Abstract
In order to maintain a fair competition environment and enjoyable experience for players, millions of dollars have been spent on against cheating in video games. There is limited research on more sophisticated forms of cheating like play-for-hire whereby players pay others to play for themselves. Our work develops a model to identify each player from player behavioural characteristics, which will contribute to solve the play-for-hire problem. Firstly, we recorded interactions between players and the game as multivariate time series. Next, we tried to use CNN and LSTM to classify data as corresponding players and we do some feature processing and parameter optimization to improve our result. We found that LSTM is acting better than CNN in higher dimensions, which achieved an accuracy of nearly 87%.
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