You are a Game Bot!: Uncovering Game Bots in MMORPGs via Self-similarity in the Wild

Game bots are a critical threat to Massively Multiplayer Online Role-Playing Games (MMORPGs) because they can seriously damage the reputation and in-game economy equilibrium of MMORPGs. Existing game bot detection techniques are not only generally sensitive to changes in game contents but also limited in detecting emerging bot patterns that were hitherto unknown. To overcome the limitation of learning bot patterns over time, we propose a framework that detects game bots through machine learning technique. The proposed framework utilizes self-similarity to effectively measure the frequency of repeated activities per player over time, which is an important clue to identifying bots. Consequently, we use realworld MMORPG ("Lineage", "Aion" and "Blade & Soul") datasets to evaluate the feasibility of the proposed framework. Our experimental results demonstrate that 1) self-similarity can be used as a general feature in various MMORPGs, 2) a detection model maintenance process with newly updated bot behaviors can be implemented, and 3) our bot detection framework is practicable. Permission to freely reproduce all or part of this paper for noncommercial purposes is granted provided that copies bear this notice and the full citation on the first page. Reproduction for commercial purposes is strictly prohibited without the prior written consent of the Internet Society, the first-named author (for reproduction of an entire paper only), and the author's employer if the paper was prepared within the scope of employment.