Behavior-based approaches for detecting cheating in online games

The online games industry has grown rapidly over the last decade. As a result of this rapid growth, many techniques have been created in response to the process of game development. One of the important aspects to consider is the prevention of cheating by the games players. Cheating in online games comes with many consequences for both players and companies. Therefore, cheating detection and prevention is an important part of developing a commercial online game. Over the years, there have been many anti‐cheating solutions that have been developed by gaming companies. However, many companies use cheating detection measures that may involve breaches to a user’s privacy. ❧ In this thesis, we provide a server‐side, anti‐cheating, system‐generic method that uses only game logs. The system consists of three main parts: cheat modeling, player modeling, and the decider. The cheat‐modeling segment focuses on defining a cheating behavior using classification techniques. This is achieved by building a model for each type of cheat. When considering the opposing side, the player modeling part focuses on defining a player’s behavior using anomaly detection techniques, which is done by building a model for each player. Each part will then give a probability of detection to the decider. The decider will give an overall probability based on different criteria discussed in the document. Many researchers have done work on the analysis of online game data, however, few of them focused on the problem of cheating detection.