Yoav Eilat and Yahav Biran talked about the Multiplayer video games are among the most lucrative online services. The overall games industry worldwide generated an estimated $174B in 2019, according to IDC. With this popularity, cheating becomes a common trend. Cheating in multiplayer games negatively impacts the game experience for players who play by the rules, and it becomes a revenue issue for game developers and publishers. According to Irdeto, 60% of online games were negatively impacted by cheaters, and 77% of players said they would stop playing a multiplayer game if they think opponents are cheating. Current methods for detecting and addressing cheating become difficult and expensive to operate as cheaters respond to the evolution of anti-cheating techniques. This session will show an effective method for game developers to continuously and dynamically improve their cheat-detection mechanisms. It uses Amazon Aurora and Amazon SageMaker for cheating detection but can be adapted to other databases with similar capabilities. We’ll utilize the recently-launched Aurora machine learning functionality, which allows game developers to add ML-based predictions using the familiar SQL programming language without building custom integrations or learning separate tools. We’ll show which ML algorithms are useful for cheat detection and how an anti-cheat developer can write a single SQL query that handles the inputs and outputs for the algorithm.