Online Service Abuse Detection via Behavioral Image Analysis
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Solution Overview
Problem
Current methods for detecting abusive acts in online services are inaccurate and easily bypassed, as they rely on fragmental information analysis, making it difficult to distinguish between normal and abusive user behavior, especially when dealing with large user populations.
Innovation Solution
A method that collects and analyzes user activity information to form patterns in images, using axes to represent the types and order of user actions, and employs neural networks to determine abnormal behavior, incorporating user-specific information to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fragmental information or specific acts are analyzed to detect abusing acts, then the detection process is simple, but the accuracy in detecting abusing acts is low
Solution Approach 1:
The patent segments user behavior detection into multiple dimensions: login information, IP address, text content, images, URLs, and sequential act patterns. Each dimension is analyzed separately and then integrated to improve overall detection accuracy while maintaining manageable complexity in each segment.
Solution Approach 2:
The patent transitions from analyzing single isolated acts to analyzing sequential patterns of multiple acts in temporal order. By adding the dimension of temporal sequencing and pattern recognition, the system achieves higher accuracy in distinguishing abusing acts from normal uses without proportionally increasing system complexity.
2Ease of manufacture
If traditional methods are used to detect abusing acts, then the implementation is easy, but users can relatively easily avoid the detection
Solution Approach 1:
The patent performs preliminary analysis of user behavior patterns by collecting and storing sequential act information before actual abusing acts occur. This preliminary action builds a baseline of normal user behavior, enabling the system to reliably detect deviations without requiring complex real-time analysis during the abusing act itself.
Solution Approach 2:
The system implements feedback mechanisms where detection results are continuously refined based on accumulated user behavior data. The detection model learns from past cases and adjusts its parameters, making it progressively more reliable at identifying abusing acts while maintaining ease of implementation through automated learning rather than manual rule updates.
3Device complexity
If only specific user acts are analyzed, then the analysis is straightforward, but it is not easy to distinguish abusing acts from normal use acts
Solution Approach 1:
The patent merges multiple types of user information including login data, IP addresses, text content, images, URLs, and sequential act patterns into a comprehensive analysis framework. By combining these diverse data sources, the system achieves high accuracy in distinguishing abusing acts from normal uses while managing complexity through integrated processing rather than separate analyses.
Solution Approach 2:
The patent adds the dimension of temporal sequencing by analyzing the order and timing of user acts. This transformation from static single-act analysis to dynamic sequential pattern analysis enables the system to distinguish abusing acts with higher precision without proportionally increasing analysis complexity, as the sequential patterns provide additional discriminatory information.
Data Source
AI summary
The present invention discloses a method of detecting an abusing act in an online service, the method including: a use act information collecting operation in which an apparatus for detecting an abusing act collects use act information including the kinds and an order of one or more use acts performed by a user; an image configuring operation in which the kinds of the one or more use acts performed by the user are arranged in a time order and an image is configured by setting the kinds of predetermined use acts to a first axis and setting the order of the use acts to a second axis; and an abusing act detecting operation in which whether the use act of the user is an abusing act is detected by using the image.