Data behavior prevention and control and anomaly detection system based on big data

By constructing a comprehensive score and threshold judgment based on emotional features, spatiotemporal trajectory features, and abnormal data features, and combining it with the decision tree algorithm, multi-dimensional anomaly detection of user behavior is achieved. This solves the difficulty of identification under complex data by traditional methods and improves the intelligence and accuracy of the system.

CN121997188AInactive Publication Date: 2026-05-08SHANDONG XIEHE UNIV +1
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Patent Information

Application Number
CN202511964181.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-24
Publication Date
2026-05-08
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Traditional anomaly detection methods struggle to accurately identify abnormal patterns in complex, multi-dimensional, and large-scale data, such as user emotions, behavioral trajectories, and automated behaviors, leading to a decline in system security and user experience.

Method used

The big data-based data behavior prevention and anomaly detection system constructs emotional features, spatiotemporal trajectory features, and abnormal data features, calculates a comprehensive score, sets a threshold for judgment, and combines a decision tree algorithm to build an anomaly detection model, generating various abnormal signals for real-time monitoring and prevention.

Benefits of technology

It improves the accuracy and response speed of anomaly detection, enabling timely identification of multi-dimensional abnormal behaviors, reducing manual intervention, and enhancing the system's adaptability and intelligence level.

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Abstract

The invention discloses a data behavior prevention and control and anomaly detection system based on big data, and relates to the technical field of data security. Comprising a data acquisition module used for acquiring user behavior data; the feature construction module is used for constructing emotion features, space-time trajectory features and abnormal data features according to the preprocessed user behavior data; the calculation module is used for calculating to obtain a comprehensive score according to the emotion features, the spatial-temporal trajectory features and the abnormal data features; the threshold judgment module is used for respectively carrying out threshold judgment on the comprehensive emotion score, the comprehensive space-time score and the comprehensive generated data score, and generating a first abnormal signal, a second abnormal signal and a third abnormal signal when a threshold condition is exceeded; and the model construction module is used for constructing an anomaly detection model and generating a fourth anomaly signal when an anomaly is detected. Through multi-dimensional feature fusion, real-time anomaly detection and a deep learning technology based on big data, the accuracy of abnormal behavior recognition is improved, and the security and reliability of the system are enhanced.
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