8 results about "Big data security" patented technology

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.

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 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 embodiment of the application discloses a kind of distributed sensitive data two-dimension exploration method, equipment and medium, belong to big data security technical field, solve the problem that existing distributed data exploration method is when facing mass data, scanning is extremely long in time, it is difficult to meet the timeliness of business. Including, receiving the exploration task configured by user;Analysis of the partition information of exploration target data set in distributed storage architecture, and based on partition information, scan task is distributed to the multiple computing nodes of distributed query processing system;Content scanning strategy is converted into query predicate, and is pushed to the storage layer of distributed storage architecture to execute data filtering;Parallelly execute data content matching based on content scanning strategy and field name matching based on field name scanning strategy;The matching results of multiple computing nodes are aggregated, to generate exploration result, match it with preset multistage security classification rule library, determine the security classification path corresponding to each exploration result, generate exploration report.

This invention discloses an artificial intelligence-based method for big data security processing in the field of network security technology. The method collects encrypted traffic and extracts its transport layer metadata without decrypting the network traffic; preprocesses the metadata and constructs a temporal feature sequence; analyzes the feature sequence using a hybrid deep learning model composed of a one-dimensional convolutional neural network and a long short-term memory network to identify malicious encrypted traffic; and executes a real-time security response based on the identification results. This invention achieves high-precision detection of encryption-masked attacks by deeply mining the behavioral features in the encrypted traffic metadata, while strictly protecting user communication privacy.

This application discloses a method, device, and medium for compressing and encrypting financial transaction logs, relating to the field of financial big data security processing technology. The method includes: acquiring original financial transaction logs and performing field-level sensitivity analysis on the original financial transaction logs to identify highly sensitive and low-sensitive fields; calculating the field entropy values ​​of the highly sensitive and low-sensitive fields respectively, and obtaining risk factors associated with the original financial transaction logs; generating a comprehensive security level score through weighted calculation based on the field entropy values ​​and risk factors; dynamically matching and invoking a pre-configured combination strategy of encryption and compression algorithms based on the comprehensive security level score to generate algorithm execution instructions; executing the encryption and compression fusion operation for highly sensitive fields and the compression operation for low-sensitive fields in parallel within the same memory address space according to the algorithm execution instructions to generate a fused ciphertext log; and outputting the ciphertext log to a downstream storage system.

The application discloses a data security authentication sharing system based on supply chain big data, and belongs to the technical field of supply chain financial big data security. The financial data incremental differential weighted authentication module is equipped with a self-developed incremental differential weighted hash authentication algorithm, and through field partition differential verification and incremental local calculation, the financial data small-scale implicit tampering can be accurately identified, the high-frequency iteration scene algorithm redundancy can be reduced, and the quantized output data can be trusted risk level. The risk linkage multi-level permission fuse control module dynamically calculates the multi-level node permission coefficient through the risk penetration authorization fuse algorithm based on the real-time risk level, realizes the hierarchical authorization and abnormal data global fuse, and solves the specific security pain points in the supply chain financial big data subdivision field, greatly improves the financial data authentication accuracy and multi-level sharing security, effectively avoids false financing, data leakage and credit risk, and adapts to the core business scenarios of supply chain financial incremental iteration and multi-level chain flow.