Games Using Machine Learning Algorithms

Multiplayer Online Games (MMOGs) face numerous security challenges, including player cheating, due to their widespread popularity. Cheating not only disrupts the fair gaming experience for users but also damages the in-game economy and the credibility of gaming platforms. This paper explores methods for predicting and identifying cheating behaviors in players using machine learning algorithms. First, player behavior data is collected and pre-processed. Then, various machine learning algorithms such as Random Forest, Support Vector Machine (SVM), and Deep Neural Networks (DNNs) are evaluated for cheat detection. The results indicate that using these algorithms can achieve high accuracy in identifying cheaters and contribute to improving security and maintaining balance in games.

Games Using Machine Learning Algorithms Keywords:

Multiplayer Online Games , Cheating , Machine Learning , Random Forest , Support Vector Machine , Deep Neural Networks