Cheating in online games is a problem both on the esport stage and in the gaming community. When a player cheats, the competitors do not compete on the same terms anymore and this becomes a major problem when high price pools are involved in online games. In this master thesis, a machine learning approach will be developed and tested to try to identify cheaters in the first-person shooter game Counter-Strike : Global Offensive. The thesis will also go through how the game Counter-Strike : Global Offensive works, give examples of anti-cheat softwares that exists, analyse different cheats in the game, consider social aspects of cheating in online games, and give an introduction to machine learning. The machine learning approach was done by creating and evaluating a recurrent neural network with data from games played with the cheat aimbot and without the cheat aimbot. The recurrent neural network that was created in this master thesis should be considered as the first step towards creating a reliable anti-cheat machine learning algorithm. To possible increase the result of the recurrent neural network, more data and more data points from the game would be needed.