Analytical Approach for Bot Cheating Detection in a Massive Multiplayer Online Racing Game

The videogame industry is a growing business in the world, with an annual growth rate that exceeded 16.7% for the period 2005 through 2008. Moreover, revenues from online games will account for more than 38% of total video game software revenues by 2013. Due to this, online games are vulnerable to illicit player activity that results in cheating. Cheating in online games could damage the reputation of the game when honest players realize that their peers are cheating, resulting in the loss of trust from honest players, and ultimately reducing revenue for the game producers. Analysis of game data is fundamental for understanding player behaviors and combating cheating in online games. In this work, we propose a data analysis methodology to detect cheating in massively multiplayer online (MMO) racing games. More specifically, our work focuses on bot detection. A bot controls a player automatically and is characterized by repetitive behavior. Players in a MMO racing game can use bots to play during the races using artificial intelligence favoring their odds to win, and automate the process of starting a new race upon finishing the last one. This results in a high number of races played with race duration showing low mean and low standard deviation, and time in between races showing consistent low median value. A study case is built on upon data from a MMO racing game, and our results indicate that our methodology successfully characterize suspicious player behavior.