Platform-Level Cheating Detection Using Machine Learning
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Solution Overview
Problem
Online gaming platforms face challenges in detecting cheating behaviors, especially when achievements from third-party games are managed at the platform level, as existing mechanisms only detect cheating within the game itself and may reward cheating by awarding items incorrectly.
Innovation Solution
Implementing a platform-level cheating detection system that uses machine learning techniques to classify player interactions as normal or outlying based on notifications, scores, and rankings, with classifying functions trained on labeled data to identify anomalous patterns indicative of cheating, and taking enforcement actions accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing game-level cheating detection mechanisms are used, then cheating within the game itself can be detected, but platform-level cheating in third-party games cannot be detected and may be rewarded incorrectly
Solution Approach 1:
The patent implements a universal platform-level detection system that monitors user progress across multiple third-party games through a common API interface. The system classifies user notifications using machine learning models trained on platform-wide data, enabling consistent cheating detection across diverse game environments while maintaining game-specific detection capabilities.
2Measurement precision
If manual review of player data is performed, then cheating patterns can be identified, but the large quantity of player data cannot be screened efficiently
Solution Approach 1:
The patent replaces manual review processes with automated machine learning classification functions. These functions analyze user notifications, game progress data, and achievement patterns to automatically identify cheating behaviors, enabling efficient processing of large volumes of player data while maintaining high detection accuracy through trained classification models.
3Reliability
If platform-level monitoring of all user progress is implemented, then cheating can be detected across all games, but system complexity and computational resources increase
Solution Approach 1:
The patent segments the platform-level detection system into modular components: notification reception modules, machine learning classification functions, and enforcement action modules. Each component handles specific aspects of cheating detection independently, reducing overall system complexity while maintaining comprehensive platform-wide monitoring capabilities through coordinated operation of these segmented modules.
Data Source
AI summary
Examples are disclosed that relate to detecting cheating at a game platform level using machine learning techniques. One example provides a computing system comprising a logic subsystem and a data-holding subsystem. The data-holding subsystem comprises instructions executable by the logic subsystem to receive notifications related to user progress in a game provided by the game to the online game platform, apply a classifying function to classify the user progress in the game as normal or outlying based upon the notifications, if the progress is classified as outlying then taking an action in response to the outlying classification, and if the progress is not classified as outlying then not taking the action.