Neural Networks Applied to Speed Cheating Detection in Online Computer Games

This work presents a new approach to deal with speed cheating in online computer games. With the great growth of the online computer games, some efforts have been made to avoid cheaters in this scenario, but the models to avoid cheaters are localized into the protocol level. Examining the state-of-art, it was observed that research exploring the Artificial Intelligence application to this goal becomes relevant. This work shows the usage of Artificial Neural Networks (ANN) applied in a Massive Multiplayer Online Games (MMOG) called Hoverkill to avoid this kind of cheat. Through the results's comparison from two different architectures approaches, the multi layer Perceptron network (MLP) and the focused time lagged network (FTLFN), it was possible to conclude that their utilization avoiding speed cheating in MMOG is possible, once good results were found in this work.