An Approach to Detect Macros via Self-similarity of Mobile Input

Joonun Jang, Huy Kang Kim

http://doi.org/10.5626/JOK.2019.46.9.951

Macros that repeats specified in-game actions without the need for human interaction are a major cause of unfairness in computer gaming. For the success of a game service, the organizational use of macros which destroys the game’s economy and can deteriorate a user’s game motivation should be prohibited. It is particularly easy for macros to be generated and used in mobile games, because a mobile game’s design and playing sequence are likely to be relatively simple compared to those of PC games because of the limited hardware resources and, inefficient input methods of mobile devices compared to PCs. At the same time, the current macro detection methods used in mobile games can consume substantial amounts of resources. Thus, macro detection is still a challenge in mobile game services. In this paper, we propose a method to detect macros via self-similarity based on the mobile input. Our proposed method sets the unit for effectively obtaining self-similarity with fewer resources. We applied the proposed method to two mobile games and showed that macro and human activities can be distinguished with high accuracy.