45 results about "Big data security" patented technology

The invention discloses an enterprise big data security early warning method based on anomaly detection, and the method comprises the following steps: S1, collecting original data, and carrying out the format unification and structure standardization processing; s2, preprocessing is carried out, and feature vectors are constructed; s3, a behavior entity relation graph is constructed, a graph attention network is adopted for training, and structural features in a normal behavior mode are learned; s4, calculating the deviation degree between the current behavior and the normal behavior; s5, reconstructing the feature vector, measuring the deviation degree between the current behavior and the standard behavior distribution by using a mahalanobis distance, and calculating the posterior anomaly probability of the behavior through a Bayesian updating mechanism; and S6, evaluating the risk level of the current behavior according to the posterior anomaly probability, and generating a corresponding early warning event. According to the method, the graph attention network and the Bayesian self-coding technology are fused, enterprise behavior anomaly detection and grading early warning are achieved, and the method has the advantages of being high in recognition precision, high in self-adaption and timely in response.

The invention relates to a big data security management system, and provides a security management system covering a whole life cycle aiming at the problems of untrusted source end, link leakage, centralized storage of single points, extensive authorization, disposal lag and the like. The system comprises an acquisition security module, a transmission encryption module, a storage security module, an access control module, a threat detection module, a situation awareness module, a traceability module and an emergency response module. A session basis is generated through multi-factor authentication, a dynamic key is derived according to time slices, and data fragments are encrypted one by one; distributed storage is adopted, and metadata is chained to be non-tampered; implementing fine-grained authorization by taking the attribute as a core; utilizing a sequence model linkage knowledge graph to identify abnormity and calculate a risk index; and when the threshold is exceeded, automatically reconstructing a log chain and executing isolation, alarm or recording. According to the system, trusted acquisition, confidential transmission, traceable storage, fine authorization and automatic disposal are realized, and the safety and compliance are remarkably improved.

The invention relates to the technical field of big data security analysis and network information security, in particular to a communication data intelligent security supervision system based on big data, which comprises a data acquisition module used for extracting standardized entity behavior feature vectors from multi-source heterogeneous communication data; the baseline modeling module is used for dynamically generating a multi-dimensional behavior baseline portrait based on the historical sequence of the entity behavior feature vectors; the anomaly detection module is used for comparing the real-time entity behavior feature vector with the multi-dimensional behavior baseline portrait so as to calculate a micro anomaly score; the atlas construction module is used for screening the micro-anomaly events according to whether the micro-anomaly score exceeds a preset threshold value or not, and quantifying association confidence among the screened events so as to construct an attack chain atlas; the risk quantification module is used for aggregating the characteristics of the attack chain atlas to determine a systematic risk score; according to the method, the discovery capability and response efficiency of complex attacks such as advanced persistent threats and the like are greatly improved.

The invention discloses a block chain-based big data secure storage sharing method and system. The method comprises the following steps: firstly, preprocessing original big data, including data cleaning, desensitization and blocking operation; then the preprocessed data is encrypted, and the safety of the data in the storage and transmission process is ensured; generating a data fingerprint from the encrypted data, and storing the data fingerprint on the block chain to realize non-tampering and traceability of the data; the encrypted data is stored in a distributed storage system, so that the reliability and the expandability of data storage are improved; sharing and access control of data are realized through an intelligent contract, and only an authorized user can obtain a data key, decrypt and use the data; and finally, realizing full-life-cycle traceability of the data by using the block chain. The method has the advantages of high security, high traceability, high reliability and the like, and is suitable for security storage and sharing scenes of big data.

The invention relates to the field of cloud computing big data secure storage, in particular to a cloud computing-based big data secure storage method and system. The method comprises the following steps: performing encryption and fragmentation processing on an original data stream to generate a fragmentation sequence with an index; generating a dynamic key sequence and a verification key group based on the fragment information; constructing a secure transmission channel through a quantum key distribution mechanism in combination with a geographic position weight and a load balancing strategy, and realizing encapsulation and transmission of a secure fragment packet; node state monitoring and redundancy management are implemented, and an update mapping table is generated; and finally, a complete data stream is recovered through fragment positioning retrieval, data recombination and dynamic decryption. According to the invention, the security and reliability of data storage and the overall load balancing capability of the system are improved.

The invention discloses an RAG and knowledge graph question-answering method and system for security classification and grading of cross-border data, and belongs to the crossing field of big data security management, artificial intelligence and compliance technologies. The method comprises the following steps: step S101, receiving and preprocessing user input; step S102, performing question analysis and semantic intention recognition; step S103, intelligent scheduling control decision making; step S104, reasoning and executing the knowledge graph; step S105, performing RAG generation reasoning execution; step S106, performing intelligent fusion and decision traceability; step S107, carrying out multi-dimensional visual display; step S108, audit record and report generation; and S109, updating the dynamic knowledge. The method has the advantages of intelligent question and answer, multi-source fusion, sustainable evolution, traceability of the whole process and the like, and can be widely applied to compliance management and intelligent decision-making scenes related to data cross-border flow, such as enterprises, governments, finance, medical treatment and the like.

The invention discloses a big data security protection and information analysis system and method in an Internet of Things environment, and the method comprises the steps: an Internet of Things device collects data, transmits the data to a storage module through encryption, carries out the encryption processing of the data through a hybrid encryption technology, and carries out the hierarchical storage according to the sensitivity; the system uses multi-factor authentication to verify the legality of the device, and adjusts the access authority according to the behavior of the device; in the data storage and analysis process, homomorphic encryption and data anonymization technologies are applied; the state of the data flow is monitored in real time, abnormity and potential threats are identified, and a processing strategy is dynamically adjusted in combination with intelligent analysis; when a security threat is detected, unauthorized equipment is automatically blocked, and abnormal data flow propagation is prevented; according to the big data security protection and information analysis system and method in the Internet of Things environment, through a multi-level security protection mechanism, it is ensured that big data analysis of the Internet of Things is safe and efficient, and the intelligent decision-making ability and the application efficiency are improved.

The invention discloses a security assessment analysis system and method based on big data, and relates to the technical field of network security, and the method comprises the steps: constructing a directed causal graph through a behavior vector matrix, recognizing a causal dependence path through causal reasoning and time sequence modeling, and outputting an attack chain path set; identifying a data source node, a transfer node, a processing node and a terminal node according to the attack chain path set, extracting risk factors, and executing vectorization processing to form a risk factor vector set; and constructing a multi-layer nested tensor structure by using the risk factor vector set, executing a tensor convolution operation, generating a risk strength tensor map, calculating a time sequence risk level of each node, obtaining a time sequence risk level distribution set, and generating a safety assessment report in combination with a safety response execution record. According to the method, classification node risk factors are combined in a multi-dimensional space-time structure through tensor volume accumulation, the problem of one-sidedness of single-point evaluation is solved, and risk propagation and strength evolution between nodes are dynamically visualized.

The invention discloses a big data security access control method. The method comprises the following steps: collecting multi-dimensional information of an access request, including time, place, equipment, network environment and historical behaviors, and generating a risk score in combination with a weight; and converting the risk score into a risk probability by using a probability mapping model, and dividing risk levels according to a preset threshold. And the system forms a dynamically adjustable permission set according to the basic permission of the user role and the limit permission corresponding to the risk level, and calculates a security strength index. And further combining the risk score with the security strength to generate a comprehensive risk coefficient and recording the comprehensive risk coefficient in an audit log. And when the risk coefficient exceeds a threshold value and exceeds the limit continuously for multiple times, triggering a security response mechanism, including account freezing, session blocking, alarm pushing and global strategy tightening. According to the method, real-time evaluation, fine-grained control and linkage response of big data access are realized, and the dynamic defense capability and the overall security of the system are improved.

The invention discloses a mining operation big data security processing and analysis system, and belongs to the technical field of data analysis. The method is used for solving the technical problems of poor data quality and high communication delay caused by mine operation environment influence in the existing scheme. Based on a mine sensor topological structure, space-time registration is carried out on environment data and operation data, an improved Kalman filtering algorithm is adopted to dynamically suppress electromagnetic interference and noise, and missing values are complemented through fractional order differential; constructing a roadway partition routing protocol, performing local feature extraction on the preprocessed data by an edge node, dynamically adjusting a transmission priority based on a data security level and time delay sensitivity, and transmitting high-priority data by adopting a 5G slicing technology; and fusing the calibrated data and the extracted key features by using a space-time diagram convolutional network, performing data analysis on the obtained fusion feature vector, outputting a real-time risk level, and dynamically adjusting a filtering scheme and a transmission bandwidth allocation scheme.

The application relates to the field of regional security management, and particularly discloses a big data security monitoring system, which comprises an image acquisition module, an image application module, a supply data detection module and an emergency end control module. The image acquisition module is used for inserting an information reader into a camera network to acquire scene images under dynamic frequency by the information reader. The image application module is used for carrying out numerical conversion on the scene images, and determining target distribution in a region according to the converted values. The supply data detection module is used for calculating a demand value according to the target distribution, randomly detecting supply data according to the demand value, and outputting a risk value according to a detection result. The emergency end control module is used for generating a control instruction pointing to an emergency end, and sending the control instruction to the corresponding emergency end. The application acquires images through a monitoring system, carries out contour recognition on the images, further determines energy supply parameters required by the images, identifies actual energy supply parameters according to the energy supply parameters, judges the risk value, accurately and timely generates reminding information, and reports the reminding information to senior management personnel.

The invention discloses a big data security risk assessment method and system, and relates to the technical field of big data processing, and the method comprises the following steps: obtaining a big data operation scene type in an operation state, service data streams and system operation logs corresponding to the scene types are collected in real time or periodically through a data collection module; determining a risk modeling mode corresponding to the operation scene type according to the operation scene type; extracting and determining risk assessment parameters for subsequent analysis; and performing comprehensive processing and analysis on the original risk indexes through the risk assessment parameters, thereby generating a safety risk assessment result and outputting the safety risk assessment result. The problems that an existing method is insufficient in authenticity and poor in adaptability can be effectively solved. By obtaining the big data operation scene type and combining the real-time service flow log and the historical security event data, the risk modeling mode matched with the scene is dynamically selected, and therefore the pertinence and flexibility of risk modeling are guaranteed.

The invention discloses an access permission control method and equipment for distributed data services, and belongs to the technical field of big data security and data governance, and the method comprises the following steps: in a data structure corresponding to a data permission strategy of a distributed data system, constructing a dynamic permission condition for a data resource security demand; the method comprises the following steps: when an access request of a user to data resources is received, acquiring basic ternary information and context information of the access request; and evaluating the basic ternary information and the contextual information of the access request based on a dynamic permission condition so as to allow or reject the access request. According to the method, the accuracy and safety of permission decision making are improved.

The invention provides a computer network big data security protection method and system, and the method comprises the steps: collecting a data block access event after a user query job is executed; extracting a quantitative index of the job based on all data block access events, and generating an aggregation access feature set of the job; matching and comparing the aggregated access feature set with a reference feature cluster to obtain a behavior deviation metric value; determining a behavior inertia index when the user performs query operation through aggregation access features of historical operations of the user, and further performing feature fusion on the behavior inertia index and the behavior deviation metric value to obtain a comprehensive risk quantized value for judging whether the operation is a malicious crawling behavior or not; and if the comprehensive risk quantized value exceeds a preset risk threshold value, implementing resource quota limitation on query jobs which are submitted by the user subsequently and have similar access mode characteristics. By adopting the scheme of the invention, the low-frequency malicious crawling behavior with behavior disguise and mode avoidance capabilities can be identified.

The invention relates to the field of data monitoring, in particular to a station real-time monitoring device and method based on edge computing, and the device comprises a data collection module, an edge computing module, a device control module, an alarm notification module, a cloud processing platform and a user interaction interface. Compared with a centralized monitoring system which is commonly adopted in the prior art and depends on a remote data center to carry out data processing and analysis, the system has the defects that data processing delay is high, bandwidth occupation is large, data security risks are increased, and system reliability is affected by network stability; according to the system, real-time processing and analysis of data at the edge of equipment are realized by adopting an edge computing technology; the delay and bandwidth cost of data transmission are remarkably reduced, the real-time response capability of the system is improved, and the local security of data and the distributed fault-tolerant capability of the system are enhanced.

The invention relates to the technical field of data security processing, in particular to a commercial finance big data security processing method, which comprises the following steps: S1, receiving uploaded data from a plurality of data provider nodes, the uploaded data comprising local feature vectors encrypted by using a joint public key based on a threshold homomorphic encryption mechanism, the commercial financial subject anonymous identifier is encrypted by using the same joint public key and corresponds to the feature vector sample; according to the method, security alignment and cooperative calculation of financial data under full-flow ciphertext are realized, privacy is guaranteed and processing efficiency is greatly improved through decentralized architecture and hardware acceleration, and a reliable and efficient solution is provided for cross-mechanism data fusion.

The invention discloses a big data security storage and backup method based on hierarchical authority, and relates to the technical field of data storage and backup, and the method comprises the steps: S1, constructing a user access behavior collection module, and collecting the access time, data access frequency, operation type, terminal environment information and access IP address when a user accesses a big data system in real time; by constructing the user access behavior portrait model, dynamic linkage of the authority and the user real-time behavior is realized, the problem of authority distribution rigidness in the prior art is solved, the data leakage risk caused by over-wide authority is avoided, the service efficiency under the compliance behavior is guaranteed, and the user experience is improved. A differential key generation and verification mechanism is matched for different permission levels, a fixed key form is replaced, the key leakage risk is reduced, safety and usability are both considered, and collaborative optimization of permission change and data storage and backup is achieved by designing a storage and differential backup strategy matched with dynamic permission.

This application provides a data security classification method and apparatus, belonging to the field of data analysis technology and also applicable to the field of big data security technology. One embodiment of the data security classification method includes: determining the required domain knowledge and corresponding data classification standards based on the data to be classified; determining multi-level labels and hierarchical logical relationships between the labels from the data classification standards; acquiring a sample dataset and, based on the hierarchical logical relationships and a pre-trained data annotation model, performing multi-label hierarchical annotation on each sample data in the sample dataset to obtain the multi-label hierarchical annotation result of the sample dataset; training a hierarchical classification model based on the sample dataset and the multi-label hierarchical annotation result, and determining the classification result of the data to be classified based on the trained hierarchical classification model. The technical solution of this application has interpretability and can improve the accuracy of data security classification.

The application belongs to the technical field of data management, and discloses a clinical test data management method and system based on artificial intelligence. The method comprises the following steps: a cloud data center constructs a clinical test data management engine and a clinical test knowledge graph, and deploys a blockchain storage network; a data server uses an OPC UA protocol to upload real-time clinical test data to the cloud data center; the cloud data center uses the clinical test data management engine to perform standardization processing; the cloud data center uses the clinical test data management engine to perform semantic expansion according to the clinical test knowledge graph; the cloud data center uses the clinical test data management engine to perform data analysis; the cloud data center performs encryption; and the cloud data center uses the blockchain storage network to perform data storage. The application solves the problems of great management difficulty, low data security and poor analysis accuracy in the prior art.

The invention discloses a computer network big data security protection method, which comprises the following steps of: deploying data acquisition probes at a plurality of nodes in a network, and acquiring and preliminarily filtering network flow data, terminal state data and user behavior data; carrying out feature preprocessing on the collected and filtered data, and constructing a feature vector for risk identification; inputting the feature vector into an anomaly identification model to identify a security risk and output a risk level and / or a risk type; according to the risk level and / or the risk type, differential security defense operation matched with the risk level and / or the risk type is executed; security logs generated in the collection, identification and defense processes are uploaded to a block chain for distributed storage; and based on a network node feedback result after the differentiated security defense operation and a newly added feature vector for risk identification, carrying out incremental training on the anomaly identification model so as to carry out iterative optimization. According to the method, the risk identification precision and the protection efficiency under the high-dimensional data are improved.

The invention discloses an intelligent information early warning system and method based on big data security analysis, and relates to the technical field of network security. According to the method, the candidate feature fragments are extracted from the historical standard security data flow through the feature extraction network, the candidate feature fragments are mutually matched through the collaborative matching mechanism, then the security event fragments are generated, the security event fragments are marked as the atomic security features, and the atomic security features are clustered and fused to generate the composite security feature. The method comprises the following steps: traversing all historical standard security data streams through composite security features to extract feature data fragments, sorting the feature data fragments to generate a multi-dimensional threat feature matrix, establishing threat behavior association trees according to the multi-dimensional threat feature matrix, matching the threat behavior association trees and connecting the threat behavior association trees with a global threat feature forest to obtain a threat behavior association tree; and converting the standard security data generated in real time into a detection feature sub-matrix, inputting the detection feature sub-matrix into the forest for matching analysis, and outputting a security early warning report.

The application relates to the field of financial risk assessment, and specifically discloses a user financial risk intelligent assessment method and system, which comprises the following steps: constructing a three-dimensional big data security protection framework to control user multi-type and multi-modal data in all links; constructing a collaborative framework based on a federal learning hybrid architecture, intelligently analyzing data by adjusting an AI model cluster, a workflow engine and a financial large language model after adjustment through a reinforcement learning intelligent agent; constructing a three-dimensional portrait model, extracting special features of digital financial scenes such as internet loans, dynamically updating and outputting scene-based risk positioning results, and realizing accurate portrait; calculating a dynamic accurate pricing formula based on a formula dynamic interest rate pricing = benchmark interest rate * risk adjustment coefficient * scene adaptation coefficient * market fluctuation coefficient, optimizing coefficient weights through an intelligent agent, and realizing dynamic accurate pricing; and outputting three-layer interfaces of a regulatory level, a financial institution business level and a user level. The application provides a standardized and efficient technical solution for user financial risk intelligent assessment.

The invention discloses a power grid big data security real-time monitoring and risk prediction method and system, and relates to the technical field of power grid big data risk management. The power grid big data security real-time monitoring and risk prediction method comprises the following steps: evaluating power grid fault interference; fault assessment is accurately regulated and managed; and predicting the power grid risk level. According to the method, the accurate condition of power grid big data fault assessment is analyzed, whether the risk identification accuracy degree is monitored or not is judged based on the analysis result, if not, accurate regulation and control of fault assessment are carried out, and if yes, whether the corresponding power grid big data are input into the preset risk level prediction model or not is judged based on the monitoring result. According to the method, the effect of improving the evaluation synchronism in the risk evaluation process based on the power grid big data is achieved, and the problem of evaluation lag in the risk evaluation process based on the power grid big data in the prior art is solved.

The invention discloses a big data security supervision system and method based on a computer, and belongs to the technical field of data security. The system comprises a data acquisition module, a data processing module, an operation management module and a data storage module, the data acquisition module is used for acquiring transmission information and historical records; the data processing module is used for classifying target objects in the transmission information, searching whether the target objects exist in historical records or not, if yes, calculating the similarity between transmission files corresponding to the target objects and historical transmission files corresponding to the historical records, and substituting the similarity into a formula to obtain the accuracy rate of the transmission behavior; the operation management module is used for carrying out early warning on transmission behaviors of which the accuracy is smaller than an accuracy threshold and the target object does not exist in historical records, prompting a user to carry out selection again, and determining a transmission mode according to a user selection result; and the data storage module stores the transmission information of each time into a historical record to provide data support for subsequent transmission judgment.

The invention discloses a block chain smart contract-based big data security reward and punishment automatic decision-making system, and belongs to the technical field of block chains, the system comprises a data collection unit, an examination and approval unit and an evidence storage unit, reward and punishment record evidence storage is performed by using a block chain, and the examination and approval process is automatically executed through a smart contract. The smart contract is configured to be automatically triggered and executed when a preset condition is met. The reward and punishment approval process is automatically executed through the intelligent contract, manual intervention is not needed, and the approval speed and the system response capability are remarkably improved. And meanwhile, the deviation of manual judgment is reduced, and the consistency and objectivity of reward and punishment decisions are ensured. The block chain technology is adopted to store the reward and punishment records, it is ensured that the data cannot be tampered, and the security and integrity of the data are provided through encryption processing and a signature mechanism. Therefore, the tamper-resistant capability of the system is enhanced, and the transparency and credibility of reward and punishment management are also improved.

The application discloses a function secret sharing method of a Boolean type interval containing function and belongs to the field of big data security calculation and privacy protection; firstly, a distributed computing scene of two servers which do not collude with each other and a user end is built; then, the user end initiates an evaluation request signal to the two servers, carries an input share and which is secret shared by an ASS algorithm, and executes a Boolean type interval containing function bDICF to generate a pair of server side keys and respectively and respectively send the keys to the two servers. Finally, the two servers respectively randomly mask the respective input shares, obtain masked inputs, combine the held keys and, perform the evaluation algorithm of the two ibDCF on the masked inputs, obtain share and of the evaluation result of whether belonging to the interval, and return respective output shares to the client, and the client XOR reconstructs a plaintext judgment result. The application reduces communication overhead and calculation complexity while completing nonlinear interval operation under single round communication.

The invention relates to the technical field of industrial internet security, and discloses an industrial internet platform big data security protection system. A security data acquisition module of the system acquires multi-source security data such as network flow characteristic parameters and equipment behavior log parameters; the attack behavior prediction module generates an attack behavior prediction map by using a behavior prediction model according to the multi-source security data, defines a security communication protocol parameter adjustment boundary range and generates a security protocol adjustment instruction; the communication protocol optimization module generates an optimized security communication protocol sequence by adopting a protocol encapsulation technology in combination with a service traffic shaping demand and a real-time threat state; the traffic shaping module determines a protocol field adjustment direction and proportion according to the security protocol adjustment instruction, and adjusts a protocol sequence; and the security verification module corrects a protocol sequence time sequence trigger node to realize synchronous self-adaptive protection of data transmission security and threat dynamic change.

The invention discloses a risk behavior detection and alarm method and system based on log analysis, and belongs to the technical field of big data security analysis and big models.The method comprises the steps that log collection is conducted, specifically, a multi-source log is captured in real time through a distributed agent and standardized; feature engineering: extracting time, behavior, statistics and session features and constructing feature vectors; calculating an operation interval and a non-working period mark according to the time characteristic; according to the behavior characteristics, the difference between high-frequency operation and the geographic position is counted; analyzing session duration and an operation sequence mode according to the session characteristics; reasoning by adopting a logistic regression model: outputting a risk score based on the feature vector by adopting the logistic regression model; and graded alarming: triggering alarms of different grades according to the risk score. According to the invention, accurate identification and rapid disposal of system risk behaviors can be realized.

The invention discloses a big data security-oriented multi-dimensional privacy protection and desensitization system, which comprises seven layers of architectures working cooperatively in sequence, and the seven layers of architectures comprise: a data access layer, which is used for receiving multi-source heterogeneous data and executing access security verification; the data preprocessing layer is used for cleaning and classifying the data output by the data access layer and identifying and grading sensitive information; the core desensitization layer is used for executing static desensitization by adopting a multi-dimensional privacy protection model based on the sensitive grading result output by the data preprocessing layer; according to the method, multi-dimensional protection can be carried out; a static desensitization k-anonymity model and dynamic calculation protection are combined, so that the full life cycle of data is covered; multi-source heterogeneous data access and identification are supported, and the sensitive information identification precision is greater than or equal to 95%; balancing privacy and availability: through hierarchical desensitization and effect verification, ensuring privacy compliance and retaining 90% or more of data analysis value at the same time; the whole process is controllable, operation traceability is achieved, and abnormity can be detected.

The invention discloses a data security storage system based on big data analysis, which relates to the technical field of big data security storage and comprises a packaging slicing module, a feature analysis module, a clustering modeling module and a coding storage module. The encapsulation slicing module performs hierarchical sequential slicing on original data according to data time continuity and content similarity to generate a slice set with an identifier. The feature analysis module executes multi-scale feature detection, and adaptively adjusts each feature weight through a dynamic weight distribution network to form a weighted feature vector. The clustering modeling module is used for clustering the feature vectors by using a self-organizing feature mapping network to generate feature cluster description; and the coding storage module executes security enhancement coding and integrity binding according to a clustering result to form a traceable security storage unit. According to the system, internal association between data slices can be kept, and the processing weight is dynamically optimized according to the data characteristics, so that the security storage accuracy and the self-adaptive management capability are improved.