Abnormal Game Play Detection Model Using Neural Networks
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Solution Overview
Problem
The increasing use of game hacking programs, known as 'game nuclei,' leads to unfair gameplay, reducing player interest and creating a challenge for game operators to prevent abnormal gameplay, as players who use these programs gain an unfair advantage, making it costly and time-consuming for others to compete fairly.
Innovation Solution
A method using an abnormal game play determination model with network functions to analyze game play scenes, identifying abnormal patterns through training data sets, and determining whether a player's gameplay is abnormal by comparing the scene to normal or abnormal patterns, allowing for the imposition of penalties on players who engage in unfair practices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If game operators manually monitor and detect abnormal gameplay, then detection accuracy can be maintained, but the time and cost required increases significantly
Solution Approach 1:
The patent replaces manual monitoring mechanisms with an automated deep learning-based detection system. The system uses neural networks to automatically analyze gameplay data, player behaviors, and transaction patterns to identify abnormal activities, eliminating the need for human operators to manually review each case while maintaining high detection accuracy through sophisticated pattern recognition algorithms.
Solution Approach 2:
The patent introduces an intermediate detection layer between gameplay activities and penalty enforcement. This intermediary system continuously collects gameplay data, processes it through multiple analysis modules (including deep learning models), and generates detection results that trigger automated responses, thereby reducing both the time and manual effort required for detection while preserving accuracy through systematic analysis.
2Productivity
If automated detection systems are implemented, then detection speed and efficiency improve, but detection accuracy may deteriorate due to false positives
Solution Approach 1:
The patent divides the detection system into multiple specialized modules, each responsible for analyzing specific aspects of gameplay (e.g., player behavior patterns, transaction anomalies, communication analysis). This segmentation allows each module to specialize in detecting particular types of abnormal activities, improving overall accuracy while maintaining high processing efficiency through parallel operation of multiple detection algorithms.
Solution Approach 2:
The patent implements feedback mechanisms where detection results are continuously evaluated and used to refine detection algorithms. The system learns from confirmed cases and adjusts its detection thresholds and parameters accordingly, reducing false positives over time while maintaining high detection efficiency through optimized algorithm performance based on accumulated experience.
Data Source
AI summary
Disclosed is a method for determining an abnormal game play. Operations stored in a computer program for processing the method include: computing a game play scene of a player using an abnormal game play determination model which includes one or more network functions; determining whether an abnormal pattern exists in the game play scene based on an output of the abnormal game play determination model; and determining whether a play of the player is abnormally performed based on whether an abnormal pattern exists in the game play scene.