Data Evolution Path Analysis for Abnormal Behavior Detection
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Solution Overview
Problem
Existing data segmentation and analysis methods fail to effectively track and respond to evolving customer behavior patterns and trends over time, particularly in detecting abnormal activities like unauthorized access, lacking a comprehensive approach to generate and analyze customer data evolution paths.
Innovation Solution
A computer-implemented method generates customer data evolution paths using clustering algorithms to segment data over defined time periods, analyzes these paths to identify behavior patterns and trends, and automatically performs appropriate actions when abnormal patterns are detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional data segmentation and analysis methods are used, then data can be divided into groups based on characteristics, but the system cannot effectively track and respond to evolving customer behavior patterns over time
Solution Approach 1:
The system performs preliminary actions by generating customer data evolution paths and establishing baseline behavior patterns before abnormal activities occur. This allows the system to have pre-computed evolution paths and behavior models ready for comparison when monitoring customer data, enabling faster and more reliable detection of deviations from normal behavior patterns.
Solution Approach 2:
The system applies dynamics by continuously updating and evolving customer behavior patterns over time periods. Instead of static segmentation, the system dynamically tracks how customer behavior evolves across multiple time periods, allowing the segmentation and analysis adapt to changing behavior patterns while maintaining reliable abnormal activity detection.
2Reliability
If comprehensive data analysis is performed to detect abnormal patterns, then data security is improved, but the complexity of the system increases
Solution Approach 1:
The system applies segmentation by dividing the comprehensive data analysis into manageable time periods and customer segments. Instead of analyzing all data at once, the system segments data into discrete time periods, generates evolution paths for each segment, and analyzes them systematically. This reduces system complexity while maintaining comprehensive security monitoring through structured, phased analysis.
3Reliability
If real-time monitoring of customer behavior is implemented, then abnormal activities can be detected promptly, but the loss of time for data processing increases
Solution Approach 1:
The system implements periodic action by monitoring customer behavior data in discrete time periods rather than continuous real-time processing. Data is collected, analyzed, and processed at defined interval periods, which reduces overall processing time while still providing timely detection of abnormal activities. The periodic generation of evolution paths and behavior models enables efficient batch processing that maintains detection responsiveness.
Data Source
AI summary
Data segmentation, analysis, and security is provided. An analysis of a set of generated data evolution paths corresponding to a set of data collected over a defined span of time is performed. A behavior trend is determined based on analysis of the set of generated data evolution paths. A set of action steps is performed automatically based on the determined behavior trend.