IP Address Credit Threshold Training for Abnormal Traffic Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for identifying abnormal IP addresses based on frequency or verification rate thresholds are inefficient and can be easily bypassed by malicious users, leading to increased costs and security vulnerabilities.
Innovation Solution
A method for training credit thresholds using machine learning to analyze service features from history data, calculating correlation coefficients, and generating credit values to determine IP address validity, and a method for detecting IP addresses by comparing real-time data with trained correlation coefficients and preset thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple verification code methods are used, then user convenience is improved, but security against message bombardment deteriorates
Solution Approach 1:
The system performs preliminary actions by calculating correlation coefficients between service features and IP validity from historical data before actual detection occurs. Credit thresholds are pre-trained using machine learning models, enabling the system to proactively identify malicious patterns before they cause harm, thus maintaining security without affecting user convenience
Solution Approach 2:
The patent introduces an intermediary mechanism - the credit threshold evaluation system - that mediates between simple verification codes and security requirements. By evaluating multiple service features through pre-trained correlation coefficients, the system acts as a intermediary layer that maintains user convenience while blocking malicious message bombardment
2Device complexity
If frequency-based threshold methods are used, then detection simplicity is improved, but detection accuracy deteriorates
Solution Approach 1:
The patent transitions from single-dimension frequency-based detection to multi-dimensional detection by incorporating multiple service features (verification rate, service type, time distribution, etc.). The machine learning model evaluates correlations across these different dimensions, significantly improving detection accuracy while maintaining implementation simplicity through automated threshold training
3Measurement precision
If multi-dimensional evaluation is implemented, then detection accuracy is improved, but system complexity deteriorates
Solution Approach 1:
The system implements self-service through automated machine learning training. The credit thresholds are automatically trained using historical data and pre-calculated correlation coefficients, eliminating the need for manual configuration of multiple detection parameters. The system serves itself by automatically optimizing detection accuracy without increasing operational complexity
Solution Approach 2:
Correlation coefficients between service features and IP validity are pre-calculated from historical data before actual detection operations. This preliminary action stores complex relationship patterns in advance, allowing the system to achieve high detection accuracy through simple threshold comparisons during runtime without real-time computational complexity
Data Source
AI summary
Provided is a method for training credit thresholds, including: acquiring a plurality of service features from history data of a service operation triggered based on a plurality of IP addresses; hierarchically calculating at least two correlation coefficients for each of the service features; generating, for each of the IP addresses based on the correlation coefficients corresponding to the service features, a credit value indicating the validity of each of the IP addresses; generating an evaluation indicator for the plurality of IP addresses; and determining a credit value corresponding to the evaluation indicator as a credit threshold in response to the evaluation indicator meeting a target condition.