10 results about "Security index" patented technology

The invention provides a comprehensive security index evaluation method based on multi-task learning, and belongs to the technical field of network security management, and the method comprises the steps: synchronously collecting original data from a plurality of heterogeneous security data sources; a multi-task learning neural network model is constructed, and the model comprises a shared feature coding network for outputting shared features; the at least two heterogeneous task decoding networks are used for respectively mapping the shared features into preliminary security assessment results with different attributes; the fusion network is used for carrying out weighted fusion on the preliminary security assessment result to generate a comprehensive security index; using the multi-source security data set to train the multi-task learning neural network model; and inputting real-time security data of a to-be-evaluated system into the trained model, and outputting a comprehensive security index. The method has the advantages that the shared feature coding network can extract high-level feature representations with commonality from different data sources, and feature engineering dependence for a single data source or a single task is avoided.

A door lock security assessment method and a related device. The method comprise: sending a security assessment instruction to a door lock end, so as to trigger the door lock end to perform local security environment assessment to obtain a first security index corresponding to the door lock end; acquiring historical behavior data for the door lock end within a specified time period; performing security assessment analysis on the historical behavior data to obtain a second security index; receiving a third security index, which is sent by a control end; determining a security assessment result of a door lock on the basis of the first security index, the second security index and the third security index; and sending the security assessment result of the door lock to the control end.

The application provides a real-time security evaluation method and device. The method comprises the following steps: acquiring a monitoring data stream, and extracting security level information corresponding to each time point of all data blocks from the monitoring data stream; updating the weight change of the security index based on the security level information through a KernelSHAP method; incrementally updating an evaluation model by using the updated security index and weight, and predicting a security evaluation result by using the updated evaluation model; and determining a security evaluation result according to a preset decision threshold and the security evaluation result. By using the KernelSHAP method, the importance of the newly collected monitoring data stream is determined based on a wide learning system structure, the negative influence caused by the approximation error is reduced, the drift detection is combined with the adaptive and generated explanation ranking preference, and therefore the result of the real-time security evaluation task can be better predicted.

The invention belongs to the technical field of artificial intelligence, and relates to a security policy arrangement method and system based on a large model, and the method comprises the steps: S1, constructing a security component library which is provided with a plurality of security components; s2, constructing an environment state vector; s3, setting different priorities for each security index and forming a user preference vector; s4, constructing a multi-target reward function; s5, training a security policy arrangement model based on reinforcement learning; and S6, when a current user request is received, utilizing the trained security policy arrangement model to dynamically generate a security component calling sequence according to the current environment state vector and the user preference vector, and gradually examining and processing the current user request and candidate replies based on the security component calling sequence. According to the method, normal form transition from a static rule link to dynamic intelligent arrangement can be realized, so that a large model has high safety, high availability, low cost and self-adaptive capability.

The invention provides a data security assessment method and system, and belongs to the technical field of data security, and the method comprises the steps: obtaining financial system data; the financial system data is financial data stored in a financial system; the financial system data comprises current financial data and historical financial data; obtaining early warning financial data based on the current financial data and the historical data baseline; the early warning financial data is partial data in the current financial data; wherein the historical data baseline is determined based on historical financial data; performing clustering processing on the early warning financial data to obtain a target clustering result; performing risk assessment on the financial system data based on the obtained target clustering result to obtain a target safety index; the target safety index is used for representing the safety of the financial system data. According to the data security assessment method and system provided by the invention, the accuracy of financial data risk assessment can be improved.

The application discloses an intelligent body running state security deviation real-time detection and self-trap stop method and system, and belongs to the technical field of intelligent body security monitoring. The application carries out sliding window statistical analysis on the intelligent body running time sequence behavior log, parallelly feeds normalized multidimensional security indexes into at least two independent detectors to capture sudden abnormality and persistent slight deviation, determines the security deviation grade through deterministic fusion, and finally executes corresponding control actions through a hierarchical self-trap stop state machine containing an irreversible absorption state. The application determines the security baseline through offline learning period, does not depend on probability distribution hypothesis, random variable or iterative optimization throughout the whole process, always produces the same output for the same input, and provides an intelligent body with a deterministic, auditable and non-bypassable running state security monitoring and self-trap stop scheme.

The application discloses a kind of based on power distribution network security situation Multistage fuzzy comprehensive evaluation method, it is related to power grid safety evaluation technical field, including acquisition power distribution network real-time operation data, environmental information and topological structure, combine history operation sequence to construct time series prediction model, output contains the dynamic safety margin sequence of key security index;Based on dynamic safety margin sequence, time attenuation cumulative operator is applied to the historical fuzzy membership degree of bottom layer security index, and the cumulative membership vector representing risk exposure intensity is generated;Call pre-built power distribution network knowledge graph, according to the semantic scene recognition of current operating state of power distribution network and the dynamic safety margin sequence, generate the multistage safety evaluation index subset adapted to current running context;Cumulative membership vector is mapped to the bottom layer security index of the multistage safety evaluation index subset, and the membership function with adjustable boundary parameter is constructed, and the fuzzy membership vector of bottom layer security index under preset comment set is output.

The invention belongs to the technical field of password target range safety operation and maintenance, and particularly discloses and provides a password target range health degree prediction and fault self-diagnosis method based on big data, which comprises the following steps: constructing a high-fidelity digital twin fusing a multi-level architecture and an operation log, defining a collaborative optimization model containing endogenous safety indexes, and performing collaborative optimization on the collaborative optimization model; the intelligent decision agent is trained through reinforcement learning, a verification execution strategy is deployed in a physical target range, and strategy increment retraining is achieved through actual operation and new attack and defense data backflow. According to the method, the fitting capability of the digital twinborn body to a real network environment is enhanced, the time sequence consistency of the digital twinborn body is ensured, fine management of security risks is reflected, closed-loop feedback between a physical target range and the digital twinborn body is realized, and an intelligent decision agent can continuously learn and adapt to a new scene.

This application provides a method, system, equipment, and medium for the operation and management of smart security business in a park, relating to the field of smart security technology. The method includes: acquiring real-time monitoring data of various areas within the park; generating a first security index for each area based on the real-time monitoring data; constructing a state correlation matrix between the areas; calculating correlation coefficients between the areas based on the state correlation matrix; adjusting the first and second security indices based on the correlation coefficients; generating a delay impact factor based on historical time-series correlated event data of each area; adjusting the second security index based on the delay impact factor to generate a target security index for each area; and identifying areas where the target security index is less than a preset index as target areas and generating early warning signals for these target areas. The technical effect of this application is to improve the accuracy of security early warnings.

The invention discloses a data storage processing method and device, equipment, a storage medium and a program product. The method comprises the following steps: receiving encrypted data sent by a data uploading node; wherein the secret state data comprises encrypted data of data to be stored and a security index; storing the secret state data according to a hierarchical index structure; wherein credible credential data corresponding to the encrypted data is stored in a block chain, and a transaction value in the credible credential data is calculated based on the security index and the encrypted data; through the hierarchical index structure and block chain storage, it is ensured that the data cannot be tampered, the data is prevented from being maliciously tampered or destroyed, and therefore the authenticity and safety of the distributed storage data based on block chain technology support can be improved.