Identifying cheating behaviour with machine learning

Abstract

We have investigated machine learning based cheating behaviour detection in physical activity-based smart-phone games. Sensor data were acquired from the accelerometer/gyroscope of an iPhone 7 during activities such as jumping, squatting, stomping, and their cheating counterparts. Selected attributes providing the most information gain were used together with a sequential model yielding promising results in detecting fake activities. Even better results were achieved by employing a random forest classifier. The results suggest that machine learning is a strong candidate for detecting cheating behaviours in physical activity-based smartphone games.