72 results about "Big data security" patented technology

The invention relates to the field of industrial internet intrusion prevention, in particular to a computer network big data security protection method and system, and the method comprises the following steps: monitoring network flow, collecting user identity, equipment security state, access time and network behavior data, and generating a network behavior record. In the invention, a dynamic security assessment system is constructed through network flow monitoring, identity verification and behavior analysis, and compared with static strategy or fixed rule matching, the dynamic security assessment system can adapt to a continuously changing network attack environment. Authority dynamic adjustment is performed based on the user identity verification state, flexible management and control of the access authority are ensured, and the problem of long-term high authority abuse is avoided. Threat detection not only depends on historical mode matching, but also is combined with real-time behavior analysis, and accurate isolation is executed for potential threats, so that protection measures are changed from passive response to active intervention, and the delay of manual intervention is reduced.

The invention relates to the technical field of data security, in particular to a block chain-based big data security data storage method, which comprises the following steps of acquiring a write-in request, analyzing a field path and a label, matching a structure number and state information, sorting a conflict path, compressing a number sequence, distributing a compression path, positioning a writable area and updating a path pointer. And writing data and recording index and verification information to obtain a safe chain-entering data set. According to the method, a clear mapping relation is formed through binding of field paths and label numbers, path conflicts are subjected to number rearrangement and hierarchical compression processing, the structural coherence of field distribution is enhanced, field write-in positioning is completed in combination with node capacity, position indexes and verification identifiers are generated, and unified binding of field contents, the path numbers and access positions is achieved; the writing accuracy and the structure traceability are guaranteed, and the regularity of chain segment storage and the orderliness of operation are improved.

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 provides a block chain and machine learning combined big data secure transmission method in the technical field of information security and distributed computing crossing. The method comprises the steps that S1, a transmission end carries out data fragmentation and preprocessing on to-be-transmitted big data to obtain data blocks; s2, acquiring real-time security monitoring data, inputting the real-time security monitoring data into the data transmission risk identification model to obtain a transmission risk identification result, matching an encryption rule from an encryption rule set based on the transmission risk identification result, encrypting the data block to obtain a data encryption block, and compressing the data encryption block to obtain a data compression block; s3, calculating a data fingerprint uploading block chain of each data compression block, and sending each data compression block to a receiving end; s4, performing security defense through the abnormal transmission behavior recognition model in the receiving process of the receiving end; and S5, performing integrity verification through the data fingerprints, and performing decompression, decryption and splicing to obtain big data. The method has the advantage that the security and efficiency of big data transmission are greatly 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 a privacy protection system based on big data security and privacy calculation, which relates to the technical field of data processing and comprises a data acquisition module, a data processing module, a protection analysis module and a management analysis module. According to the method, the privacy of the desensitized data is improved while the original numerical range and precision of the desensitized data are maintained in a data desensitization mode of random disturbance, and the desensitized data can still support complex analysis and calculation due to introduction of random disturbance; and the data mask form based on differential privacy ensures that the masked data can still support effective data analysis while protecting privacy, so that the method can improve the privacy of the data, the data fusion form of homomorphic encryption and differential privacy can realize efficient and safe data fusion on encrypted data, and the data fusion efficiency is improved. The fused data has privacy and meets the requirements of complex analysis and calculation, the defects of a traditional data privacy protection system can be overcome, and the method is more intelligent and practical.

The invention relates to the technical field of big data processing, in particular to an energy big data secure storage method, a storage medium and a system.The method comprises the steps that energy data of any type of energy is obtained, the energy data comprises multiple data types, and any group of data in any data type is arranged according to a time sequence to form a data sequence; determining an association value and a data regularity of each type of data; the encryption strength coefficient of each type of data is obtained; determining the local feature approximation degree of each type of data based on the mutation moment difference and the distribution range difference corresponding to the subsequences between each type of data and all other types of data; determining the period approximation degree of each type of data; further obtaining a key length coefficient of each type of data; and when the energy data is stored, encrypting various types of data by adopting an AES encryption algorithm based on the key length coefficient. The invention aims to improve the encryption strength while ensuring the encryption efficiency.

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 an intelligent security linkage early warning method and system based on big data service, and relates to the field of intelligent security, and the method comprises the steps: constructing a big data security control platform, connecting multiple data sources, and collecting corresponding security data; constructing a double-layer security early warning model to identify security risks and abnormal behavior modes; according to the judgment result of the double-layer security early warning model, early warning information is responded, and the response strategy of the security equipment is adjusted. Through the big data platform and the double-layer early warning model, real-time security data collection and behavior pattern analysis are realized, and the early warning precision and the response speed are improved. Through graded early warning and dynamic response, high-risk behaviors are ensured to be intervened in time, resource allocation is optimized, the efficiency of a security and protection system is improved, potential risks are effectively prevented, and community safety and resident satisfaction are guaranteed.

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 secure storage method and system, and belongs to the field of data processing, and the big data secure storage method comprises the following steps: judging the importance of different data of a data set, classifying the different data of the data set into data needing to be encrypted or data not needing to be encrypted; grading the data needing to be encrypted based on the importance degree of the data needing to be encrypted to obtain data of different sensitive levels; compared with the prior art, the method has the beneficial effects that the data is classified, and a large amount of conventional data is not encrypted, so that the calculation amount consumed by encryption and decryption is reduced; the data needing to be encrypted is graded, and different encryption algorithms are used based on different sensitive levels, so that the reliability of the data is ensured; when the user modifies the data, feedback information is pushed to the decision-making user, so that working conflicts or data coverage is avoided, and decision-making errors are reduced.

The invention discloses an electric power big data security management method and system based on artificial intelligence, and relates to the technical field of artificial intelligence. The method comprises the steps that electric power big data are collected and preprocessed, the data collection process is monitored in real time through a nondestructive testing technology, an electric power big data classification and grading model is constructed through machine learning, an electric power big data classification and grading data set is encrypted, and a risk destruction program, a safety recovery program and a data verification program are implanted. And obtaining an authorized access modification record according to the electric power big data classification and classification data set, and performing irregular security audit and real-time monitoring on the electric power big data classification and classification encryption data to perform encryption optimization on the electric power big data classification and classification encryption data. According to the method, the electric power big data can be classified and graded according to the source and the sensitivity of the electric power big data, data encryption is performed on the electric power big data, a protection program is implanted, and data protection measures are continuously optimized according to a risk invasion mode, so that the data security protection degree is improved.

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 invention belongs to the technical field of big data security, and provides a digital identity trusted management method for cross-domain data sharing. The method comprises the steps that a data owner creates a data box and performs data right confirmation; before using the data box, a data user firstly performs user identity authentication, that is, a user identity hash abstract is generated through a multi-hash algorithm, and dual comparison authentication is performed by using a data box environment and a trusted server environment; and after the authentication is passed, data consistency check needs to be carried out, and similar to user identity verification, consistency comparison is carried out by adopting a method for generating a data hash abstract by adopting a multi-hash algorithm. Besides, secondary verification is carried out based on the trusted server environment and the user identity, so that the security of the digital identity trusted management device is improved, and the data is ensured not to be illegally accessed or tampered.

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 utility model discloses a big data security integrated platform relates to big data technical field, including the first display screen, left and right sides of the first display screen close to upper and lower sides are fixedly connected with four rotatory plates that are distributed in the rectangle, the mutually close sides of the rotatory plates are rotatingly connected with two rotatory columns that are symmetrical left and right, and the rotatory columns are connected with the first display screen. Fixing mechanisms are fixedly connected to the inner sides of the upper walls and the lower walls of the two rotating columns, rotating mechanisms are fixedly connected to the inner sides of the two rotating columns in a sleeving mode, and second display screens are slidably connected to the rear walls of the two rotating columns. According to the big data security integrated platform, the second display screen rotates around the rotating plate by rotating the screw rod, and the second display screen cannot rotate reversely due to the self-locking property between the mechanism sleeve and the moving seat, so that the second display screen is fixed, and the security of the second display screen is improved. Therefore, the rotation operation of the second display screen can be completed only by rotating the screw rod, and the operation is simple, convenient and fast.

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 a metadata authority auditing system implementation method and a metadata authority auditing system, and belongs to the technical field of big data security, metadata authority auditing is realized based on a SpringBoot framework and an Elasticsearch distributed storage system, a user is enabled to realize management and maintenance of authority policy information of metadata through a universal REST API, and the user experience is improved. The access operation behavior of the permission policy is audited and analyzed; the user manages authority strategy information through the REST API; and the audit monitoring SDK monitors the access condition of the authority strategy information and analyzes the audit log in a fixed period. Uniformity and universality of permission policy management are supported, auditing monitoring is performed on data access and operation permissions, safety of data resources is guaranteed, and a safer and more convenient use environment is created for a user to use metadata.

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 provides a method for realizing secure transmission of massive big data. The method comprises the following steps: S1, acquiring a private picture; s2, obtaining the number of pixels and the total number of pixels of the private picture according to the private picture obtained in the step S1; s3, generating an encrypted first character string according to the private picture obtained in the step S1; s4, obtaining an encrypted second character string according to the total number of the pixel points obtained in the step S2 and the encrypted first character string generated in the step S3; and S5, carrying out operation on the private picture obtained in the step S1 and the encrypted second character string obtained in the step S4 to obtain a private encrypted picture. The security of the private picture can be guaranteed.

The invention provides a big data security risk assessment method and system, and relates to the technical field of risk assessment, and the method comprises the steps: formulating a test case according to a security rule, obtaining a test result, and collecting operation data if the test result accords with expectation; otherwise, an alarm is triggered. Then feature extraction and weighted average processing are conducted on the operation data to obtain first data, whether the data exceed a preset threshold value or not is judged, if yes, the first collection step is executed again, and if not, an alarm is triggered, and potential risks in big data can be reduced.

The invention discloses a CAN data storage and retrieval system of a commercial vehicle, and belongs to the technical field of commercial vehicle electronics. In order to solve the problems that a CAN data storage mode is large in storage space occupation, low in data security and incapable of timely analyzing a vehicle, the system comprises a data acquisition and preprocessing module for acquiring and preprocessing data on a CAN bus in real time; the data compression and encryption module is used for compressing the data file by adopting a DEFLATE algorithm and encrypting the data file by adopting a DES encryption algorithm; the data storage management module is used for storing according to a time sequence, and covering and replacing original data files in sequence according to the time sequence when the utilization rate of a disk exceeds 80%; the data retrieval and extraction module retrieves and extracts the data file; and the data decryption and decompression module is used for carrying out decryption and decompression processing on the data file, so that the file can be directly played back through a CAN OE or BUSMASTEER tool. The method is used for CAN data storage.

The invention discloses a big data security monitoring management system with an early warning processing function, and relates to the technical field of security monitoring. The method comprises the following steps: collecting picture display images corresponding to each camera device in real time, and integrating to generate a time picture display image library; setting a picture display image display condition to obtain a display condition group; screening the picture display images according to the display condition group and obtaining a screened picture display image library; periodically deleting the screened picture display image library to obtain a to-be-deleted picture display image, and preparing the to-be-deleted picture display image to obtain a prepared picture display image library; backing up the preliminary picture display image library to obtain an expired picture display image library; deleting the expired picture display image library according to the expired deletion period, and generating a deletion signal; and a deletion signal recognizer is arranged to judge whether the signal is a deletion signal, and if not, an early warning signal is generated for early warning.

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.