Bot Detection via Sensor and Interaction Data Analysis
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Solution Overview
Problem
Existing security measures are inefficient in distinguishing human players from bots in online games and other software applications, leading to fraudulent activities that discourage honest players and disrupt fair gameplay.
Innovation Solution
A system and method that utilizes a server arrangement communicably coupled with user devices to receive and analyze user-interaction data and sensor data to detect whether a user is a bot, performing actions to prevent fraudulent activities by determining the reliability factor based on interaction patterns and sensor data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing security measures (CAPTCHA) are used to distinguish human players from bots, then some level of fraud prevention is achieved, but the measures are inefficient and can be circumvented
Solution Approach 1:
The patent combines multiple data sources (sensor data from device sensors, interaction data from application usage, and device information) into a unified analysis system. This merging of previously separate security checks creates a more reliable fraud detection mechanism that is harder to circumvent while maintaining manageable system complexity through integrated processing.
Solution Approach 2:
The server arrangement performs multiple functions: collecting sensor data, collecting interaction data, collecting device information, analyzing fraud risk, and determining reliability factors. This multi-functional approach consolidates what would otherwise require separate security systems into a single universal platform, improving reliability without proportionally increasing complexity.
2Productivity
If autonomous programs (bots) are used to automate gameplay, then players can achieve recognition and monetary gain, but honest players are discouraged from playing and making transactions
Solution Approach 1:
The system implements continuous feedback by monitoring sensor data, interaction patterns, and device information in real-time. The server analyzes this data to determine fraud risk and reliability factors, providing ongoing feedback that identifies bot behavior. This allows the system to maintain fair gameplay by detecting and preventing bot advantages while preserving honest players' experience.
3Measurement precision
If comprehensive sensor data and interaction data collection is implemented, then fraud detection accuracy is improved, but data processing requirements and system resource usage increase
Solution Approach 1:
The system performs preliminary data collection and processing by gathering sensor data, interaction data, and device information before fraud analysis is needed. The server prepares and stores this data in advance, allowing for efficient real-time fraud detection without excessive energy consumption during critical analysis moments. This preliminary action reduces the computational burden during actual fraud detection operations.
Data Source
AI summary
A system for detecting and preventing fraudulent activities on a software application. The system includes a server arrangement that is communicably coupled with a user device. The server arrangement is configured to receive user-interaction data and sensor data from the user device on which the software application is being executed. The user-interaction data is indicative of an interaction of a user with the software application. The sensor data is collected by at least one sensor of the user device during execution of the software application. The server arrangement is further configured to analyse the user-interaction data and the sensor data to detect whether or not the user is a bot, and perform at least one action when the user is a bot.