to the technical field of computers.The safety verification method comprises the following steps: acquiring a first remote proof report corresponding to each container group included in the distributed large model service and acquiring a second remote proof report corresponding to each node included in the distributed large model service; and then, according to the first remote proof report and the second remote proof report, obtaining a third remote proof report corresponding to the distributed large model service, so that a user can carry out security verification on the operating environment of the distributed large model service through the third remote proof report. According to the method, whether the containerized large model instance runs in a trusted environment or not can be effectively proved, and remote proof reports of all large model instances and nodes of the distributed large model service can be contained, so that a user can perform all-around security verification on the distributed large model service through the third remote proof report; and the trust degree of the user on the service provided by the service provider is greatly improved.

The invention relates to the technical field of e-commerce, and discloses an internet-based e-commerce account risk assessment system, which comprises a distributed data acquisition module for collecting account data through a distributed data acquisition network; the intelligent data processing module is used for processing the collected data by using a deep learning algorithm; the dynamic risk assessment module is used for carrying out risk assessment on the user account through an integrated risk assessment model; the enhanced security verification module is used for performing enhanced verification on the identity of the user when the risk level reaches a preset threshold value; the real-time behavior monitoring module is used for monitoring the real-time behavior of the account and discovering and early warning the abnormal behavior of the account; and the feedback driving optimization module is used for collecting feedback information of the user and continuously optimizing the evaluation model. According to the method, the accuracy, the safety and the real-time performance of electronic commerce account risk assessment are remarkably improved, the misjudgment rate is reduced, and the user credibility is enhanced.