Target Account Detection Using Active Behavior Timing Features
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Solution Overview
Problem
Current methods for identifying abnormal game accounts, such as those used in gaming services, suffer from low detection coverage due to limited feature dimensions and the ability of abnormal accounts to mimic normal behavior, leading to ineffective identification and resource misallocation.
Innovation Solution
The introduction of an active behavior timing feature, combined with account features, to predict the probability of a target account being of a specific type, enhancing detection accuracy and coverage by analyzing time-related behaviors and account activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional account feature analysis is used for target account detection, then the detection process is simple, but the identification coverage is low and false positives occur due to limited feature dimensions
Solution Approach 1:
The patent transforms the detection approach by introducing temporal dimension through active behavior timing features. Instead of only analyzing account features at a single point in time, the system incorporates time-series behavior data, converting a static detection problem into a dynamic one that captures behavioral patterns over time, thereby improving identification coverage without excessive complexity increase
Solution Approach 2:
The detection model is segmented into multiple independent feature extraction components: account feature extraction, active behavior timing feature extraction, and probability prediction. This modular segmentation allows each component to be optimized independently and improves overall detection coverage while managing complexity through structured organization
2Measurement precision
If more feature dimensions are added to improve detection coverage, then identification accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary extraction of active behavior timing features from raw account data before the main detection process. By pre-processing and structuring temporal behavior data into standardized timing features, the system reduces the computational burden during actual detection, maintaining high accuracy while minimizing processing time
Solution Approach 2:
The patent extracts only the most critical temporal features (active behavior timing features) from the vast amount of account data, rather than processing all available data. This selective extraction of essential temporal patterns maintains detection accuracy while significantly reducing computational complexity and processing time
Data Source
AI summary
A target account detection method and apparatus, an electronic device, and a storage medium. The method includes: determining an active behavior timing feature of a target account to be detected according to active behavior data of the target account; determining an account feature of the target account according to account data of the target account; predicting a first probability that the target account is of a target type; and determining, in response to the first probability being greater than a target probability threshold, that the target account is of the target type. Detection is performed in the dimension of timing, so that the impact of an account of a target type pretending to be a normal account on detection can be reduced, and more accounts of the target type can be detected, thereby enlarging the identification coverage.