258 results about "Adaptive security" patented technology

The invention relates to the technical field of vulnerability management, and discloses a vulnerability management method and system based on an adaptive security platform. The method comprises the following steps: constructing an asset information database based on all IT assets in an organization network environment, and calculating a time sensitivity parameter set; based on the asset information database and the time sensitivity parameter set, executing aperiodic time stratification vulnerability data collection and dynamic self-shaping processing to obtain a standardized structure vulnerability data set; performing vulnerability utilization chain topology analysis through the bidirectional adversarial neural network model to generate a vulnerability risk score and a vulnerability association relationship graph; and generating vulnerability risk decision information according to the vulnerability risk score and the vulnerability association relationship graph, and performing constraint perception adaptive repair arrangement based on the vulnerability risk decision information to generate an optimal vulnerability repair scheme. The vulnerability discovery process is more efficient and accurate, the repair success rate is improved, the service interruption time is shortened, and continuous optimization of the repair process is achieved.

The invention relates to the technical field of path planning, and discloses a path planning and control method and system for a container port automatic driving vehicle. The method comprises the following steps: acquiring environment data through a multi-source sensor, and obtaining a harbor district digital twinborn model through space-time registration fusion; a three-layer progressive planner is used for optimizing and generating a main and standby path scheme based on the model; predicting a dynamic target trajectory by using a time sequence attention network to obtain situation assessment; processing the evaluation data through a distributed predictive control algorithm to generate a control instruction; and monitoring a motion state through adaptive security constraints to form a security policy, and continuously optimizing by using deep reinforcement learning. According to the method, safe and reliable operation of the automatic driving vehicle is realized in a complex and changeable harbor district environment, and particularly, accurate path planning and control decisions can be made in the face of dynamic obstacles.

The invention provides a method and a system for detecting and defending cross-domain threats of a power system. The method comprises the following steps: after carrying out anomaly identification on operation monitoring data of a physical domain node in a target power grid region to obtain an anomaly identification result and carrying out denial of service attack identification according to network flow data of an information domain node to obtain an attack identification result, carrying out abnormal event association analysis on the anomaly identification result and the attack identification result to obtain an attack cross-domain anomaly identification result; according to key nodes and key risk propagation paths in a cross-domain attack chain generated based on a graph theory algorithm, generating an attack tracing atlas, and according to vulnerability information of the key nodes in the atlas, obtaining a corresponding power system topological graph and a corresponding communication network topological graph; and iteratively generating an active defense rule based on a game theory algorithm and a reinforcement learning algorithm, and issuing the active defense rule to the node. According to the method, the cross-domain attack risk is accurately perceived in real time and adaptive security defense is executed through cross-domain abnormal event association analysis, so that the comprehensiveness and reliability of security protection of the power system are improved.

The invention belongs to the field of network security, and relates to a trusted management and control network platform construction method and device, electronic equipment and a storage medium, the method comprises the following steps: constructing a basic security architecture and a trusted base, the basic security architecture being used for providing a unified root of trust and a management core for collaborative operation of upper security components; based on the basic security architecture and the trusted base, implementing multi-dimensional trusted verification and dynamic access control; an intelligent boundary protection and depth detection system is deployed and is used for constructing an intelligent, three-dimensional and self-adaptive security defense line at the boundary of each region of the network, and various known and unknown threats can be identified and blocked; constructing a unified secure communication tunnel and a cross-domain transmission guarantee; defining and realizing a standardized security function interface and a collaboration protocol; and a continuous trust evaluation and automatic operation management and control closed loop is established. The overall security is enhanced, the secure transmission of data is ensured, the compatibility and collaboration of the system are improved, and the security operation intelligence is realized.

The invention discloses a medical equipment remote control system based on Internet of Things data, relates to the technical field of equipment remote control, and realizes efficient safety protection of medical equipment remote control through four links of multi-source data acquisition and behavior baseline construction, real-time anomaly detection, risk grading evaluation and linkage processing, and adaptive safety strategy execution. Firstly, an operation log is collected and cleaned, a multi-dimensional label is added, and a baseline model M is constructed to judge the deviation degree; then, deviation degree calculation is carried out on the instructions in real time, suspicious instructions are screened, and a structured suspicious instruction set is formed; in the risk grading stage, performing multi-factor grading on suspicious instructions and screening medium-high risk objects; and finally, according to the risk vector, calculating the priority of a strategy and matching a predefined interval, implementing security protection through measures such as multi-factor authentication, secondary approval or temporary freezing, and continuously writing back and updating a disposal result to form a closed loop to improve the overall security management and control level.

The invention discloses an image encryption system and method in an intelligent power grid environment, and aims to solve the problem that an encryption strategy is static and lacks self-adaptive capability in the prior art. The image encryption system comprises terminal equipment, edge equipment and central equipment, and a dynamic key generation unit on the terminal equipment generates a dynamic key by cooperatively utilizing a physical unclonable feature (PUF) of the equipment and a real-time load of a power grid; the hierarchical encryption unit performs encryption of different intensities based on image content complexity. A topology sensing transmission module of the system analyzes power grid topology by using a graph neural network (GNN) and dynamically optimizes transmission. In addition, the image encryption system further integrates low-density parity check code encoding and decoding and a self-adaptive decryption strategy so as to enhance the anti-interference capability. According to the invention, by constructing a sensing, decision-making and execution integrated adaptive security system, the security, high efficiency and robustness of smart grid image data transmission are significantly improved.

The invention provides a cross-park enterprise data collaborative analysis method based on federated learning, and relates to the technical field of distributed machine learning and data security, and the method comprises the steps that a central server distributes an initial global model and configuration parameters to each park node; the nodes execute local data feature alignment to generate standardized feature vectors; calculating dynamic collaborative factors of local data and global distribution; adjusting a training strategy based on the collaborative factors and updating model parameters; collecting model updating through an encrypted channel, and screening effective updating by adopting a dynamic aggregation offset threshold value; performing weighted aggregation to generate a new global model; and terminating the process when the cross-park convergence condition is met or the maximum round is reached. According to the method, heterogeneous data differences are eliminated through a dynamic feature alignment mechanism, dual-channel collaborative evaluation and adaptive security protection are combined, multi-park collaborative modeling efficiency and robustness are remarkably improved on the premise of guaranteeing data sovereignty, and the problems of feature space splitting, weak attack protection and node contribution imbalance are solved.

The invention discloses a self-adaptive safety control method of a nonlinear MIMO system under intermittent DoS attack, which comprises the following steps: establishing a nonlinear MIMO system model suffered from intermittent DoS attack, and the system model is in a strict feedback form and comprises an unknown nonlinear function and bounded external disturbance; the DoS attack causes periodic interruption of a communication link from a sensor to a controller, so that actual measurement data is unavailable in an attack activation period; designing a switching type adaptive state estimator; coordinate transformation is carried out by introducing a filter, and a system error is decomposed into a tracking error and a dynamic surface filtering error; a Lyapunov function is constructed; performing stability analysis in stages; designing an adaptive law to update unknown parameter estimation values of the FLS; and a final event triggering controller is designed, and the event triggering controller is combined with a virtual controller to track an error compensation term and an FLS approximation term. The problem that the state cannot be measured is solved.

The invention discloses a real-time multi-dimensional sensitivity evaluation and self-adaptive safety prevention and control method and system for natural resource geographic information. The method comprises the following steps: acquiring real-time natural resource geographic information data; obtaining a preset behavior graph model; updating a preset behavior graph model according to the real-time natural resource geographic information data so as to obtain an updated behavior graph model; acquiring an updated node risk vector according to the updated behavior graph model; obtaining a trained Bayesian risk prediction model; inputting the updated node risk vector into a trained Bayesian risk prediction model so as to obtain a real-time abnormal behavior identification result; and generating a personalized prevention and control strategy scheme according to the real-time abnormal behavior recognition result. According to the method, intelligent identification, dynamic evaluation and active protection of natural resource geographic information in a full life cycle are realized by constructing a multi-dimensional sensitivity quantitative model, a dynamic risk perception mechanism based on a graph structure and a safety prevention and control strategy capable of being updated in real time.

The invention belongs to the technical field of intelligent electric power internet of things, and particularly relates to an unmanned aerial vehicle heterogeneous terminal self-adaptive security access method for electric power edge computing, which comprises the following specific steps: terminal registration and credible anchoring: binding a hardware-level identity, collecting a multi-mode fingerprint and constructing a security behavior portrait; self-adaptive access authentication is carried out; dynamic behavior monitoring and baseline adjustment: establishing an identity authentication and management system, synchronously carrying out multi-dimensional real-time monitoring, carrying out linkage treatment on abnormal behaviors, and adopting a baseline evolution mechanism to avoid misjudgment; the multi-dimensional real-time monitoring comprises terminal service trend monitoring and flow situation analysis; and performing safe closed loop and cooperative response. According to the invention, unified risk detection of the unmanned aerial vehicle can be realized, real-time monitoring of vulnerabilities, access authority and operation states of the unmanned aerial vehicle is carried out, a dynamic behavior monitoring model is constructed in combination with unmanned aerial vehicle terminal information, business data, environment data and the like, and real-time monitoring and early warning of the unmanned aerial vehicle terminal are realized.

Systems, computer program products, and methods are described herein for AI-based adaptive security parameter calculation in license key generation with quantum-resistant protections. The present disclosure is configured to utilize advanced cryptographic techniques, including elliptic curve cryptography and SHA-3 hashing, to produce secure digital signatures for license keys. An AI-driven engine analyzes historical security data to adaptively adjust key generation parameters, ensuring enhanced protection against current and emerging threats. This adaptive approach allows for continuous learning and improvement of security measures, making the system robust against both classical and quantum cryptographic attacks. The disclosed methods integrate the generation, assessment, and validation of keys into a streamlined process that promotes high security while maintaining efficiency.

A network security script arrangement method based on LLM enhanced RL comprises the following steps: 1) LLM dynamically expands a to-be-selected strategy atom set based on a security threat scene: firstly, performing semantic similarity retrieval on input security threat intelligence by using a vector library, and matching an optimal defense strategy atom in a knowledge library; the context understanding capability of the LLM is then utilized to implement semantic understanding and reasoning on the candidate atom set, generating new policy atoms complementary to the defense. 2) RL refers to LLM atomic action preference to optimize a script generation strategy, and self-adaptive security arrangement of a complex network environment is realized: firstly, a security script graph model based on strategy atoms is adopted, and multiple constraints corresponding to nodes and edges in the graph model are abstracted on the basis of security arrangement definition; and then, a composite reward function is adopted to model a linear programming function for the multi-constrained security script arrangement problem, so that multi-dimensional optimization of defense efficiency and execution cost is realized.

The invention provides a self-adaptive safety self-healing system based on artificial intelligence, and aims to improve the safety protection capability and the self-repairing capability of an information system. The system comprises a client Agent module, a secure transmission module, a data analysis and anomaly detection module, a self-healing strategy management module, an execution module and a monitoring display module. The client Agent module collects data such as system logs, network flow and resource use, and predicts resource changes through a long short-term memory (LSTM) network model. And the secure transmission module adopts a TLS protocol and a block chain technology to ensure the integrity and security of the data. And the data analysis and anomaly detection module identifies threat behaviors through an Isolation Forest algorithm and a deep learning technology, and automatically generates a security event report. And the self-healing strategy management module generates a self-adaptive repair scheme based on an AI analysis result, and the system realizes efficient anomaly detection and self-healing repair through intelligent analysis and self-adaptive adjustment, and is suitable for dynamic security protection in a complex network environment.

The invention discloses a large model method for network security, which sequentially constructs an adaptive security policy generation model, a network security risk assessment model and a multi-task deep neural network model through multi-stage training, so that a large model covers wide network security tasks, and comprehensive protection from threat detection to risk assessment is realized. The fine training and optimization of each stage ensure that the model deeply understands and deals with complex network security challenges. The self-adaptive security policy generation model adjusts the security policy in real time by learning the change of the network state, and the defense capability is enhanced. The network security risk assessment model generates an accurate risk score by analyzing risk factors, and provides a clear security situation view for managers. A double-attention mechanism is introduced to further optimize feature processing, channel attention intensifies key features, and space attention optimizes space distribution, so that the model is more accurate when capturing and positioning abnormal behaviors.

Systems and processes enhance cybersecurity by dynamically generating a decoy sub-application that operates parallel to the primary application using generative design and a large language model. The core feature involves real-time anomaly detection within application traffic, utilizing AI to assess threats and orchestrate appropriate responses. Upon identifying potential security threats, the system employs generative design principles to architect a restricted-functionality sub-application, deploying it instantly to engage and analyze the attack vectors without compromising sensitive production data. The sub-application is isolated through software-defined networking, ensuring that its operations do not affect the primary application's functionality. Additionally, sophisticated traffic redirection mechanisms are employed to divert suspicious traffic from the primary to the decoy application, thereby protecting the integrity while allowing detailed threat analysis. This dual-capability system not only safeguards against disruptions but also enhances adaptive security measures through continuous learning and system adjustments.

The invention relates to the technical field of network security, in particular to a satellite internet threat identification method and system and electronic equipment, and the method comprises the steps: constructing a node reputation evaluation model through a block chain technology, obtaining trusted nodes, constructing a satellite network, and collecting inter-satellite link information in real time; acquiring a satellite network topology change in real time, constructing an inter-satellite link security evaluation model, and generating a self-adaptive security policy matched with the inter-satellite link security level; dynamically calculating and optimizing a data transmission path through a mixed integer linear programming algorithm; constructing an inter-satellite link rapid switching model to analyze inter-satellite link information, generating inter-satellite link abnormal information, and rapidly switching to a standby inter-satellite link according to a self-adaptive security policy and network topology information; performing data transmission according to the inter-satellite link, and monitoring and acquiring transmission behavior data; a data exception recognition model is constructed to analyze the transmission behavior, and a data exception response is obtained; and identifying the satellite network threat according to the data exception response.

The invention discloses an energy internet intelligent key node identification and elasticity enhancement method and system, and the method comprises the steps: 1, constructing and dynamically maintaining a semantic enhanced energy internet digital twin super network and a multi-modal knowledge graph, fusing multi-dimensional information, and achieving the self-evolution and dynamic reasoning capability; 2, based on the model, fusing multi-time scale prediction data, and adaptively evaluating the dynamic comprehensive criticality of the node through an intention-function-resource-vulnerability four-layer penetrating traceability model; 3, aiming at the key nodes, generating an intelligent attack strategy by utilizing a generative adversarial network, deducing an information physical cascade failure process by combining multi-agent deep reinforcement learning, and quantitatively evaluating the system elasticity; and step 4, based on an evaluation result, generating a self-adaptive security reinforcement and dynamic reconstruction decision oriented to active immunity and elastic optimization. According to the method, the accuracy and the dynamism of key node identification can be remarkably improved.

The invention relates to the technical field of cloud computing and identity authentication security, and discloses a cloud environment intelligent identity authentication cross-domain docking method and system.The cloud environment intelligent identity authentication cross-domain docking method comprises the steps that heterogeneous identity data of multiple cloud platforms is collected and preprocessed, and a standardized identity data set is obtained; constructing a unified identity semantic model based on the standardized identity data set and generating a feature embedding vector; performing identity mapping conversion on the feature embedding vector by using a deep neural network to obtain a mapped identity feature vector; performing multi-factor adaptive trust evaluation in combination with the mapped identity feature vector and the real-time behavior data to obtain a dynamic trust evaluation result; generating an adaptive security token based on the dynamic credibility evaluation result; according to the method, the problems of cross-domain isomerism and dynamic trust evaluation of identity authentication in a cloud environment can be solved, and an effective solution is provided for cloud computing security.

The invention relates to the technical field of intelligent construction, and discloses an intelligent construction collaborative management platform and method based on digital twinning, and the method comprises the steps: firstly obtaining the practical operation data of a target engineering machine, analyzing the data safety level of the practical operation data to generate a distributed storage path, extracting storage node identifiers in the path, and deploying the storage node identifiers to a digital twinning platform; analyzing a node storage state index, querying a data backup demand and constructing a storage backup framework; then identifying an access request of the storage security framework, verifying a user identity, calculating an authority level index, analyzing an access range, and determining a platform multi-layer access regulation; then monitoring a permission change event, collecting a log, identifying a risk access behavior and calculating an access risk index; and finally, adjusting node encryption configuration according to the risk index, identifying an authority control key, and generating a self-adaptive security policy of the target engineering machinery on the platform. According to the invention, the integrity and safety of construction management can be improved.

The invention discloses an electric power data drainage model based on a cloud security technology, and the model combines a plurality of flow traction schemes, introduces the flow to different cloud virtual security resources, and achieves the efficient utilization of resources and security protection. The method comprises the following steps: constructing a drainage model by taking traditional virtual security resources, Huawei cloud and Ali cloud as triple coordinate origins, determining a position according to traffic parameters, and having a dynamic updating mechanism; a position characterization sub-data model capable of being adaptively adjusted is built inside to adapt to different flow characteristics and network environment changes; determining a drainage direction based on the triple coordinate system, and carrying out optimization adjustment according to virtual resource load balancing and a security policy; proposing a security service function chain construction algorithm based on a graph neural network to adapt to different scales of cloud environments; through unified management and control of security technical prevention measures, the adaptive security protection capability and the cloud security operation service support capability are improved; the model enables resource allocation to be flexible, ensures accurate drainage, guarantees safety and stability, and provides guarantee for safe operation of a power network cloud platform.

The invention relates to the technical field of wireless communication protection, in particular to a wireless communication self-adaptive safety protection method and system based on edge computing. Capturing physical layer channel state information when the Internet of Things terminal equipment accesses the network gateway for the first time, a functional area semantic identifier of the network gateway and a current timestamp, and generating an anonymous session voucher; tracking operation track data of the anonymous session voucher, and constructing a track operation fragment; obtaining a privacy risk assessment value based on trajectory uniqueness, spatial information sensitivity and behavior association analysis of the trajectory operation fragment; when the privacy risk assessment value exceeds a preset risk threshold value, triggering a track link breaking mechanism; obtaining privacy enhanced trajectory data based on privacy protection processing and a trajectory broken link mechanism; training a virtual data generation model through the privacy enhancement trajectory data; and after the virtual data generation model converges, driving a virtual data generator in the virtual data generation model to generate a virtual operation and maintenance data stream so as to realize self-adaptive protection of wireless communication data.

The invention belongs to the technical field of artificial intelligence, and discloses an energy data acquisition encryption and privacy protection method and system. The power grid frequency deviation FREQ of a power line carrier communication system is obtained at the time of power grid data acquisition, and deviation information at different time points is combined into a random number sequence by using a hash function; a specific pulse signal is sent at a data acquisition point to obtain PLC channel pulse response CIR, a minimum mean square error estimation algorithm is used for processing, and key features such as the maximum pulse amplitude are extracted to serve as channel feature values. And then, inputting the random number sequence and the channel characteristic value into a deep reinforcement learning model trained by a large amount of historical data to generate an encryption key. And finally, encrypting the acquired energy data by adopting an AES algorithm based on the key, transmitting the energy data through an OFDM technology, and meanwhile, generating the key again according to the change of a channel characteristic value by setting a self-adaptive security mechanism. According to the method, the safety and privacy of energy data acquisition and transmission are remarkably improved.

The invention provides a self-adaptive transformation and migration method and system for an internal website, and relates to the technical field of website migration. Comprising the following steps: constructing a hierarchical credential adaptation architecture, establishing a dynamic authority-data mapping mechanism, realizing knowledge base-data migration linkage and deploying an adaptive security protection chain. The method is used for realizing collaborative improvement of compatibility, security and knowledge service efficiency of an internal website in a credential environment through hierarchical credential adaptation, dynamic permission and data association, knowledge base linkage and self-adaptive security protection; besides, through cooperative work of the hardware driving adaptation module and the software interface conversion module, automatic identification of hardware features and interface features, precise analysis of adaptation requirements and dynamic generation of configuration files are realized. The compatibility of an internal website to different models of servers, autonomous controllable components and software interfaces in a credential environment is remarkably improved, the adaptation period is shortened, and stable operation of the system is guaranteed.

The invention provides a cross-border data flow safety management method based on a block chain and dynamic risk assessment, and relates to the technical field of cross-border data processing, and the method comprises the steps: carrying out the data collection, and carrying out the classification extraction according to the sensitive information types of data; establishing and quantifying a multi-dimensional risk assessment index; performing dynamic cross-border risk assessment through a comprehensive risk calculation model according to the multi-dimensional risk assessment indexes to obtain a comprehensive risk score of the data to the transmission target; executing a self-adaptive security policy according to the comprehensive risk score; generating a security transaction abstract on the basis of the data subjected to the adaptive security policy processing, and performing uplink evidence storage; and sending the data after the execution of the adaptive security policy to a transmission target, and verifying the data by the transmission target. The cross-border data security management and control method has the advantage of improving intelligence, refinement and reliability of cross-border data security management and control.

The invention belongs to the technical field of data processing, and discloses a flexible loading self-adaptive safe intelligent information processing method and system, and the method comprises the steps: carrying out the sensitivity evaluation of Internet of Things information based on a preset evaluation index, dividing the Internet of Things information into a plurality of credible levels according to a sensitivity evaluation result and a predefined target user access authority; configuring encryption and decryption modes for the Internet of Things information under different credibility levels, and generating an Internet of Things information access mechanism conforming to the access permission of the target user based on the encryption and decryption modes; and setting a burst cross-level scheduling mode based on an Internet of Things information access mechanism, and if an emergency occurs in the Internet of Things system, starting the burst cross-level scheduling mode to realize information scheduling among Internet of Things information. According to the method, the problem of insufficient consideration of security credibility in a traditional Internet of Things information scene is effectively solved, and security circulation of information is ensured from multiple dimensions and mechanisms.

The invention discloses an anti-quantum key management method for a vehicle-mounted domain centralized electronic and electrical architecture. Firstly, the invention provides a self-adaptive security policy generation method based on an analytic hierarchy process. Key factors influencing vehicle-mounted service safety are sorted through a system, a three-layer AHP hierarchical structure model with the purpose of selecting an optimal encryption mechanism is constructed, and therefore vehicle-mounted service safety modeling is achieved. According to the method, the most suitable security policy can be automatically matched according to the characteristics of different services, the self-adaptive security policy generation of the service granularity is realized, and the optimization of the system resource use efficiency is realized while the communication security is ensured. Secondly, the invention provides a fine-grained anti-quantum key management scheme based on the Chinese remainder theorem, and the fine-grained anti-quantum key management scheme is specially designed for adapting to a one-to-many communication scene widely existing in a vehicle-mounted network. The method supports efficient distribution and dynamic updating of group keys of service granularity, and has good quantum attack resistance.

The invention discloses an asynchronous federal self-adaptive security aggregation method based on heterogeneity perception, which comprises the following steps that: each client carries out training update on global model parameters based on a local data set to obtain each client model; selecting a gradient sensitive layer of the client model, and adding Laplace noise to an update gradient of the gradient sensitive layer to obtain a first disturbance update gradient of the client model; the server side calculates the maximum update difference of all client side models, and distributes privacy budget to each client side based on the maximum update difference; injecting Gaussian noise into the first disturbance update gradient of each client model according to the privacy budget to obtain a second disturbance update gradient of each client model; screening the clients to obtain an effective client set; calculating an aggregation weight of each client in the effective client set; and determining a weighted median of the effective client set according to the aggregation weight, and updating the global model according to a second disturbance updating gradient of the client corresponding to the weighted median.

The invention relates to the technical field of safety monitoring artificial intelligence, and discloses a safety management method and system based on artificial intelligence and big data. The method comprises the steps that multi-modal real-time monitoring data is collected through a distributed sensor array, and a standardized sequence is generated after preprocessing; the system accesses a historical security event library, adaptively divides risk category clusters by using an unsupervised clustering algorithm, and extracts key risk features and influence domains for each cluster. On the basis, a plurality of special self-adaptive security analysis engines are configured to perform parallel analysis on real-time data to generate a plurality of security analysis feature maps. And carrying out weighted fusion and enhancement on the feature maps through a feature fusion network to obtain a unified security situation feature vector, and finally outputting a security level and a management and control instruction through a decision model. According to the method, autonomous identification, dynamic evaluation and accurate response of complex risks are realized, and the intelligent level of safety management and the decision accuracy are improved.

The invention discloses a dynamic response method of a network attack defense strategy based on multi-head attention fusion, and relates to the technical field of network security, and the method comprises the steps: collecting network flow data and equipment state data of IIOT equipment, carrying out the time alignment, obtaining statistical features and frequency domain features through feature extraction, and generating a structured feature matrix; calculating a cross-domain association weight of statistical features and frequency domain features in the structured feature matrix through an attention mechanism, generating a fusion feature vector, carrying out time sequence modeling by using an LSTM model, calculating an abnormal probability score, and generating an abnormal event report; the method comprises the following steps: mapping spatio-temporal information in an abnormal event report into a quantum bit coding scheme in a digital twin environment, generating a Pareto optimal solution set by using a quantum annealing algorithm, screening a final network attack defense strategy, and coding the final network attack defense strategy into a quantum decision instruction. According to the invention, the adaptive security protection capability of the industrial Internet of Things system in a complex attack scene is effectively improved.

The invention discloses a dynamic encryption and security level adaptive adjustment method for flight data, particularly relates to the field of data security, and is used for solving the security and integrity problems of the flight data in an unstable network environment. According to the method, the mass of random seeds is quantified in real time in the mixing process, incremental seeds are added once the dispersion is reduced, and weak random source transmission is blocked; the historical track continuously provides threshold correction, so that the secret key updating rhythm and the link fluctuation are kept at the same frequency; a new secret key with sufficient safety margin is output by depending on a structured spin mixing and real-time source supplementing mechanism, and a secret key material keeps double characteristics of high discretization and high independence, so that randomness attenuation caused by packet missing, retransmission or node out-of-step is avoided; meanwhile, security level adjustment logic is deeply coupled with a key generation process, so that adaptive balance of encryption strength and operation load is realized, and the flight data is enabled to maintain collaborative excellent performance of confidentiality, integrity and real-time performance in a complex environment.