Cheat Detection on Online Chess Games using Convolutional and Dense Neural Network

Cheat Detection on Online Chess Games using Convolutional and Dense Neural Network

연구 분야: Artificial Intelligence

학회: 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

With the widespread use of chess engines cheating in chess has become easier than ever, especially in online chess. Cheating obviously brings a negative impact to the sport. However, research on the topic on cheat detection in chess is still scarcely found. Thus, this paper will discuss data and algorithms that can be used to develop cheat detection tools to analyze games. For data, there are analyzed data and unanalyzed data from online chess games whereas for the algorithm that will be explored there are convolutional neural network (CNN) and densely connected neural network. The results from the experiment using the CNN algorithm are better than the densely connected neural network for detecting if the player is cheating or not. Meanwhile for the data, using either unanalyzed and analyzed data doesn't change the best performing neural network, but it was found using the analyzed data still boosts the accuracy of both neural networks.