Gaming Fraud Detection via Input Threshold Validation
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Solution Overview
Problem
Current technologies fail to effectively detect and counteract player fraud and collusion in gaming environments, particularly in large venue scenarios and remote player setups, where digital inputs and unauthorized activities can go undetected.
Innovation Solution
A system that monitors user inputs during application execution, establishes behavioral models through simulation, and uses validation and counter-measures modules to identify and respond to suspicious activities, including cheating, collusion, and unauthorized assistance, by comparing user inputs against thresholds and employing machine learning for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional proctoring methods are used in large venue scenarios, then the system is simple to operate, but it fails to detect digital inputs and cheating activities
Solution Approach 1:
The system segments the detection process into multiple independent modules: input monitoring module, behavioral analysis module, validation module, and counter-measures module. Each module handles specific aspects of fraud detection, allowing complex detection capabilities to be built from simpler, manageable components that can operate in parallel.
Solution Approach 2:
The system introduces intermediary components such as the validation module that acts as a mediator between user inputs and the game execution. This validation layer intercepts and analyzes inputs before they affect game state, enabling detection without requiring complete system redesign.
2Reliability
If remote player scenarios are monitored with basic methods, then the implementation is simple, but unauthorized activities and impersonation go undetected
Solution Approach 1:
The system implements continuous feedback loops where user behaviors are monitored, analyzed against established models, and used to dynamically adjust detection thresholds. The validation module receives feedback from behavioral analysis and automatically responds by validating or rejecting inputs, creating an automated reliability enhancement system.
Solution Approach 2:
The system changes detection parameters dynamically based on learned behavioral patterns. Thresholds for suspicious activity detection are adjusted based on machine learning models that adapt to normal player behaviors, allowing the system to maintain high reliability while automating the detection process.
3Measurement precision
If machine learning models are trained extensively to detect all fraud types, then detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models offline to establish behavioral baselines and detection thresholds before actual game execution. During live gameplay, the pre-trained models quickly evaluate inputs against established patterns, significantly reducing real-time processing requirements while maintaining high detection accuracy.
Solution Approach 2:
The system applies partial validation by focusing computational resources on detecting specific high-risk fraud types rather than analyzing every possible input in equal detail. The validation module selectively applies different levels of scrutiny based on the nature of the input and detected risk patterns, optimizing the balance between accuracy and processing speed.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, comparing an input received from a peripheral device associated with an execution of a gaming application with a threshold value, wherein the threshold value is based on a first identification of a first user, a second identification of the peripheral device, and a third identification of stimuli presented as part of the execution of the gaming application. Responsive to the comparing, a determination may be made that the input exceeds the threshold value. Responsive to the determination, a validation request may be transmitted to a user device of the first user. Other embodiments are disclosed.