With the development of chess engines, cheating online has never been easier, resulting in a need for more robust and accurate detection systems. This paper presents a novel approach to chess cheater detection that combines conventional chess engines and neural networks to help identify which games are authentically played by humans and which show signs of extraneous intervention. By utilizing Stockfish to measure centipawn loss and its mathematical derivatives, we can measure deviations from typical computer-generated moves much like in conventional anti-cheat systems. Additionally, the neural network Maia, designed specifically to mimic human play, transmutes centipawn loss data to highlight deviations from human style. This dual-measurement system addresses the limitations of the given traditional anti-cheat systems, which face the issue of distinguishing between strong human players and those using engines. The collected metadata is analyzed using a sequential neural network, which identifies patterns of fair play violation. Our approach offers a robust solution for maintaining the integrity of online chess by accurately detecting and preventing cheating.